Download Understanding the implications of Climate Change for woodland

Understanding the implications of Climate Change for woodland biodiversity and community functioning Pam Berry, Environmental Change Institute, University of Oxford Yuko Onishi, Environmental Change Institute, University of Oxford James Paterson, Centre for Environmental Management, University of Nottingham Report commissioned by the Forestry Commission (UK) 1
Contents 1. Introduction ..........................................................................................................................3 2. Climate change ......................................................................................................................4 2.1. Introduction......................................................................................................................... 4 2.2. Recent changes in climate ................................................................................................... 4 2.3. Modelling future climate change ........................................................................................ 4 2.4. Observed impacts of climate change on biodiversity.......................................................... 6 2.5. Predicting climate change effects on biodiversity............................................................... 6 3. Methodology.........................................................................................................................8 3.1. Literature review ................................................................................................................. 8 3.2. Bioclimate envelope modelling ........................................................................................... 9 4. Results................................................................................................................................. 13 4.1. Reviewing the effects of climate change on biodiversity and community function .........13 4.2. Impacts of climate change on woodland Priority Habitat species ‐ results from the bioclimate envelope modelling .........................................................................................16 4.3. Analysis of results in relation to the National Vegetation Classification (NVC).................31 4.4. Impacts of climate change on rare and charismatic species .............................................33 4.5. Potential new species ........................................................................................................41 5. Discussion............................................................................................................................ 42 5.1. Application of bioclimate models......................................................................................42 5.2. Comparison with Ecological Site Classification results ......................................................43 5.3. Potential impacts on woodland structure and function ................................................... 51 5.4. Biological adaptation to climate change ...........................................................................54 5.5. Adaptation strategies for biodiversity conservation .........................................................54 5.6. Non‐intervention ...............................................................................................................61 5.7. Data gaps ........................................................................................................................... 65 5.8. Further Study.....................................................................................................................65 6. Conclusions ......................................................................................................................... 73 7. References........................................................................................................................... 77 8. Appendices........................................................................................................................ 102 8.1. Sources of European Species distribution data ............................................................... 102 8.2. Modelling results for 8 climate change scenarios ...........................................................104 2
1. Introduction Forests and woodlands are important parts of the landscape of the UK covering over two and a half million hectares (an area slightly larger than Wales), which equates to 12% of our total land area. The tree species native to the UK have gradually spread in response to the local climate, atmosphere and soils since the last glaciers retreated over 10,000 years ago. Other woodland community assemblages have similarly developed in relation both directly to the climate and indirectly through their link to the tree species. However, future climate changes are predicted to have a range of effects on our native species which may have dramatic consequences for woodland community structure and function. There is much evidence of recent climate change impacting upon species in Europe and the UK (Broadmeadow et al., 2002; Sparks & Menzel, 2002; Boisvenue & Running, 2006; Broadmeadow et al., 2009a; Sier & Scott, 2009). There is, however, much less evidence for habitats (but see Sanz‐
Elorza et al., 2003; Peñuelas et al., 2007). The impacts on species may be of interest if the species is of key importance to the habitat's composition ‐ for example tree species for woodlands ‐ and may help in understanding the implications of climate change for woodland biodiversity and community functioning. There are many approaches to identifying and projecting the impacts of environmental pressures, including climate change, on biodiversity. The sensitivity of habitats to future climate change can be established by a number of approaches. These include extrapolation of observed and experimental data (Sparks, 2007) and niche and dynamic vegetation modelling (e.g., Woodward & Lomas, 2004). In this report, the impacts are addressed through a review of the existing evidence and literature, as well as further detailed analysis of the outputs of a bioclimate envelope model, SPECIES. This report sets out to examine the known effects of climate change on woodland biodiversity and functioning, focusing on woodland Priority Habitats, as well as rare and charismatic species. The effects of the arrival and increase of new species in the UK are also examined. Despite an increasing body of work examining these issues there are still major knowledge gaps in this field; here we make suggestions for further research, including methods for filling these gaps. 3
2. Climate change 2.1. Introduction During the 20th century and especially during the later part, observed changes in climate were increasingly seen as being due to human emissions and land cover change altering the overwhelming scientific consensus is that man‐made causes have resulted in significant changes to our climate since the industrial revolution (Hegerl et al., 2007; Le Treut et al., 2007). 2.2. Recent changes in climate There are numerous methods for understanding changes in climate over various time‐scales and all rely on observational records or measures of either direct climatic changes (e.g., temperature, snowfall) or indirect changes (e.g., glacier retreat, sea‐level rise) (Trenberth et al., 2007). For example, observational records (in some instances that go back many centuries) show global average air and ocean temperatures have increased, snow cover has reduced and average sea levels have risen over the past century (Lemke et al., 2007). Global average surface temperature is perhaps the climatic variable that has changed most though: from 1906 to 2005 it rose by 0.74°C ± 0.18°C; between 1956 and 2005 it has risen by 0.13°C per decade (Trenberth et al., 2007). In the UK, mean annual temperature (derived from the Central England Temperature monthly series which is based on a number of weather stations around England, but is highly correlated with temperature data from other British stations) has risen by approximately 1°C since 1980 and the increase is actually is more pronounced than the global average over the same period (Jenkins et al., 2009). In Scotland and Northern Ireland this rise has been 0.8°C. Annual mean precipitation in England and Wales has remained relatively unchanged over the last few centuries, although the summer rainfall has slightly decreased and winter rainfall has slightly increased. 2.3. Modelling future climate change The science of global climate prediction is an ongoing undertaking of improvement and understandably involves a complex and detailed process. Here, we outline the main aspects. 2.3.1. Emissions scenarios The modelling used in this report relied upon a series of four emissions scenarios developed by the IPCC (Nakićenović et al., 2009). These emissions scenarios (SRES ‐ Special Report on Emissions Scenarios) were created to cover a range of different possible patterns of GHG emissions; they are based on likely further economic development and encompass worst‐case scenarios, as well as sustainable development forecasts. This report utilises two contrasting scenarios: • A2 scenarios represent a less cohesive world in which many nations are self‐reliant and independent, the population increases unabated, there is slower uptake of technologies and economic development is regional rather than global. 4
• The B1 scenarios are altogether more sustainable and integrated. Rapid economic growth occurs but it is more oriented towards information and services, rather than primary industry. The global population reaches a maximum of 9 billion in 2050; clean and resource efficient technologies gain in popularity, as does an emphasis on economic, social and environmental stability (Nakićenović et al., 2009). 2.3.2. General Circulation Models In order to predict future climate change modellers have developed General Circulation Models (GCMs) which are mathematical representations of atmospheric, ocean, cryosphere and land parameters (Randall et al., 2007). They produce climate scenarios on a three‐dimensional grid over the earth, albeit at a relatively coarse resolution, which produces averaged properties over the large scales involved (and hence provides one source of uncertainty). Current models utilise ocean and atmospheric (AOGCMs), as well as sea ice levels or evapotranspiration to produce better quality outputs and are increasingly capable of incorporating feedback mechanisms, like water vapour and warming, ice and snow albedo. The models use climate simulations (usually over a defined time period) to project temperature changes under future climate scenarios (using the SRES scenarios above). 2.3.3. European and UK projections of future climate change The results of different model simulations have provided the IPCC and others with a high degree of confidence regarding the future climate change up to 2030. In Europe, these projections have been outlined by the IPCC in various reports (Alcamo et al., 2007; Christensen et al., 2007) and for the UK (Hulme et al., 2002; Murphy et al., 2009). Here we summarise the main predictions: 1. The increase in annual mean temperatures in Europe is likely to be higher than the global mean figure. Warming will vary across the region; in northern Europe, winters are likely to be more affected, in southern Europe, summers will see the greatest increases in temperature. In the UK, annual temperatures may rise by between 2ºC and 3.5ºC by the 2080s, depending on the SRES scenario; the south‐east will receive more warming than the north‐west of the UK, with warming in summer and autumn more than in winter and spring. Normal spring temperatures will start between one and three weeks earlier by 2050 and conversely winter temperatures may start later by one to three weeks. 2. Annual precipitation will increase in northern Europe and decrease in southern Europe; in northern Europe there will be greater numbers of extreme precipitation events in the winter, conversely, in southern and central Europe, there are likely to be more drought events in summer. In the UK, annual average precipitation will possibly decrease by 2080 although there will be regional differences; as in the rest of Europe, winters will be wetter and summers will be drier. 3. Snowfall in winter across Europe will decrease and the duration of snow cover will decrease also; this pattern will be the same for the UK, but by 2080 snowfall could decrease by as much as between 30 and 90%. 5
2.4. Observed impacts of climate change on biodiversity The evidence of climate change impacts on species and habitats so far recorded is quite extensive. Changes in climate can affect a number of aspects of a species' autoecology including phenology (timing events like bud burst, senescence, etc.), growth, reproduction, germination, establishment, competition and response to herbivory (Fitter & Fitter, 2002; Sparks et al., 2002). These impacts not only affect species and their populations, but can have serious implications for community structures and ultimately even ecosystem function (McCarty, 2001). The evidence for impacts of climate change on species is now very conclusive (for issues of attribution see: Shoo et al., 2006) and in recent years a number of comprehensive reviews of the impacts of climate change on biodiversity have been produced summarising effects across different taxonomic groups, biological systems, regions and type of response (Parmesan & Yohe, 2003; Root et al., 2003; Hickling et al., 2006; Thomas et al., 2006). In Section 4.1 we review the literature on climate change impacts on five main aspects of change (physiological changes, distribution shifts, phenology, evolutionary change and community responses) pertinent to species found in woodland habitats and conclude with a discussion of the likely consequences for woodland ecosystem functioning. 2.5. Predicting climate change effects on biodiversity Ecologists have a range of tools to utilise for prediction although they all have strengths and weaknesses. Currently, the five main methods of studying the effects of climate change on plant and animal communities involve some form of extrapolation or inference (Morecroft & Paterson, 2006); these are: • Direct long‐term monitoring of species or habitats; • Experimental control of climatic factors on species or habitats; • Inference from spatial patterns in plant or animal communities; • Inference from palaeoecological studies of previous habitats; and, • Modelling climate change scenarios to determine species potential future climate space. The technique used in this study is the latter (although the others are covered in the literature review). 2.5.1. Bioclimate envelope models (BEM) Bioclimatic envelope models have been used in ecology for a number of years now (Huntley et al., 1995; Sykes et al., 1996; Guisan et al., 1999) and have enjoyed a steady development since then (Thuiller et al., 2008; Elith & Leathwick, 2009; Rubidge & Monahan, 2011). BEM (or niche models) are essentially correlative models that use two forms of data: presence and/or absence data for species' distributions and corresponding environmental variables for each presence and/or 6
absence location. The basic assumption is that climatic factors are the main agents determining species' distributions at continental and national scales, a relationship that has long been accepted in ecology (Holdridge, 1947; Box, 1981). Therefore, by correlating current climatic factors with current species' distribution, it is possible, by applying future climate change scenarios, to create new potential distributions. This approach has been applied to various taxa including amphibians and reptiles (Araújo et al., 2006), plants (Bakkenes et al., 2002; Berry et al., 2003; Iverson et al., 2008) and birds (Peterson, 2003; Huntley et al., 2008). It is important to note that the outputs produce potential suitable climate space for species not actual distributions. The design of BEM varies considerably with a number of different statistical approaches in current use including General Linear Models, General Additive Models, Genetic Algorithm for Rule‐set Prediction and Classification Tree Analysis (Araújo et al., 2005; Elith et al., 2006; Heikkinen et al., 2006). In this report, we used two approaches: an Artificial Neural Network (ANN) and Ecological Niche Factorization Analysis (ENFA). 7
3. Methodology 3.1. Literature review To examine the implications of climate change for woodland biodiversity and functioning, peer‐
reviewed and grey literature was reviewed. The peer‐reviewed literature was searched using Web of Science, as it provides access to multiple databases. The literature search was performed systematically using a set of search terms, as well as following up citations and using literature known to the authors. For example, in order to examine the climate change impacts on species composition and abundance changes, climate change, regions, parameters related composition and abundance were used as the search terms. The literature on the rare and charismatic species was searched using the species name and climate change and other climate‐related parameters as the search terms (Table 3.1 and 3.2). The grey literature was identified from the websites of the institutions engaged in forestry, environment and conservation in the UK (e.g. Forestry Commission) and Europe (e.g. European Environment Agency), as well as using Google search, applying a combination of terms related to climate change and the UK forests (Table 3.3). The first 100 hits were searched as the relevance decreases afterwards. Table 3.1: Search terms used to examine species’ composition for the literature review Search term 1 Search term 2 Search term 3 Climate change Composition UK Environmental change Abundance Britain Community function Community structure Table 3.2: Search terms used to examine the rare and charismatic species Search term 1 Search term 2 Species’ names (in English and Latin) Climate change Temperature Rainfall Distribution shift Habitat shift Table 3.3: Search terms used to examine the grey literature Search term 1 Search term 2 Climate change
forest woodland species biodiversity 8
Search term 3 UK Britain England Scotland Wales Ireland 3.2. Bioclimate envelope modelling Most of the species modelling used in this study was based on an artificial neural network (SPECIES ‐ Spatial Estimator of the Climate Impacts on the Envelope of Species), but a number of species with ‘presence‐only’ data were processed using Ecological Niche Factorization Analysis (ENFA). Artificial Neural Networks (ANNs) were initially developed to model biological functions and can now be found in such diverse situations as speech recognition, molecular biology and predicting farmer risk preferences (Lek & Guégan, 1999). ANNs are proving increasingly popular because of their wide applicability and their ability to cope with complicated scenarios; these traits have commended them to ecologists who often favour them because of the complexity of many ecological problems. 3.2.1. Modelling Methodology 3.2.1.1. Data requirements Reliable plant species distribution data was sourced from the Atlas Florae Europaeae and various other distribution studies (see Appendix for full list). All these data were obtained in electronic form consisting of the European grid‐cell coordinates of the species’ presence and absences or from scans of hard‐copy book atlases. The type of distribution data can affect model performance ‐ for example, a number of studies have shown that presence/absence data is better than presence‐only data (Pearson et al., 2006) although presence‐only techniques (like ENFA) can also provide robust results (and may be necessary for studying mobile taxa like birds). The BEM models works on the basis of a correlation between species distribution and environmental factors. For all taxa, except birds, the following environmental data were used: absolute minimum temperature expected over a 20 year period, annual maximum temperature, growing degree‐days above 5°C, accumulated annual soil moisture deficit and accumulated annual soil moisture surplus. The inputs used all have a direct physiological limiting function for plant growth and were fed into the neural network along with an interpolated presence/absence data set for each species. For birds different environmental data were used based on knowledge of factors affecting their distribution in the UK, including absolute minimum temperature expected over a 20‐year period, mean summer temperature (May, June, July), mean summer precipitation (May, June, July), mean winter precipitation (December, January, February), and mean summer water availability (May, June, July). To cover as much of the possible variation in anticipated climate change as possible two main climate change scenarios (A2 and B1) were used which cover much of the range of possible driving forces of future GHG emissions (Nakićenović et al., 2009). These two scenarios were then used at 9
three different time‐slices (2011–2020, 2041–2050 and 2071–2080) which were modelled using two global climate models (HadCM3 and PCM) (Gordon et al., 2000; Pope et al., 2000; Washington et al., 2000). 3.2.1.2. Statistical and GIS tools The preparation of data and running SPECIES required a number of statistical packages to augment the processing of BEM including 'R' (R Development Core Team, 2008) (with the following packages G‐stat, lattice and maptools), SNNS v4.1 (the neural network simulator) (Zell et al., 1995), ArcCatatog and ArcMap (to digitise the distributions maps) (ArcMap, ESRI, Inc., Redlands, California) and Idrisi Kilimanjaro (to convert the digitised distributions to text files (Clark Labs, Clark University, Worcester, MA, USA). Scanned and downloaded image maps of species distributions were digitised first using Arc GIS software (ArcMap, ESRI, Inc., Redlands, California) and geo‐referenced accurately to European latitudinal and longitudinal co‐ordinates. The distribution data (both the scanned and the AFE) had to be interpolated into presence/absence data on a 0.5° grid of Europe and then interpolated further using ‘Surfer’ software into a 10 km grid by ‘Kriging’, a geostatistical gridding method (Pearson et al., 2002). Kriging aided the training of the ANN by making sure that a species presence was not adjacent a species absence when the environmental conditions for both locations were similar. The SPECIES model runs using data (species European presence/absence, environmental data, climate change scenarios) in text file format and then exports text files of future presence/absence for each scenario, as well as mapped images for visual interpretation. A multi‐layer feed‐forward (MLFF) network, trained by backpropagation algorithm, was used in the ANN because of its ability to model complex relationships between variables. The data set was randomised, and then split into three equally sized sub‐data sets that were used for the training, validation and testing (Bishop, 1995). The multi‐layer feed‐forward network was set for five input nodes, eleven hidden nodes (one layer) and one output node. Once the model is trained and validated output maps at different scales can be produced (here the European scale input was used). Climate suitability maps (see figures in section 5) are produced outlining the likelihood of a species being present (1, or the darkest shading = highest likelihood). 3.2.2. ENFA For modelling with “presence‐only” datasets, i.e., datasets containing a set of points with observed species presences, but no absence points, which is a common situation for many rare species, an Ecological Niche Factorization Analysis (ENFA) was used. This only requires known presence data in order to generate habitat suitability surfaces (Figure X). Areas with low ENFA suitability are then randomly selected and treated as pseudo‐absence points to train the SPECIES model. The full methodology is explained in Chapter 2 of MONARCH 3 (Berry et al., 2007). In this report ENFA was used for Andrena ferox, Loxia scotica, Trichomanes speciosum, Biatoridium 10
monasteriense, Catapyrenium psoromoides, Schismatomma graphidioides, Sematophyllum demissum, Thelenella modest. For two species, Catapyrenium psoromoides and Sematophyllum demissum, the kappa (see below) was so low that they were omitted from any further analysis. Figure 1: The observed presence (left) and ENFA habitat suitability index surface (right) for the mining bee, Andrena ferox. 3.2.3. Analysis of climate space projections There are a number of different methods for testing model performance, but perhaps the two most widely used are Cohen's kappa statistic (a test of the proportion of species' presences or absences that are predicted correctly after taking into account chance agreement) and the area under the receiver operating characteristic (ROC) curve (AUC) (Fielding & Bell, 1997). Kappa is dependent upon a set threshold value to determine the cut‐off between a species' presence being retained by the model whereas AUC is independent of this. Although both methods have their downsides (Allouche et al., 2006), they generally provide robust methods of performance assessment. For example, Monserud (1990) suggests the following ranges of agreement for kappa: excellent K>0.85; very good 0.7<K≤0.85; good 0.55<K≤0.7; fair 0.4<K≤0.55; and poor K<0.4. In practice, kappa is often significantly correlated with AUC (Manel et al., 2001) and they are often used together in BEM studies. Independent validation of model predictions are rare, but in one study conducted by Araújo et al (2005) they used observed distribution shifts for 116 British breeding‐birds over a twenty‐year period and compared the predictive performance of GAM, GLM, CTA and ANN models. The results demonstrated that these models were generally good at predicting the actual distribution shift, but the ANN and GAM models produced the best performance. Realistic evaluations of models are also required to validate their performance (Araújo et al., 2005): available methods range from data partitioning or splitting which are the most prevalent even if not ideal. However, if independent test data are available they provide a far superior 11
method of validation: e.g., the use of 'hindcasting' techniques, which tests the model on known past distributions (sometimes using fossil pollen‐based distributions) (Hijmans & Graham, 2006; Pearman et al., 2008). A test using breeding‐bird data over a twenty year period demonstrated that non‐independent data (data splitting) produced more accurate test statistics than that of the independent data (20‐year observed range shifts) suggesting that most models over‐estimate performance (Araújo et al., 2005). Testing with other taxa against independent data is troublesome though as most data are difficult to come by. 12
4. Results 4.1. Reviewing the effects of climate change on biodiversity and community function We briefly outline how climate change has already begun to affect plant and animal autoecology and cover four main aspects of change (physiological changes, distribution shifts, phenology and evolutionary change) pertinent to species found in woodland habitats. 4.1.1. Physiological responses All species respond directly to climatic factors and for many species climate is largely the defining element governing their growth and reproduction; it is important to note however, that whilst it is comparatively easy to study the response of species to climatic factors or even to combinations of factors (which better reflects reality), scaling up to community and ecosystem response is much harder. The three main response factors of future climate change are temperature, water availability and CO2, others though (not discussed here) include extreme events such as fire, storms, floods and fire. Evidence of the effects of these factors on growth is accumulating, although it is not always easy to disentangle their relative contributions (Hughes, 2000). Plant response to CO2, temperature and water availability is a well‐studied field and a number of generalisations regarding response can be made. For most temperate woodland plants (i.e., that uses C3 carbon fixation), increased levels of CO2 stimulates growth (Hyvönen et al., 2007) generally through a net increase in photosynthesis (Ziska et al., 2003). Although there is often a long‐term acclimation of photosynthesis to increased CO2, and this response frequently interacts with temperature and water availability, the overall pattern, certainly at a forest stand level, is for increased growth for many woodlands in northern latitudes. This pattern is more complicated though at more southerly latitudes where drought effects may have an over‐riding impact on, for example, tree growth (Jump et al., 2006). Increased growth is not the only response though: for example, the ratio of carbon to nitrogen in the leaf is also affected by CO2, which has consequences for decomposition, herbivory and frost resistance (Bazzaz, 1996). Reproduction and germination are also affected by CO2, but responses vary across species. Plant response to temperature is well recorded and is clearly one of the major determinants of species' growth and distribution (Woodward, 1987). Dendroecology has highlighted variations in growth for species along climatic (temperature and precipitation) gradients over centuries of growth (Dittmar et al., 2003; Bascietto et al., 2004). Other responses to changes in temperature include effects on the abundance of plants with winter seed chilling requirements (Inouye et al., 2000) and effects on reproductive systems (Aerts et al., 2004). Temperature can have direct impacts on plant physiology on a day‐to‐day basis: for example extreme heat can reduce photosynthesis and increase respiration in many temperate tree species (Rennenberg et al., 2006). However, the most important response to increased temperatures is likely to be the extension of 13
the growing season for most species in northern Europe (Hyvönen et al., 2007) and a reduction in growth in southern Europe (Reich & Oleksyn, 2008). Water is one of the most important limiting factors for plant growth (Pigott & Pigott, 1993) and will play an important role in community composition in the future. Not only will annual precipitation in many European countries decline, but extreme events such as drought and flood, are very likely to increase too (Beniston et al., 2007). Reduced water availability limits growth through a reduction in water uptake in the roots which reduces the water potential (along a root to leaf gradient). However, different species demonstrate a range of ecological responses to water availability, which is readily demonstrated within European trees by the contrasting responses of the drought intolerant beech and the drought tolerant sessile oak (Thomas, 2000; Gessler et al., 2007). These differential effects are likely to change the competitive balance in communities and may result in the changes in community dominance (Fotelli et al., 2005; Saxe & Kerstiens, 2005; Sthultz et al., 2007; Piovesan et al., 2008). These effects may be further enhanced by delayed responses to drought in some species due to an inability to recover from a breakdown of photosynthetic ability (Breda et al., 2006). This has been seen in some woodlands in Britain when beech stands continued to decline or die several years after the severe drought of 1976 (Peterken & Mountford, 1996). There is huge variation in the ability of different plant species to cope with the above‐mentioned climatic factors ‐ this is clearly reflected in the myriad vegetation biomes across the globe. Although there is a large body of work which has examined the responses of plant species and even communities to CO2, precipitation and temperature, there is still uncertainty over the outcomes for many plant communities and ecosystems, particularly when other factors, like land‐
use change are taken into account (Tylianakis et al., 2008). 4.1.2. Range and altitudinal shifts The role of climate in determining a species' distribution has long been known (Woodward, 1987) and evidence of species' range shifts during previous cooling and warming events is well documented through extensive palaeoecological studies (Davis & Shaw, 2001; Moore, 2005). However, whereas palaeoecological studies have demonstrated the migration of species often tracked a relatively slow changing climate after glacial periods (except the Younger Dryas period), more recent climatic change has occurred at a faster rate (Jansen et al., 2008). This poses problems for many species that are not able to migrate quickly enough (e.g., tree species), but already we have recorded distribution changes from a range of taxa. Two main responses have been documented in species: range shifts, where the distribution of a species has moved (usually polewards) as the climate has warmed (Parmesan, 2006); and, altitudinal or elevational shifts where montane and sub‐montane species have shifted their zone of occurrence to higher altitudes (Peñuelas & Boada, 2003; Raxworthy et al., 2008; Lenoir et al., 2010). Many of these effects have been seen in more mobile taxa (e.g., butterflies, birds) although we are already witnessing the decline of some plant species at the rear edge of their distribution (e.g., beech in the Catalonian 14
mountains: Peñuelas et al., 2007), as well as shifts in elevation for a number of shrub and tree species in Scandinavia (Kullman, 2002). Long‐lived perennial plants present a more difficult group to study as they can often have significant lags in response. These changes are not only determined by temperature, long‐term changes in precipitation can also affect species' distributions. In the southern or rear edge of species' distributions, precipitation is known to be limiting factor for survival (Engelbrecht et al., 2007; Morin et al., 2007) and recent changes in precipitation patterns have started to affect some species in southern Europe (Castro et al., 2004; Jump et al., 2006). 4.1.3. Phenological change Perhaps the largest collection of observations recording climatic change responses is in the study of phenology (Parmesan, 2006) and this aspect of species' biology has been the focus of a number of reviews and studies on a range of taxa (Root et al., 2003; van Vliet et al., 2003; Visser & Both, 2005). Some studies observing phenological response date back many decades or even centuries (Sparks & Carey, 1995) and often records have been maintained by non‐academic observers. For plant phenology, the range of responses recorded has been large, but the most common are for dates of first flowering, bud burst and leafing; however, autumn events are less well recorded (Menzel et al., 2006). The main patterns observed thus far are earlier response to spring events, like bud burst or first flowering dates and slightly delayed leaf fall, but responses can vary across species (Fitter & Fitter, 2002; Menzel et al., 2006). Peñuelas et al (2002) found that whilst the mean flowering date advanced for a range of plant species, some species in the Mediterranean region had delayed flowering. Further inconsistencies in phenological trends can be seen in regional differences too: Root et al (2003) and Parmesan (2007) have reported a greater response at northern latitudes although this pattern reflects differences in regional climate changes. Reports focussing solely on the responses of woodland plants are not common, but similar results have been reported so far; for example, in Slovakia, six woodland herbs species have shown earlier onset of flowering in response to climate over the last decade (Schieber, 2007). Similarly, in other studies of ‐ or including ‐ European forest tree species, a general pattern of advancing leaf or flowering dates over the last few decades due to warming spring temperatures has been reported (Chmielewski & Rotzer, 2001; Gordo & Sanz, 2005). 4.1.4. Adaptation Although there are widespread records of species' populations responding to climate change by migrating or becoming locally extinct, many species also have an ability to adapt in situ to new climatic pressures (Jump & Penuelas, 2005; Aitken et al., 2008). This may occur with species that have a large phenotypic plasticity (the ability of individuals to modify their behaviour, morphology 15
or physiology in response to altered environmental conditions) (Nicotra et al., 2010) (Hubert & Cottrell, 2007), but other factors like fecundity and biotic interactions, may play a part (Aitken et al., 2008). Adaptation is also likely to vary within species throughout their distribution; for example, the genetic diversity of species' populations at range margins is sometimes more impoverished due to the fragmented nature, low fecundity or effects of introduced species on the population resulting in lower adaptation (Hampe & Petit, 2005; Aitken et al., 2008). Species with large distributions and large populations with high genotypic variation are more likely to adapt to climate change, despite a possible generational response lag (Aitken et al., 2008). Foresters have made use of inter‐specific genetic variation in tree species for years and have drawn on different provenances to meet suitable local climatic conditions (Leverenz et al., 1999; Greenwood et al., 2002; Boshier & Stewart, 2005; Hubert & Cottrell, 2007). This artificial mixing of genetic diversity may well actually benefit some species: many woodlands in the UK are composed of native species, but often their provenance is foreign (Rackham, 2006); therefore, woodlands that have genetic provenances of species (e.g., common oak) from lower latitude European countries, may well be better adapted to future climate change. Challenges to the conservation ethos that local provenance is best are discussed further in section 5.4.9. Phenotypic plasticity is not the only feasible adaptation response, as some species are capable of rapid evolution (Bradshaw & Holzapfel, 2006). However, for many species there will be constraints on evolutionary response including the lag between climatic change and response (e.g., for most perennial plants (Davis et al., 2005)); lack of genetic variation (e.g., in small populations (Willi et al., 2006)); and erosion of genetic variation (Skelly et al., 2007). This means that plants that are likely to evolve to adapt to climate change will be small, have short life cycles and have large population sizes (Fitter & Fitter, 2002; Berteaux et al., 2004; Valladares, 2008). There is some evidence that plants can adapt to rapid climate change through evolution, for example, Franks et al (2007) have shown that the annual field mustard (Brassica rapa) was able to respond to drought conditions by selecting for earlier flowering in the population under study. However, the evidence suggests for many woodland species, once phenotypic plasticity is stretched to its limit (Willi et al., 2006), the options in the face of climate change are either migration or local extirpation (Jump & Penuelas, 2005; Valladares, 2008). 4.2. Impacts of climate change on woodland Priority Habitat species ‐ results from the bioclimate envelope modelling 4.2.1. Overall picture A total of 178 species woodland species have been modelled for the whole of the British Isles, of these 31 are tree species, 18 shrub species, 86 ground flora species, 11 mammals, 1 moss, 5 lichens, 4 butterflies, 14 birds, 1 beetle, 1 bee, 4 ants and 2 amphibians. The overwhelming 16
pattern is for species bioclimatic space gain and mean 1 figures for all species and all scenarios vary from 12% gain (Had A2 2020) to 100% gain (PCM A2 2080). Table 4.1 outlines the main responses to climate change in the British Isles for some key woodland species. These responses are primarily described in terms of the change in climate space in the British Isles, but significant differences between the countries are noted, as some species have the potential to gain climate space one country (e.g. in Scotland) and lose it in another (e.g. England) and thus while the net change may be small these gains and losses are important. Their significance for woodland composition, structure and function are assessed using knowledge of their ecology and other research on the species concerned. Table 4.1: Key British Woodland Species Change in British Trees climate space Very significant loss (>90% to 100%) Significant loss (>50% to <90%) 1
Bird cherry Prunus padus Shrubs Other notable species Pied flycatcher Ficedula hypoleuca Twinflower Linnaea borealis, Narrow‐headed ant Formica exsecta Moderate loss (>25% to <50%) Scots pine Pinus sylvestris, Wych elm Ulmus glabra, Grey sallow Salix cinerea Blackberry Rubus fruticosus, Wild red raspberry Rubus idaeus, Cowberry Vaccinium vitis‐idaea St Johns wort Hypericum maculatum, a lichen Biatoridium monasteriense, Stump lichen Cladonia botrytes, Bellflower Campanula latifolia, Globeflower Trollius europaeus, Wood vetch Vicia sylvatica, Scottish crossbill Loxia scotica, Great crested newt Triturus cristatus Small loss (>15% to <25%) Sycamore Acer pseudoplatanus, Silver birch Betula pendula, Downy birch Betula pubescens, Juniper Juniperus communis, Goat willow Salix caprea Yellow‐necked mouse Apodemus flavicollis, Wild angelica Angelica sylvestris Insignificant loss (<15%) European hornbeam Carpinus betulus, Ash Fraxinus excelsior, Aspen Populus tremula, Oak Quercus petraea, Rowan Sorbus aucuparia Hazel Corylus avellana, Sweet woodruff Galium odoratum, Wood avens Geum urbanum, Honeysuckle Lonicera periclymenum After taking out the extreme gains of 1000% or more for 4 species.
17
Table 4.1: Key British Woodland Species Insignificant gain (<15%) Midland hawthorn Crataegus laevigata, Hawthorn Beech Fagus sylvatica, English Crataegus monogyna, holly Ilex aquifolium, Wild Bell heather Erica cinerea, cherry Prunus avium, Blackthorn Prunus spinosa, Common oak Quercus robur, Gorse Ulex europaeus, English yew Taxus baccata, European privet Alder Alnus glutinosa, Ligustrum vulgare, Blackthorn Prunus spinosa Orange‐fruited elm lichen Caloplaca luteoalba, Wavy hair grass Deschampsia flexuosa, Herb Robert Geranium robertianum, Ground ivy Glechoma hederacea, English ivy Hedera helix, Wood sage Teucrium scorodonia Small gain (>15% to <25%) Crab apple Malus sylvestris, Small‐leaved lime Tilia cordata European privet Ligustrum vulgare Ajuga Ajuga reptans, Slender flase brome Brachypodium sylvaticum, Dog's mercury Mercuralis perennis Moderate gain (>25% to <50%) Field maple Acer campestre, Wild fruit trees Pyrus pyraster Bloodtwig Cornus sanguinea, Spindle Euonymus europaeus, Common buckthorn Rhamnus cathartica Roe deer Capreolus capreolus, Dormouse Muscardinus avellanarius, Old man's beard Clematis vitalba, Early dog violet Viola reichenbachiana Significant gain (>50% to <90%) Whitebeam Sorbus aria Yellow archangel Lamiastrum galeobdolon Stag beetle Lucanus cervus, Wood spurge Euphorbia amygdaloides, Barbastelle bat Barbastella barbastellus, Red squirrel Sciurus vulgaris, Oxlip Primula elatior, Chequered skipper Carterocephalus palaemon, Large skipper Ochlodes venata, Wryneck Jynx torquilla, Turtle dove Streptopelia turtur, Pool frog Rana lessonae Very significant gain (>90%) Common box Buxus sempervirens, Service tree Sorbus domestica, Wild service tree Sorbus torminalis, Large‐leaved lime Tilia platyphyllos 4.2.2. Key tree species In terms of the dominant canopy tree species ‐ common oak (Quercus robur), sycamore (Acer pseudoplatanus), beech (Fagus sylvatica), ash (Fraxinus excelsior), silver birch (Betula pendula) ‐ the overall mean change is one of a slight loss of climate space. However, some of these species do better. Surprisingly, beech only loses climate space in the two 2080 scenarios (and even then only by 2% for the whole of the British Isles). Common oak gains space in all scenarios, except HadA2 2080 (and then it is only projected to lose 14%). The contrast between these two species is slightly surprising considering the better tolerance of drought that common oak has (see section 4.2.1 for more detail). A breakdown of the country responses shows that common oak loses most space in England and gains the most in Scotland. Beech follows this pattern except it also has quite dramatic potential losses in Wales. Ash has a similar response to common oak in that it only loses climate space in the three 2080 scenarios; not surprisingly it only maintains or gains space in Scotland, conversely it loses the most space in England. 18
4.2.3. Key Shrub species On the whole, the main woodland shrub species do quite well and 13 of the 18 species maintain or gain climate space (Table 4.1). Of these, gorse (Ulex europaeus), heather (Calluna vulgaris), Midland hawthorn (Ulex gallii), privet (Ligustrum vulgare), dogwood (Cornus sanguinea), buckthorn (Rhamnus cathartica) and spindle (Euonymus europaeus) do the best. The worst performers are cowberry (Vaccinium vitis‐idaea), bramble (Rubus fruticosus), gooseberry (Ribes uva‐crispa) and raspberry (Rubus idaeus), but even hazel loses a small amount of bioclimate space. Not surprisingly, losses are greatest in England and minimal in Scotland; while dogwood, spindle and buckthorn all gain large amounts of bioclimatic space in Scotland. 4.2.4. Key ground flora The number of ground flora winners and losers is more complex; there are a number of species, for example that lose bioclimatic space in England and Scotland (twinflower ‐ Linnaea borealis is a good example), but there are also some species that appear to do badly in Wales, but nowhere else: ryegrass (Poa trivialis), wood sorrel (Oxalis acetosella), Yorkshire fog (Holcus lanatus), hedge woundwort (Stachys sylvatica), common cottongrass (Eriophorum angustifolium), tall oatgrass (Arrhenatherum elatius), tufted hairgrass (Deschampsia cespitosa), wood sedge (Carex sylvatica), honeysuckle (Lonicera periclymenum), Lords and Ladies (Arum maculatum), hare’s tail cottongrass (Eriophorum vaginatum), globeflower (Trollius europaeus) and hogweed (Heracleum sphondylium). Other losses include: Lady’s slipper orchid (Cypripedium calceolus), Twinflower, small cow‐wheat (Melampyrum sylvaticum), bellflower (Campanula latifolia), wood vetch (Vicia sylvatica), St. Johns wort (Hypericum maculatum), globeflower (Trollius europaeus), lesser twayblade (Listera cordata), crowberry (Empetrum nigrum), grass of Parnassus (Parnassia palustris), hogweed (Heracleum sphondylium) and wild angelica (Angelica sylvestris), all lose more than 15% bioclimatic space in the British Isles (with most losses occurring in England). 4.2.5. Other key species Of the mobile species there are a number of major winners including: amphibia ‐ pool frog (Rana lessonae); mammals ‐ Bechstein`s bat (Myotis bechsteinii), lesser horseshoe bat (Rhinolophus hipposideros), Barbastelle bat (Barbastella barbastellus), red squirrel (Sciurus vulgaris), roe deer (Capreolus capreolus), dormouse (Muscardinus avellanarius); birds ‐ red‐backed shrike (Lanius collurio), turtle dove (Streptopelia turtur), tree sparrow (Passer montanus); butterflies ‐ Ochlodes venata, Erynnis tages; beetles ‐ Lucanus cervus. The species that lose most bioclimatic space are: ants ‐ Scottish wood ant (Formica aquilonia), narrow head ant (Formica exsecta), southern wood ant (Formica rufa), hairy wood ant (Formica lugubris); birds ‐ pied flycatcher (Ficedula hypoleuca), Scottish crossbill (Loxia scotica), spotted flycatcher (Muscicapa striata), song thrush (Turdus philomelos), bullfinch (Pyrrhula pyrrhula), 19
black grouse (Tetrao tetrix), capercaillie (Tetrao urogallus); mammals ‐ yellow‐necked mouse (Apodemus flavicollis), brown hare (Lepus europaeus), amphibia ‐ great crested newt (Triturus cristatus); and butterflies ‐ pearl‐bordered fritillary (Boloria euphrosyne). Nearly all mammals gain new climate space in Scotland (except brown hare, which loses 9%) and for the other mobile taxa the only species that lose space are pearl‐bordered fritillary, pied flycatcher, Scottish crossbill, fieldfare (Turdus pilaris), mining bee (Andrena ferox), Scottish wood ant, narrow head ant and hairy wood ant. In England, a number of species run a risk of becoming extinct by 2080 including pied flycatcher, black grouse, bullfinch, narrow head ant and hairy wood ant. The following sections focus on the main structural responses in different Priority Habitats and NVCs; two additional sections outline the possible responses of rare and charismatic species. 4.2.6. Wood‐Pasture & Parkland This Priority Habitat is described as “areas that have been managed by a long‐established tradition of grazing allowing, where the site is in good condition, the survival of multiple generations of trees, characteristically with at least some veteran trees or shrubs” (Maddock, 2010). It roughly corresponds to several National Vegetation Classification (NVC) habitats including W10 (Quercus robur‐Pteridium aquilinum‐Rubus fruticosus woodland), W11 (Quercus petraea‐Betula pubescens‐
Oxalis acetosella woodland), W14 (Fagus sylvatica‐Rubus fruticosus woodland), W15 (Fagus sylvatica‐Deschampsia flexuosa woodland) and W16 (Quercus spp.‐Betula spp.‐Deschampsia flexuosa woodland) (Rodwell, 1991; Rodwell & Dring, 2001). Clearly, unlike most of the other Priority Habitats, Wood Pasture and Parkland can be found across a range of soil types and its classification is more than anything a product of human intervention rather than edaphic or phyto‐
sociological associations. Ninety‐eight of the species modelled were known to be associated with woodland pasture and parkland. Of those, 17 were tree species tree species, 8 shrubs and 51 ground flora species. Table 4.2 highlights the major modelling results and the overall mean change in species climate space for all scenarios was a slight gain of 9% for the British Isles. The two key wood‐pasture tree species, beech and common oak, generally have a good outlook in the UK with potentially insignificant losses, although both could lose more space in England by 2080 (oak by up to 47%). However, beech's greater intolerance of drought is further complicated due to the lack of data on determining if woodland or parkland trees are more sensitive than forest trees in these conditions. Whilst there has been research on evapotranspiration in parkland trees (Paço et al., 2009), a comparison with woodland trees has not been made. Long term studies at Denny Wood, an ancient wood‐pasture, indicate that over a 40 year period beech trees were the most severely affected by drought, although some oak were killed too and drought 20
Table 4.2: Modelling results for Wood‐Pasture & Parkland species Common name Botanical name Sessile oak Quercus petraea Ash Fraxinus excelsior European hornbeam Carpinus betulus Aspen Populus tremula English holly Ilex aquifolium Scots Elm Ulmus glabra Silver birch Betula pendula Downy birch Betula pubescens Great Maple Acer pseudoplatanus Common oak Quercus robur Beech Fagus sylvatica Wild cherry Prunus avium Crab apple Malus sylvestris Small‐leaved lime Tilia cordata Field Maple Acer campestre Changes in climate space insignificant loss insignificant loss insignificant loss insignificant loss insignificant loss moderate loss small loss small loss small loss insignificant gain insignificant gain insignificant gain small gain small gain moderate gain Shrubs Blackberry Rubus fruticosus moderate loss Ground flora Wavy hair grass Bracken Deschampsia flexuosa Pteridium aquilinum little change little change susceptibility increased with tree age (Mountford et al., 1999). Similar effects of drought have also been noted on beech in other (closed) woods, such as Lady Park Wood (Peterken & Jones, 1989a,b). Trees have been shown to have greater transpiration rates, (and hence water use) at woodland edges than deeper in woodlands (Taylor et al., 2001; Giambelluca et al., 2003; Herbst et al., 2007) due to the edge trees having greater exposure to wind currents. Trees in more open wood pasture similarly may be more exposed, but this could be counterbalanced by less competition for ground water from grassy vegetation than from other trees in dense woodland. The net effect, therefore, is unclear and more research is needed. 4.2.7. Upland Oakwood There are believed to be between about 70,000 and 100,000 ha of Upland Oak woods in the UK and it is found mostly throughout the north and west of the UK (Maddock, 2010). The Priority Habitat classification is mainly based on the prevalence of oak and birch in upland sites rather than edaphic association. The resulting correspondence to NVC habitats is therefore wide and includes W10 (Quercus robur‐Pteridium aquilinum‐Rubus fruticosus woodland), W11 (Quercus petraea‐
Betula pubescens‐Oxalis acetosella woodland), W16 (Quercus spp.‐Betula spp.‐Deschampsia flexuosa woodland) and W17 (Quercus petraea‐Betula pubescens‐Dicranum majus woodland) (Rodwell, 1991; Rodwell & Dring, 2001). 21
Many of the main species associated with Upland Oak woods are modelled here (including all the canopy trees and much of the shrub and ground flora). Notable omissions from the species list include some of the main bird species often associated with the woodland type (e.g., redstart Phoenicurus phoenicurus, wood warbler Phylloscopus sibilatrix) and some mosses (e.g,. Eurhynchium spp). Otherwise, the 86 species modelled that are found in Upland Oak woods give a fairly good representation of the community type. The modelling results suggest that these woods are perhaps one of the least vulnerable habitats to climate change with a mean change in bioclimatic space for trees of +18% (perhaps not surprising given that most of these woods are to be found in Scotland and north‐west England). The dominant tree ‐ sessile oak ‐ also does well and gains 25% new space in Scotland. Table 4.3 provides an overview of the main plant species responses. Table 4.3: Modelling results for upland oakwood woodland species Common name Botanical name Sessile oak Quercus petraea European hornbeam Carpinus betulus Ash Fraxinus excelsior English holly Ilex aquifolium Rowan Sorbus aucuparia Scots elm Ulmus glabra Sycamore Acer pseudoplatanus Silver birch Betula pendula Common oak Quercus robur Beech Fagus sylvatica Changes in climate space insignificant loss insignificant loss insignificant loss insignificant loss insignificant loss moderate loss small loss small loss insignificant gain insignificant gain Shrubs Cowberry Hazel Blackthorn European privet Vaccinium vitis‐idaea Corylus avellana Prunus spinosa Ligustrum vulgare moderate loss insignificant loss insignificant gain small gain Ground flora Wood sorrel Common male fern Brean down Tufted hair grass Enchanters Nightshade Oxalis acetosella Dryopteris filix‐mas Viola riviniana Deschampsia cespitosa Circaea lutetiana insignificant loss insignificant loss insignificant loss insignificant loss insignificant gain Hornbeam could dramatically increase its range into Scotland and may become a competitor to the dominant sessile oak in the future along with beech and sycamore (both of which have potential to gain space in Scotland). However, sessile oak has been shown to be highly tolerant of drought (Niinemets & Valladares, 2006) which could lead to a change in competitive balance between it and other species (notably common oak, beech and ash) in the future. Silver birch also loses significant bioclimate space in Scotland (particularly along the coastal areas where this habitat is found). 22
Other notable changes in this habitat include a possible decline in cowberry (Vaccinium vitis‐idaea) (27% of space lost in Scotland), but expansions of blackthorn (Prunus spinosa) and privet (Ligustrum vulgare). Of the main ground flora species in this habitat, the biggest losers of bioclimatic space are wood sorrel, common male fern (Dryopteris filix‐mas) and common dog violet (Viola riviniana). 4.2.8. Lowland Beech and Yew Woodland 121 species were modelled in this habitat including 24 tree species, 12 shrub species and 85 ground flora species. Beech is the dominant tree species and in southern England is at the northern edge of its (native) range, although it is successfully planted further north; the same species is found much further south in Europe. The NVC classifications for this habitat are W12 (Fagus sylvatica‐Mercurialis perennis woodland), W13 (Taxus baccata woodland), W14 (Fagus sylvatica‐Rubus fruticosus woodland) and W15 (Fagus sylvatica‐Deschampsia flexuosa woodland) (Rodwell, 1991). The results suggest that beech could be minimally affected in the UK under the projected climate scenarios, but since its distribution extends into lower latitudes in continental Europe this is not surprising. There are other aspects of climate change, however, that may reduce its capacity to respond. Water stress from prolonged droughts is known to reduce growth, canopy condition and competitiveness of beech and may result in increased mortality (Peterken & Mountford, 1996). In north‐east Spain, at its southern limit, high spring and summer temperatures were shown to decrease growth and tree establishment of beech at lower altitudes and precipitation limited adult growth (Jump et al., 2007). Drought strongly limited growth of populations in this area (Jump et al., 2006). Table 4.4 highlights the main plant species responses. Broadmeadow et al (2005) predicted that beech would suffer growth reductions in south east England by 2050, which would agree with the model results here. Observations have shown that in central and northern Europe beech growth is increasing in some areas (Dittmar et al., 2003; Bascietto et al., 2004; Gärtner et al., 2008), although in southern Europe (Spain and Italy) it is showing a reduction in growth and there are already changes to its distribution due to successive warmer and drier years (Peñuelas & Boada, 2003; Peñuelas et al., 2007). In a comparison of various tree species, beech was considered to be relatively drought intolerant (Niinemets & Valladares, 2006). Periods of drought may become more frequent in the future and this may have significant impacts on the survival of beech. While beech growth and competitiveness has been shown to increase with elevated levels of CO2, it suffers badly when drought controls are introduced and can be outcompeted by other species (Fotelli et al., 2002; Fotelli et al., 2005). It seems that increased droughts will be a problem for beech even if the future projected mean climate change appears to be suitable. 23
Table 4.4: Modelling results for lowland beech and yew woodland species Trees Botanical name Changes in climate space Silver birch Betula pendula small loss Downy birch Betula pubescens small loss Sessile oak Quercus petraea insignificant loss Ash Fraxinus excelsior insignificant loss English holly Ilex aquifolium insignificant loss Beech Fagus sylvatica insignificant gain English yew Taxus baccata insignificant gain Common oak Quercus robur insignificant gain Common box Buxus sempervirens significant gain Shrubs Blackberry Rubus fruticosus moderate loss Ground flora Wood sorrel Wavy hair grass Bracken Dog's mercury Oxalis acetosella Deschampsia flexuosa Pteridium aquilinum Mercuralis perennis insignificant loss little change little change small gain Even if beech does not lose climate space it may lose the competitive edge over other tree species. Beech is dependent on available soil water to maintain growth and although the model results suggest that it will fare better than the oak species, it is highly likely that, given a succession of drought years, that oak will cope better than beech (Raftoyannis & Radoglou, 2002). The response of beech to drought also depends on soil type and the underlying hydrology; beech on chalk may be able to access additional moisture via capillary action, but those on acid gravels and weakly gleyed soils may be at a greater risk of drought. Also, older trees seem more likely to move into a period of decline and mortality following drought episodes, whereas young trees may be able to respond well if shade and competition from mature trees is reduced. The other major tree species in this habitat is yew. Although pure yew woodlands are rare in the UK (Kingley Vale in Sussex stands out as the best example), yew can often form small stands in other woods. Yew is considered to be quite tolerant of heat and drought and this is seen in the results for England (small gain of 2%); in Scotland, where it is commonly only found in churchyards, it gains 19% in bioclimate space. Although yew is a slow coloniser and depends upon the right scrub conditions to develop (Tansley, 1965), it is possible that it could increase its range in calcareous woods in Scotland in the future. 4.2.9. Upland Mixed Ashwoods This is a particularly important habitat in the North‐west of the British Isles but also can be found infrequently in England and is fairly widespread on the continent on steeper slopes (Barbati et al., 2007). It corresponds to two main NVC habitats: W9 (Fraxinus excelsior‐Sorbus aucuparia‐
Mercurialis perennis woodland) and W8e (Fraxinus excelsior‐Acer campestre‐Mercurialis perennis 24
woodland, Geranium robertianum sub‐community) (Rodwell, 1991). Upland ashwoods predominate on steeper gorge sites, the canopy is usually dominated by ash, but often accompanied by sessile and common oak, sycamore and birch whilst hazel and hawthorn are the commoner shrubs; towards the north and west field maple becomes less abundant and rowan, alder and wych elm become more prevalent. The ground layer is normally consists of dog’s mercury (Mercurialis perennis), wood sorrel, wood avens (Geum urbanum), common male fern, enchanter’s nightshade (Circaea lutetiana), ground ivy (Glechoma hederacea), common dog violet, ivy (Hedera helix) and stinging nettle (Urtica doica). Of these, common male fern, nettle, wood avens, wood sorrel and common dog violet will all lose bioclimatic space (see Table 4.5). Table 4.5: Modelling results for upland mixed ashwood species Trees Botanical name Bird cherry Prunus padus Scots elm Ulmus glabra Sycamore Acer pseudoplatanus Silver birch Betula pendula Downy birch Betula pubescens Ash Fraxinus excelsior Sessile oak Quercus petraea Rowan Sorbus aucuparia Common oak Quercus robur Alder Alnus glutinosa Changes in climate space significant loss moderate loss small loss small loss small loss insignificant loss insignificant loss insignificant loss insignificant gain insignificant gain Shrubs Hazel Hawthorn Blackthorn Corylus avellana Crataegus monogyna Prunus spinosa insignificant loss insignificant gain insignificant gain Ground flora Lady`s Slipper Orchid Small Cow‐wheat Wood Vetch Common male fern Stinging nettle Wood sorrel Wood Avens Brean down English ivy Herb robert Wild Garlic Wood sage Enchanters Nightshade Dog's mercury Ground ivy Herb paris Cypripedium calceolus Melampyrum sylvaticum Vicia sylvatica Dryopteris filix‐mas Urtica dioica Oxalis acetosella Geum urbanum Viola riviniana Hedera helix Geranium robertianum Allium ursinum Teucrium scorodonia Circaea lutetiana Mercuralis perennis Glechoma hederacea Paris quadrifolia significant loss significant loss significant loss insignificant loss insignificant loss insignificant loss insignificant loss insignificant loss insignificant gain insignificant gain insignificant gain insignificant gain insignificant gain small gain small gain significant gain The species composition of this habitat is very well covered with 95 species modelled (including all the main canopy and shrub species), although important missing species include dark red helleborine (Epipactis atrorubens), Jacob`s ladder (Polemonium caeruleum), autumn crocus 25
(Colchicum autumnale) and whorled solomon`s seal (Polygonatum verticillatum) and a number of beetle, flies and other invertebrates. Like the oak woods, upland ash woods which are found in the north west of the British Isles seem quite resilient to climate change (Table 4.5). In Scotland, the mean change in bioclimate space for the trees is +15%, with only five species losing more than 50% bioclimate space: Lady`s slipper orchid, Herb Paris (Paris quadrifolia), common cow wheat (Melampyrum sylvaticum), bird cherry (Prunus padus), wood vetch (Vicia sylvatica). Furthermore, species that are just beginning to break into the Scotland may do much better in future (e.g., the lime species which increase by 33% and 54% for T. cordata and T. platyphyllos). Alder may also increase if wetter conditions in winter allow, while silver birch could lose 19% of its bioclimate space in north‐west Britain (although this may be countered somewhat by local topography as these woodlands are usually colder and wetter than the surrounding open countryside). One concern is whether sycamore in upland ash woods will start to become locally more frequent and compete more with ash to become the dominant canopy tree (despite an overall UK loss of bioclimate space it has a potential 16% increase in Scotland and ash starts to lose space western Scotland in 2080). In the understory, common hawthorn could continue to expand its range, although it may have to compete more with blackthorn (+31%). 4.2.10. Wet Woodland Wet woodlands are found on poorly drained and seasonally wet soils on a range of soil types, including nutrient‐rich mineral and acid, nutrient‐poor organic ones (Rodwell, 1991). Often wet woods occur within wet flushes in other woodland types (e.g. with upland mixed ash or oakwoods) (Maddock, 2010). Correspondence between this Priority Habitat and the NVC is relatively straightforward although there are 7 main NVC habitats that are relevant. The main groups are W1 (Salix cinerea‐Galium palustre woodland), W2 (Salix cinerea‐Betula pubescens‐Phragmites australis woodland), W3 (Salix pentandra‐Carex rostrata woodland), W4c (Betula pubescens‐Molinia caerulea woodland Sphagnum sub‐community), W5 (Alnus glutinosa‐Carex paniculata woodland), W6 (Alnus glutinosa‐Urtica dioica woodland), and W7 (Alnus glutinosa‐Fraxinus excelsior‐
Lysimachia nemorum woodland) (Rodwell, 1991). These woodlands are dominated by alder, silver birch, goat willow (Salix caprea) and grey willow (Salix cinerea), but also ash and occasionally common oak and sycamore. The species composition of this habitat is reasonably well covered with 67 species (see Table 4.6). The majority of species modelled for wet woodlands cope quite well with the various climate change scenarios (mean 34% gain for the British Isles). Of the main tree species, both willow species, birch and sycamore decline by more than 10%, but all the tree species show a decline in England and one of the important shrub species, buckthorn (Rhamnus cathartica), increases by 21%. The implications of these changes could be quite 26
Table 4.6: Modelling results for wet woodland species Trees Botanical name Grey sallow Salix cinerea Scots elm Urtica dioica Downy birch Betula pubescens Goat willow Salix caprea Sycamore Acer pseudoplatanus Ash Fraxinus excelsior Alder Alnus glutinosa Common oak Quercus robur Changes in climate space moderate loss moderate loss small loss small loss small loss insignificant loss insignificant gain insignificant gain Shrubs Common buckthorn Rhamnus cathartica moderate gain Ground flora Grass of Parnassus Globeflower Purple moor grass Bog‐moss Common reed Tufted sedge Marsh valerian Greater tussock sedge Parnassia palustris Trollius europaeus Molinia caerula Sphagnum cuspidatum Phragmites australis Carex elata Valeriana dioica Carex paniculata moderate loss moderate loss insignificant loss insignificant loss insignificant gain small gain small gain moderate gain profound as all of the dominant canopy species could struggle in southern Britain; the picture is somewhat better for Scotland as alder and grey willow could both increase their range. 4.2.11. Native Pine Woodlands Native Pine Woodlands are dominated by Scots pine, although birch, rowan (Sorbus aucuparia), alder are also found and sessile oak occurs infrequently. The NVC has only one corresponding classification ‐ W18 (Pinus sylvestris‐Hylocomium splendens woodland) (Rodwell, 1991); they occur throughout the central and north‐eastern Grampians and in the northern and western Highlands of Scotland on infertile podsolic soils (Maddock, 2010). This habitat has only 34 species represented in the modelling results, although all the main tree species, including Scots pine (Pinus sylvestris), are included (Table 4.7). In Scotland, Scots pine is projected to lose suitable climate space significantly under the high emission scenarios (A2) and moderately under the low emissions scenario (B2) by 2080. Silver birch is also projected to decrease its suitable climate space in Scotland, although most of its climate space within pine woodlands remains until 2080. On the other hand, alder and sessile oak are projected to increase their suitable climate space. There is little change projected for downy birch and rowan. For understory small trees and shrubs, the suitable climate space is projected to decline for common juniper, while that of aspen (Populus tremula), holly (Ilex aquifolium), hazel, bell heather (Erica cinerea) and crowberry show little change. Some ground flora species are projected to lose their climate space, including lesser twayblade and twinflower (see 4.3.7). The charismatic red 27
Table 4.7: Modelling results for native pine woodland species* Trees Botanical name Scots pine Pinus sylvestris Silver birch Betula pendula Juniper Juniperus communis Downy birch Betula pubescens Rowan Sorbus acuparia Aspen Populus tremula English holly Ilex aquifolium Alder Alnus glutinosa Sessile oak Quercus petraea Shrubs Hazel Crowberry Bell heather Corylus avellana Empetrum nigrum Erica cinerea Ground flora Twinflower Linnaea borealis Lesser twayblade Listera cordata *Projected changes in suitable climate space in Scotland Changes in climate space Moderate loss small loss small loss insignificant loss insignificant loss insignificant loss insignificant gain moderate gain moderate gain insignificant loss insignificant loss insignificant gain significant loss small loss squirrel (see 4.2.1) is largely associated with pine woodlands and potentially could have increased climate space in Scotland. Scots pine is thought to live for about 300 years, but a study in Sweden found that the climate‐related mortality in Scots pine occurred mainly in the early stages (first 20 years) of a tree’s life cycle (Persson & Ståhl., 1990). The trees in the later stages, however, are mostly not immediately affected by changes in climatic conditions. Thus, it seems unlikely that its range will be markedly reduced in the next 100 years or so, despite the projected losses of their climate space. Ray (2008) also noted that the persistence of Scots pine is unlikely to be affected by climate change. However, Scots pine has been shown to be vulnerable to extreme water deficits in drought conditions (Lebourgeois et al., 2010) which may impact lowland populations more. Indirect impacts of climate change on Scots pine include increased risks from pests and diseases due to warming. Already, a fungal disease, red needle blight, which infects a wide range of pine species, has increased in the UK since the late 1990s, with the first outbreaks occurring in Scotland in 2002 (Brown et al., 2003). In addition, increases in winter temperatures have led to the northward spread of the pine processionary moth (Thaumetopoea pityocampa) in Italy (Battisti et al., 2005). At present, sessile oak does not occur frequently in pine woodland. However, as the climate becomes less favourable for Scots pine and more favoured for sessile oaks, the frequency of sessile oak stands may increase. With sessile oak increasing, the associated species including fungi, mycorrhizae, mosses, lichens and invertebrates may also increase. Alder and rowan may increase too as their climate space remains in the future. An increase in the dominance of broadleaved trees at the expense of Scots pine will affect some of the characteristic species in pine woodlands, such as Scottish crossbills and wood ants, as these species are strongly associated with Scots pine. 28
Colonisation in Scots pine communities would depend also on various factors other than climate, including soil types, land cover, and grazing practices. For example, if land‐use and grazing pressure allow, scrub species may shift above the current treeline and broaden the upper edge of pinewoods (Ray, 2008). Juniper and montane willows may also shift to higher elevations where soil conditions allow (Ray, 2008). While the projected changes in temperatures and rainfall are likely to bring a negative impact on Scots pine, enhanced carbon dioxide concentrations are likely to have a positive effect on the height, diameter growth, and needle biomass (Broadmeadow & Jackson, 2000). The increase in leaf and needle production will increase shading on the forest floor, which will then affect the species intolerant of shading. For example, heather may disappear from areas within the pinewood where light intensity falls below 40% of that under open conditions (Gimingham, 1960), although its suitable climate space is projected to remain. Growth of wavy hair grass (Deschampsia flexousa) may also be limited by shade. Thus, species composition may change through losses of the shrubs and grasses together with their herbivores and this will in turn lead to the changes in ecosystem functioning. Disturbance events are likely to become more frequent under climate change. While fires can kill much of the vegetation, they can often aid in the regeneration of Scots pine (Rodwell, 1991). Silver birch may also regenerate well following a burn, as the open competition free conditions produced are ideal for seedling establishment and growth (Atkinson, 1992). Thus, depending on the regeneration rates, species composition may be affected. Grazing, particularly by deer, has been identified as the major factor limiting the regeneration of the native pine woodlands (Palmer & Truscott, 2003). Deer populations are adversely affected by cold, wet weather, and thus the population densities and ranges are likely to increase due to climate change (Stokes & Kerr, 2009). Therefore, unless appropriate management actions are taken, climate change is likely to increase grazing pressures on pine woodland. 4.2.12. Lowland Mixed Deciduous Woodland Lowland Mixed Deciduous Woodland corresponds with three NVC habitats: W8 (Fraxinus excelsior‐Acer campestre‐Mercurialis perennis woodland), which makes up the bulk of the priority habitat, W10 (Quercus robur‐Pteridium aquilinum‐Rubus fruticosus woodland) and W16 (Quercus spp.‐Betula spp.‐Deschampsia flexuosa woodland) (Rodwell, 1991). Clearly then, this habitat is found on a range of soil conditions and is found throughout the lowland areas of England, Wales, Scotland and Ireland (Maddock, 2010). This woodland tends to be of great value for conservation because many of the remaining examples of it are considered ancient semi‐natural woodlands. Table 4.8 highlights the main plant species responses. 29
Table 4.8: Modelling results for lowland mixed deciduous woodland species Trees Botanical name Changes in climate space English elm Ulmus glabra moderate loss Sessile oak Quercus petraea insignificant loss Ash Fraxinus excelsior insignificant loss European hornbeam Carpinus betulus insignificant loss Aspen Populus tremula insignificant loss Common oak Quercus robur insignificant gain Small‐leaved lime Tilia cordata small gain Field Maple Acer campestre moderate gain Wild pear Pyrus pyraster moderate gain Large leaved lime Tilia platyphyllos very significant gain Service Tree Sorbus domestica very significant gain Wild Service Tree Sorbus torminalis very significant gain Shrubs Hazel Hawthorn Corylus avellana Crataegus monogyna insignificant loss insignificant gain Ground flora Honeysuckle Tufted hair grass Killarney fern Bracken Wood anemone Bluebell Lonicera periclymenum Deschampsia cespitosa Trichomanes speciosum Pteridium aquilinum Anemone nemorosa Hyacinthoides non‐scripta insignificant loss insignificant loss insignificant loss little change little change little change 138 modelled species are associated with this habitat, not surprising given its ubiquitous ‘catch‐all’ status and distribution throughout the British Isles. All the main tree species are represented and many typical shrub and herb layer species are included too. The results of the model could have some interesting implications for the future of lowland woods in the British Isles. In particular, the potential large increase of large‐leaved lime (and to a lesser extent small‐leaved lime) could change the make‐up of the canopy in woods where these species are found. However, as they are both relatively scarce species, this would require human intervention to increase their range significantly. Other notable increases include true service tree (Sorbus domestica) and wild service tree (S. torminalis), both very rare trees. Field maple, which is at its northern limit in northern England, also could increase, as could wild pear (Pyrus pyraster). The most notable loser in this habitat is wych elm (which is already becoming rarer due to Dutch elm disease). In England, hornbeam, sycamore, birch, ash, aspen and common oak all lose space; again, this highlights the possibility of a localised switch in the dominant canopy species from oak, ash and sycamore (and occasionally hornbeam) to the lime species where they are already present or where human intervention is planned. 30
4.2.13. Upland Birchwoods Upland Birchwoods are primarily found in Scotland and are, not surprisingly, dominated by both birch species, as well as less frequent numbers of willow, aspen and even juniper (Maddock, 2010). Because of the fast‐growing and pioneer nature of this woodland type it is quite dynamic in terms of its boundaries and location. The soil type is usually acidic, but it can occur on more base‐
rich soils. Correspondence to the NVC woods include: W11 (Quercus petraea‐Betula pubescens‐
Oxalis acetosella woodland), W17 (Quercus petraea‐Betula pubescens‐Dicranum majus woodland) and in wetter conditions W4 (Betula pubescens‐Molinia caerulea woodland). 79 species in this habitat are covered by the modelling data, including all the major tree species, but also many of the important and commoner shrub and ground flora species (Table 4.9). This habitat is commonly found in Scotland, although the outlook for it there is not very encouraging. Both birches lose bioclimatic space (silver ‐19% and downy ‐9%) as do other important tree species (rowan ‐ 4%; grey willow ‐4% and in the 2080 scenarios wych elm too). However, both oak species increase bioclimatic space and although neither are pioneer species like birch, this habitat may well lose out to Upland Oakwoods. Table 4.9: Modelling results for upland birch woodland species Trees Botanical name Silver birch Betula pendula Downy birch Betula pubescens Common oak Quercus robur Sessile oak Quercus petraea Scots elm Ulmus glabra Rowan Sorbus acuparia Goat willow Salix caprea Shrubs Cowberry Blackberry Heather Vaccinium vitis‐idaea Rubus fruticosus Calluna vulgaris Ground flora Wood sorrel Oxalis acetosella Purple moor grass Molinia caerula Hare's‐tail cotton grass Eriophorum vaginatum *Projected changes in suitable climate space in Scotland Changes in climate space small loss insignificant loss moderate gain moderate gain insignificant loss insignificant loss insignificant loss moderate loss insignificant loss moderate gain insignificant loss insignificant loss insignificant gain 4.3. Analysis of results in relation to the National Vegetation Classification (NVC) 4.3.1. The background and uses of the NVC The National Vegetation Classification (for Woodlands and Scrub) is a phytosociological description of twenty‐five woodland and scrub communities found in Britain. It follows the largely continental European school of plant community taxonomy (e.g., Braun‐Blanquet et al., 1932; Ellenberg, 1963) 31
in that it is the product of statistical classification (primarily TWINSPAN) on plant species associations which does not make allowance of other factors such as management (compare, for example, Rackham, 1980; Peterken, 1981). However, it was not the first British woodland classification to be created in this manner (Tansley, 1965; Bunce, 1982), but it remains today as a classification widely used by both conservationists and ecologists. It offers a different approach to the Priority Habitat classification in that it is more refined and less generalised (for example, Lowland Mixed Deciduous Woodland is covered by a multitude of NVC communities). Using the NVC for analysing climate change effects on woodland communities offers some advantages over the Priority Habitats; for example, will any given NVC habitat retain the same classification and nomenclature over the next 50 or 80 years? This is less likely than the Priority Habitats perhaps (as they are so broad) and poses some interesting questions for ecologists as well as conservationists. The NVC, as a (fairly) easy classification to use, also allows woodland managers an opportunity perhaps to gauge community change over the coming years more easily than using the Priority Habitat system ‐ this, at least offers more scope for improving adaptation options (see section 5.5). Here we will look at two fairly common and species‐rich NVC communities. 4.3.2. NVC W8 Fraxinus excelsior‐Mercurialis perennis‐Acer campestre woodland This lowland ash‐dominated woodland is fairly ubiquitous in Britain (in fact one of the sub‐
communities can be found on higher slopes too), although it is more of a rarity on the continent (possibly as foresters prefer to encourage oak for timber) (Rodwell & Dring, 2001). A comparison with the results from the Lowland Mixed Deciduous Woodland Priority Habitat allows us to pick out the W8 species from the W10 (although in reality W8 and W10 share many species, but differ in the dominance of the main canopy species, not surprisingly ash and oak). Although W8 is probably the ‘climax’ woodland on their very base‐rich soils where rainfall is less than 1000mm (out‐competing even oak) it requires a certain amount of soil moisture to grow comfortably (Rodwell, 1991). In situations under future drier and warmer conditions the dominance of ash may begin to be threatened by common oak which has a greater tolerance of drought. It may even see a return to the prehistoric patterns of small‐leaf and large‐leaf lime dominance on these soil types where they are still present (e.g., Wye valley and the White Peak (Pigott, 1969; Peterken & Jones, 1989a); if warmer and drier summers become more prevalent then an increase in lime is a distinct possibility (Pigott & Huntley, 1978, 1980, 1981), especially if a more planned recovery/re‐
introduction programme is adopted. Certainly the modelling results suggest that the lime species are capable of doing very well under future climate change and whilst other species also do well (the service trees, wild pear and field maple), the limes are the only two, other than oak, that are major canopy species. However, without human intervention, it is perhaps more likely that oak will be a more common successor to ash‐dominated woodlands (except in the really base‐rich soils) if only because oak is very common and the limes, although widespread, are not very abundant (heavy browsing from 32
deer would also reduce the chances of limes spreading). On, the more northerly facing and damper sites though ash is likely to hold on and maintain the W8 community composition for longer. 4.3.3. NVC W10 Quercus robur‐Pteridium aquilinum‐Rubus fruticosus woodland The NVC W10 oak woodlands are found on slightly more acidic soils to the W8 community although they share many species. Their edaphic preferences lie in between the base‐rich soils and the truly acidic podzols that W16 and W17 prefer, in this regard it is not surprising that they can be very species‐rich. The relative climate change tolerance of both oak species suggests that many W10 woods will remain fairly intact in terms of their dominant canopy species; however, like W8, the lime species may prove to be more successful colonisers if encouraged by human intervantion. Similarly, on the more acidic sites with W10, sweet chestnut (Castanea sativa) could compete more with oak and start to replace it. In the north, where oak still outcompetes beech (and hornbeam) there may be a gradual shift in dominance to beech. This is already happening but many managers of W10 ancient semi‐natural woods in the north actively control beech. In the wetter parts of Scotland, W10 may also succeed W11 (Quercus petraea‐Betula pubescens‐Oxalis acetosella) as the climate becomes drier. 4.4. Impacts of climate change on rare and charismatic species 4.4.1.
Charismatic species The charismatic species showed a mixed response to projected changes in potential suitable climate space (Table 4.10), which is similar to those found in other studies, such as MONARCH (Harrison et al., 2001; Berry et al., 2005; Walmsley et al., 2007). Table 4.10: Changes in potential suitable climate space for charismatic species Non‐plant taxa Scientific name Change in climate space Great crested newt Triturus cristatus Decrease Capercaillie Tetrao urogallus Decrease Otter Lutra lutra Little change Bluebell Hyacinthoides non‐scripta Little change Nightjar Caprimulgus europaeus Increase Red squirrel Sciurus vulgaris Increase Beech Fagus sylvatica Increase Wild Service Tree Sorbus torminalis Increase Yew Taxus baccata Increase Dormouse Muscardinus avellanarius Mixed Plant taxa Juniper Juniperus communis Decrease Scots pine Pinus sylvestris Decrease 4.4.2. Species showing little change Both the species showing little change are currently widespread, although they could be affected by other pressures, which at the moment do not appear sufficiently serious to affect the species' future. For example, otter (Lutra lutra) was a widespread species, and was lost from many 33
catchments in the 1950s‐1970s, but it has been recovering and spreading into southern and northern English rivers as water quality improves (Crawford, 2003). Climate change appears to have little direct impact via climate space, so providing water quality continues to improve and remains suitable, despite possible climate change related decrease in flows, this species should not be affected. For bluebell (Hyacinthoides non‐scripta), the spread of alien bluebells (hybrids or ‘Spanish’) is a possible future threat, although a study in south‐central Scotland showed that currently increasing rainfall was associated with increasing native and decreasing alien densities and that the presence of aliens was more related to human factors (Kohn et al., 2009). 4.4.3.
Species showing potential increases in climate space Of the five showing a potential increase, nightjar (Caprimulgus europaeus) is not projected to have current suitable climate space, despite it being widely recorded in the UK, although it is not so frequent in Scotland. Thus, while there is potential new climate space, particularly in Scotland and Ireland, much of the apparent model increase is a function of the model not projecting current suitable climate space well. The same is true for red squirrel and yew. Red squirrel, for example, was recorded from almost all squares in Great Britain, apart from the north of Scotland and was infrequently recorded in Ireland. Nowadays it is no longer found in many of these due to competition with the introduced grey squirrel (Sciurus carolinensis) (Gurnell & Pepper, 1993). Grey squirrel also carry parapox virus, which is highly pathogenic to red squirrels and studies in the UK have shown that 61% of apparently healthy grey squirrels have been exposed to the virus and may be carriers (McInnes, 2006). When the virus is present, the grey squirrel can replace the red squirrel 20 times faster than normal replacement rate (Rushton et al., 2006) and thus is a potentially more significant and immediate threat than climate change. The other main threats to red squirrel are habitat loss and fragmentation, but these are not considered to pose a major risk at present (Shar et al., 2008). Beech is projected to increase, particularly in Scotland and Northern Ireland and a part of this is a function of the model being trained on the “native range”, rather than its planted or naturalised range (Berry et al., 2002). Wild service tree is found primarily in the southern and central parts of England and the lower areas of Wales and there is a large increase potential increase of suitable climate space in Scotland and Ireland. The other tree species are discussed in more detail under the Priority Habitats with which they are associated. Nightjar has declined in range by 52% between 1968‐72 and 1992, although there has been a partial recovery due to improved forest management and increased felling. The decline is thought to be due to loss of heathland habitat area and condition, although it is possible that a decrease in food supply arising from changing agricultural practices and/or climate change also may have affected population numbers (from the Species Action Plan). In a study of the effect of urban areas and human disturbance on nightjar numbers, it was shown that the presence of woodland within 500m of the heathlands had a positive effect (Liley & Clarke, 2003). 34
4.4.4.
Species showing potential decreases in climate space The potential for a decline in great crested newt is supported by a 12 year study of four pond systems near Canterbury, Kent, which found that variations in inter‐annual survival was inversely related to winter temperatures and winter rainfall, while asynchrony in population dynamics was probably a function of recruitment and juvenile dispersal (Griffiths et al., 2010). A study characterising the fundamental and realised niche of capercaillie in Europe found that a low degree of canopy density (50‐60%) and an extensive layer of dwarf shrubs (80‐100%) were essential habitat parameters (Scherzinger, 2009). It was suggested that these can be achieved by appropriate management when logging. The possible causes of declines in capercaillie in Scotland since the 1970s have been reviewed by Moss (1994) and include low breeding success, predation, and habitat destruction through changed silvicultural practices, climate change and collision with fences (Baines & Summers, 1997; Baines & Andrew, 2003). Moss et al. (2000) suggested that the main causes of decline were the low breeding success, exacerbated by deaths of fully grown birds flying into forest fences. Wet summers and cool springs also have been associated with poor chick survival (Moss et al., 2001) and data from Speyside shows a recent tendency for June rainfall to increase (Summers et al., 2004). Capercaillie is projected to lose all suitable climate space under some scenarios by the 2050s or 2080s, although the above studies suggest that warmer, drier summers should favour the species. The key species with which it is associated: Scots pine and bilberry (Vaccinium myrtillus) do not show significant losses (Berry et al., 2005) and thus if its habitat can be maintained and the other pressures affecting its survival can be addressed, climate change could have a less severe impact on this species. Scots pine and juniper are discussed in association with pine woodland and both could decline in range under climate change. Many stands of juniper are isolated in the present‐day British landscape, and this fragmentation and loss of juniper populations can result from a variety of causes, such as shading from planted or developing secondary woodland, land use changes (e.g. Ward and King, 2006), aging juniper populations (Ward, 1982), or changes in site conditions (Broome et al., 2008). Regeneration of juniper is now a rare event (e.g. Ward, 1973; Clifton et al., 1997; Sullivan,2003) and this failure to regenerate may stem from a variety of causes linked to changes in land management practices, including increased herbivore and rabbit pressure (Fitter and Jennings, 1975; Gilbert, 1980; Ward, 1982; Clifton et al., 1997; Ward and King, 2006) and possibly limited seed viability (Garcia et al., 2000; Verheyen et al., 2005; Verheyen et al., 2009). This indicates a potential for a wider distribution if woodland was re‐created, pressures reduced and climate remained suitable. 35
4.4.5.
Species showing a mixed response Dormouse (Muscardinus avellanarius) is primarily found only in England and it has become extinct from seven counties. The main threats are changes in woodland management practice, particularly the cessation of hazel coppicing and stock incursion into woodland, as well as woodland fragmentation, since short distances, possibly as little as 100m, form absolute barriers to dispersal, unless arboreal routes are available (Species Action Plan). Dormouse shows a mixed response, with a possible decline under both the HadCM3 scenarios, but an increase under the PCM scenarios, thus suggesting the climate space of this species is quite sensitive to climate. A number of the charismatic species, therefore, have the potential to maintain or increase their suitable climate space and thus continue to contribute to people's enjoyment of the woodlands in which they are found. Other potential threats, however, have been identified and it is important that these are monitored and/or action taken to reduce them so that the species are better placed to cope with the pressure of climate change (Section 5). 4.4.6. Rare Species The rare species also show a mixed response to climate change, with the greatest proportion showing a loss of suitable climate space (Table 4.11). A number of the findings for the rare species Table 4.11: Changes in potential suitable climate space for rare species in the UK Change in climate space Species Scottish wood ant Formica aquilonia Narrow‐headed ant Formica exsecta Hairy wood ant Formica lugubris Southern wood ant Formica rufa Mining bee Andrena ferox, Scottish crossbill Loxia scotica Spotted flycatcher Muscicapa striata Bullfinch Pyrrhula pyrrhula Black grouse Tetrao tetrix, Song thrush Turdus philomelos Brown hare Lepus europaeus, Linnet Carduelis cannabina Wryneck Jynx torquilla Tree sparrow Passer montanus Pipistrelle bat Pipistrellus pipistrellus Lady`s slipper orchid Cypripedium calceolus Pool frog Rana lessonae Stag beetle Lucanus cervus Barbastelle bat Barbastella barbastellus Bechstein’s bat Myotis bechsteinii Greater horseshoe bat Rhinolophus ferrumequinum Lesser horseshoe bat Rhinolophus hipposideros Pearl‐bordered fritillary Boloria euphrosyne (increase then decrease) Twinflower Linnaea borealis 36
Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Little change Little change Little change Little change Little change Increase Increase Increase Increase Increase Increase Mixed Decrease need to be treated with caution, as often models have greater difficulty in accurately projecting the species' current suitable climate space, particularly if there are few recorded presences. Also if these presences are widely spaced then it can over‐project the current and future climate space, with large parts of the UK being apparently climatically suitable, when the species occurrence is infrequent and/or in restricted locations. An extreme example is the warty wax lichen (Thelenella modesta), for which there are only eight records on the NBN website at sites widely distributed across Britain and Ireland and the model shows most of this area to be suitable climate space. The SAP 2 says that there is only one remaining population which is stable for as long as its host tree lives, although there are apparently suitable adjacent trees which have not been colonised. A similar pattern is seen for some other lichens, such as Biatoridium monasteriense. The model, therefore, is apparently over‐projecting suitable climate space, although this may not be the case where there are particular environmental or historical factors that are severely constraining the species' distribution in the UK. Another difficulty encountered with modelling rare species is that if they are at the edge of their range in the UK and/or have a very concentrated distribution, the model may not simulate any suitable climate space for the species. This is the case for the stump lichen (Cladonia botrytes) where the European distribution does not include its UK distribution and although there is suitable climate space in the future it is in the west, not the east of Scotland where it is currently recorded. A similar problem was encountered for the charismatic species, nightjar. These modelling difficulties are discussed further in the MONARCH 3 report (Berry et al., 2007) and those species for which the modelling outcomes are not sufficiently robust in terms of their relationship to the species recorded distribution are not discussed further here. All the rare species had either an excellent or very good agreement between their observed and projected European distribution (see Section 3.2.3), while those for spotted flycatcher, Barbastelle bat and Bechstein’s bat were classified as good. This suggests that the model outputs are robust enough for further analysis of the potential future climate space distribution for these species. 4.4.7. Species with little change in suitable climate space Those showing little change are species for which nearly the whole of the UK is projected as suitable climate space, although in the case of the pipistrelle bat (Pipistrellus pipistrellus) there are only a few records in Ireland and much of the currently suitable climate space has yet to be colonised. Wryneck (Jynx torquilla) has a scattered distribution throughout Great Britain, with no records in Ireland and for both there is little change in the availability of future climate space. Climate change, especially increased rain in the breeding season is thought to be one of the reasons for the long‐term decline in the numbers and range of wryneck (BirdLife International, 2011) and drier springs could help breeding success, but other factors, such as agricultural improvement, loss of habitat and herbicide and pesticide use are also thought to be responsible (del Hoyo et al., 2002). For all the species, factors such as changing agricultural practices affecting 2 http://www.ukbap‐reporting.org.uk/plans/national_plan.asp?SAP={37EE0A07‐1586‐4CB3‐8C28‐C8F9D7672474}. Accessed 01/03/11 37
food availability at critical times of the year will be important factors affecting their future, while pipistrelle bats could also be affected by the loss of winter roosting/hibernation sites in buildings and old trees. Lady’s slipper orchid used to be more widespread in northern England and currently is only found naturally at one site and it has been planted in two others. It is one of the species for which the model projects no current and little future suitable climate space in the UK and the latter has a low probability of occurrence. 4.4.8. Species with increased suitable climate space Pool frog only has very scattered records in England and has been re‐introduced in some of these locations. While it has a great spread of potential future climate space it is unlikely to fulfil it, partly due to a lack of suitable habitats to aid dispersal and colonisation, although longer hotter summers could help the tadpoles to metamorphose in September. The projected increase in suitable climate space for the Stag beetle (Lucanus cervus) across much of Britain and Ireland under the high scenarios represents much of its former range. This is a thermophilous species, with the mean average temperature in Belgium being largely found within the 16.5oC mean July isotherm (Hawes, 2004), although some authors suggest that its distribution is limited by the amount of rainfall (Percy et al., 2000; Pratt, 2000). Also. it requires dead wood, possibly with a preference for oak. This could mean a reduction in suitable future habitat in southern England, although it does lose suitable climate space in some of this area under a 2080s high scenario. All the bat species have declined over the last 50 years, as a result of factors such as loss of pasture, wetlands and hedgerows that provide insect‐rich feeding areas and flyways, loss or disturbance of roosting and hibernation sites, and for greater horseshoe bat population declines during the 1960s and 1980s have been attributed to unsuitable weather conditions. They all show a projected expansion northwards and in some cases eastwards in Britain and Ireland. Bechstein’s bat is not observed in Ireland, but suitable climate space occurs here and there is a question as to whether it is likely to fulfil any of this potential range. The bats have varying mean flying ranges although individuals are known to have flown much further, but they should be able to disperse into new suitable climate space providing that a sufficient supply of insects and roosting and hibernation sites are available. Climate change could benefit the greater horseshoe bat, where it is known that a minimum mean temperature of 10oC in April and May is crucial for the timing of births and population numbers (Walmsley et al., 2007). 4.4.9.
Species with decreased suitable climate space The Scottish wood, narrow‐headed, hairy wood and southern wood ants are all projected to lose suitable climate space, with the narrow‐headed ant being the most severely affected, with a complete loss projected under the high emission scenario by the HadCM3 climate model. Also, the narrow‐headed ant has very poor dispersal capabilities and it is unlikely that it can adapt or disperse adequately in the face of rapid environmental change (Hughes, 2006). There is no new 38
climate space projected, but some overlap between its current and future climate space would remain in Scandinavia. Wood ants have been considered as a ‘keystone’ species in the forest ecosystem (Fowler & Macgarvin, 1985), as they have a vital functional role through influencing the distribution and abundance of many invertebrate species, dispersing seeds, preying on invertebrates that affect tree health and growth, contributing to nutrient cycling, providing microhabitats and a food source for predators, including the charismatic capercaillie (Hughes & Broome, 2007). Both the Scottish wood ant and hairy wood ant are associated with areas of semi‐natural woodland, especially pine and birch. Scottish wood ant is more tolerant of shading than hairy wood ant and its nests tend to be in more sheltered spots, while hairy wood ant prefers more open situations, as it needs more sunshine to raise solaria temperatures high enough for brood development (Willet, 2001). If any changes occur to the species that provide the canopy cover, wood ants can also be affected, but there is not enough evidence to know how the relative abundance of the two might be affected. However, in an investigation into wood ants in southern Finland it was found that the hairy wood ant was more common in young forests and in small old‐
forest fragments than expected by chance (Punttila, 1996). The main threats to these ant species is shading due to lack of management or unsympathetic clear felling that may destroy nests and lead to a loss of food sources (Hughes & Broome, 2007). Little is known about the mining bee (Andrena ferox), but it is only found in a few sites in southern England, thus its current climate space is far wider than its recorded distribution. It is only projected to have slight decreases in climate space although these are particularly in Scotland where it is not currently found. The mining bee is thought to be primarily associated with oak; with populations being possibly affected by the availability of a succession of oak trees flowering, such as would occur in a natural rather than even aged stand (Edwards, 2008). The mining bee also could be adversely affected by the potential decline in oak in southern England, although it has been observed collecting pollen from hawthorn and field maple (Edwards, 2008). Therefore, while it is not likely to be directly affected by climate change, it could be indirectly affected through declines in its prime pollen source. The projected suitable climate space for Scottish crossbill declines moderately by 2080. Using a different modelling technique (i.e. response‐surface approach), but with the same HadCM3 climate model, Huntley et al. (2008) also found a decrease in the suitable climate space, but to a greater extent, such that climate space will completely be lost by 2080 under either a A2 or B2 scenario. The Scottish crossbill is confined to northern and eastern Highlands of Scotland (Summers et al., 2002). It feeds on conifer seeds, and while it is also found in conifer plantations, a high proportion of the species is associated with Scots pine woodlands, particularly the old‐growth forests 39
(Summers et al., 2002; Summers, 2007). While Scots pine is unlikely to be lost within the next 100 years or so (see native pine woodland), phenological changes may occur, such as changes in the timing and availability of seeds and the size of cones, which may affect its breeding phenology (Benkman, 1987, 1990; Summers & Proctor, 1999). The degree of threat to the Scottish crossbill also depends on whether populations that currently reside in Scots pine woodlands can adapt to plantations of non‐native species, which is currently unknown. The spotted flycatcher (Muscicapa striata) is a summer migrant bird that has shown no change in its arrival date in recent years (Sparks et al., 2007), but has shown a more than 50% decrease in populations in England between 1967–2006 (Thaxter et al., 2010). The causes of this decline are not known, but the Species Action Plan suggests that weather in the UK or drought in the Sahel, as well as loss of nest sites and decreased invertebrate availability in summer may contribute. Increased summer temperatures and sunshine may have positive effects. The main loss of climate space is projected to be in Wales and Ireland. While the bullfinch (Pyrrhula pyrrhula) is widespread in the UK, the Common Bird Census has indicated a 75% decline on farmland and 47% in woodland between 1968 and 1991. It is possible that the loss of nesting habitat through the removal of farmland trees and hedgerows with an associated loss of food sources (buds, seeds and fruits) through herbicide usage, along with a loss of winter food sources, such as winter stubble fields, have contributed. The loss of climate space is particularly severe in England and appropriate farmland management will be important in increasing population numbers. Historically black grouse used to be much more widespread, being common in Britain, but absent from Ireland, thus the current climate space does not match its former distribution. Between 1994/95 and 2005 populations have declined by 22% (Sim et al., 2008), although there has been recovery in some areas in North Wales and the Pennines as a consequence of favourable habitat management. Agricultural intensification, moorland drainage, and increased grazing levels are possible causes of decline, especially in the Pennines, while commercial forest maturation, along with collision with fences are likely causes in the highlands of Scotland. Potential simulated climate space is projected to be lost from almost the whole of Britain by the 2080s under the HadCM3 A2 scenario, although the Scottish Highlands remain suitable under the B1 scenario. Curculionidae, which are thought to be an important part of their diet are predicted not to be particularly vulnerable to climate change (Pearce‐Higgins, 2010). The song thrush has declined since about 1970 and it appears to be particularly affected by long frost periods and by drought (Robinson et al., 2007), which may affect its foraging for earthworms in damp ground. The loss of grassland from eastern England, loss of hedges and scrub and the degradation of woodland may be contributory factors (Peach et al., 2004). The projected loss of climate space is particularly focused on southern England and this, combined with potentially 40
negative effects of climate change on its food supply in this area, poses a threat to this species, however, a decline of frost periods could enhance fledgling success. The brown hare (Lepus europaeus) has been declining in many areas and a study examining population densities in connection with habitat found that in 12 European countries it is primarily associated with farmland, with intensification of agriculture as the major cause of its decline (Smith et al., 2005). While is a highly adaptable species it has a preference for weeds and wild grasses when available and intensified agro‐practices have reduced this food source resulting in the selection of crop species (Reichlin et al., 2006). By 2080, a significant amount of climate space is lost in England, south of a line from the Mersey and the Humber. Twinflower is confined to north‐east Scotland and occurs in highly isolated patches (Scobie & Wilcock, 2009). It is projected to significantly lose suitable climate space by 2050 and may lose it completely by 2080. Given that the current distribution is more restricted than its current climate space, the loss could have detrimental effects on the species. However, it is not known to be critical to ecosystem functioning or to provide a direct ecosystem service, other than perhaps a cultural one. Thus for many of these rare species, climate change is only one of a number of pressures that could affect them in the future and, in many cases of species losing climate space the two act together to enhance the risk of population and/or range loss. Reversing habitat loss and inappropriate land management practices are two key options for addressing the impacts of climate change on these species, although the exact requirements will depend on the ecology of the species. 4.5. Potential new species If no native tree species are suitable replacements in woodlands, then non‐natives might be another option, but this suggestion is likely to be very controversial. Some continental European tree species have similar ecological niches to some tree species potentially threatened by climate change in Britain and could be substitutes, for example downy oak (a species that gains a lot of space in Britain in the model) for common oak; and some have similar decay processes too e.g. sweet chestnut and common oak. Looking further afield, the eastern oriental beech is known to be more drought tolerant than common beech (Wilson et al., 2008). Other potential replacement species include narrow‐leaf ash (Fraxinus angustifolia), which also gains a lot of climate space in England and Wales, for common ash; and by 2080 Manna ash (F. ornus) may even by a suitable replacement. However, much more comparative work needs to be done to establish which species will provide similar niche opportunities for specialists and which, if any, tree would offer a suitable replacement for drought affected beech, before consideration of replacement tree species is a realistic option. There are also issues relating to plant hygiene and, in addition, a fundamental shift in thought would be 41
needed to allow the introduction of exotic species onto sites of conservation importance where the current management strategy is to maintain the current native species composition. Guidance on dealing with the changing distribution of tree species in England (Natural England, 2009) addresses controversial ‘possible’ native species, such as sycamore, and puts in context the value of species like sweet chestnut and pines, but the clear presumption is that more ‘exotic’ species are not recommended. 5. Discussion 5.1. Application of bioclimate models Bioclimate envelope models are fraught with theoretical and practical difficulties, not least of which is the actual extent to which climate governs distributions (Davis et al., 1998; Hampe, 2004). Each current species' distribution represents the realised niche of that species but future re‐
distributions under climate change scenarios provide a fundamental niche (i.e., it does not take into account biotic and other factors). At smaller scales other determinants affect species' distribution including biotic factors like competition, facilitation and mutualism; but also intra‐
population factors like phenotypic plasticity; geographic factors like soil type, land‐use, human management, fire and fragmentation. However, whether these factors exert any control over macro‐scales is still disputed (Hampe, 2004; Araújo & Luoto, 2007). One study has shown that European plant and bird distributions are closer to ‘climatic equilibrium’ than reptiles and amphibians which has obvious implications for modelling performance in cross‐taxa studies for conservation (Araújo & Pearson, 2005). However, other research has shown that in a study of 55 European tree species, the majority fill less than 50% of their potential climatic range (using a bioclimatic model and Atlas Flora Europaeae data); they attributed this to historical factors such as poor dispersal after the last glacial period (Svenning & Skov, 2004). The issue of equilibrium also highlights problems that can occur with sampling distribution data ‐ a number of studies have shown that model performance is reduced with incomplete coverage of all climatic zones in a species' distribution (Stockwell & Peterson, 2002; Kadmon et al., 2003; Thuiller et al., 2004a). Other studies have shown that where species have limited occurrence data available then the type of modelling method can affect the performance considerably too (Hernandez et al., 2006; Wisz et al., 2008). To date, bioclimatic models have used a number of different statistical techniques including various regressions (GLMs, GAMs, multiple logistic regression), classification tree analyses, artificial neural networks and maximum entropy methods amongst others (Heikkinen et al., 2006). A number of studies have compared these techniques although within the context of climate change these have been comparatively few (Thuiller et al., 2003; Araújo et al., 2005; Pearson et al., 2006). Results from these analyses vary although in general the more complex techniques (GAMs and ANNs) have been more successful (Heikkinen et al., 2006), but it is also worth noting that model performance also varies with the quality and selection of raw data. ANNs have been shown to have a number of advantages over other approaches (Heikkinen et al., 2006): they are robust to noise (e.g., errors in distribution data)(Hilbert & Ostendorf, 2001); they 42
have the ability to cope with different types of predictor variables such as categorical, numerical or boolean (Pearson et al., 2002); and, they can work with distributions that have non‐linear responses to variables (Hilbert & Ostendorf, 2001). The last advantage is particularly useful for modelling species with non‐contiguous or patchy distributions and ANNs have been used successfully in recent years for species distribution modelling (Guisan & Zimmermann, 2000; Hilbert & Ostendorf, 2001; Williams et al., 2009). So, given that these models have a number of caveats limiting their utility, do they have any use in modern predictive ecology? They do at least offer a quick and (relatively easy) assessment of climate change impacts for large numbers of species, particularly if there are few data on a species' physiology and ecology. There is, though, plenty of room for improvement ‐ general calls for improving data quality, processing and evaluation abound (Guisan & Thuiller, 2005; Araújo & Guisan, 2006); in fact the limitations of bioclimatic models are well known and development addressing any of these issues is a step forward (and indeed many research groups are doing just this). Perhaps some issues stand out more than others though, and given the complexity of community dynamics (Ferrier & Guisan, 2006), some are perhaps more attainable. For example, Thuiller et al (2008) suggest progression in incorporating species migration as well as a better understanding of species' trailing edge dynamics are imperatives. One other possible improvement would be to incorporate more realistic community assembly processes, an approach that would perhaps be best achieved by adopting species' traits (Thuiller et al., 2004b; Guisan et al., 2006). 5.2. Comparison with Ecological Site Classification results The potential impacts of climate change on timber tree species have been assessed by the Forestry Commission using the Ecological Site Classification (ESC) (Ray, 2001; Broadmeadow & Ray, 2005; Ray, 2008; Broadmeadow et al., 2009b; Ray et al., 2010). The ESC models indicate suitability of tree species to site conditions where suitability is defined in terms of growth relative to maximum growth rates achieved in the UK (Broadmeadow et al., 2009b). They use six biophysical factors, including two soil factors, fertility and moisture availability, and four climatic factors, accumulated temperature, moisture deficit, wind exposure, and continentality (distance from the sea). ESC first calculates the potential yield of a given species at the site as a function of accumulated temperature, subsequently modified by the most limiting of the remaining five biophysical factors (Ray, 2001). The underlying climate data represent the 1961‐90 average climate at a spatial resolution of 10 km. The future climate data are derived from UKCIP02 climate projections in 2050 and 2080 under the High and Low emissions scenarios (Hulme et al., 2002). Suitability is then calculated from the predicted yield; for example, the site is classified as ‘very suitable’ if the species achieve 75% or more of its maximum yield in Britain, ‘suitable’ if 50%‐75%, and ‘unsuitable’ if less than 50% (Ray, 2001). Like SPECIES, ESC suitability models do not takeaccount of the effects of pests and pathogens, extreme climatic events, or increasing CO2 concentrations on tree productivity. 43
While both ESC and SPECIES models project the potential changes of suitable conditions for tree species under future climate scenarios, comparison of the results are complicated partly because the suitability changes projected by ESC models are different from the climate space changes projected by SPECIES. The changes in the suitability classes in ESC represent the changes in timber productivity. It follows that the effects of reduced suitability under the future climate are likely to be seen in the form of reductions in productivity, rather than complete loss of the species from the site (Broadmeadow et al., 2009b). In addition, ESC does not take account on the species distributions and does not indicate if the species is planted at the ‘suitable’ site. Moreover, the climate scenarios used to provide future climatic conditions are different as SPECIES as used in this project utilised HadCM3 General Circulation Model (GCM) and the High and Low emissions scenarios were derived from the IPCC A2 and B1 scenarios. On the other hand, the UKCIP02 High emissions projections correspond with IPCC A1F1, while the UKCIP02 Low emissions projections correspond with IPCC B1 scenario. The climate projections based on UKCIP02 High emissions scenarios show generally greater changes than those for HadCM3 A2 scenarios, in particular, temperature increases in England, winter precipitation increases in Wales, and summer precipitation decreases in Britain (Table 5.1). (The projections for Northern Ireland were not available for ESC models and thus excluded from comparison here.) Table 5.1: Changes in temperature and precipitation for two 2080 scenarios England Wales Annual temperature change (°C) UKCIP02 2080 High 3.9 3.64 HadCM3 2080 A2 3.14 3.1 Summer precipitation change (mm/month) UKCIP02 2080 High ‐28.06 ‐38.16 HadCM3 2080 A2 ‐22.38 ‐27.54 Winter precipitation change (mm/month) UKCIP02 2080 High 17.6 27.88 HadCM3 2080 A2 17.8 19.92 Scotland 2.95 2.76 ‐22.6 ‐12.61 19.54 15.42 (Adapted from Berry et al., 2007) The ESC projections for beech under Low and High scenarios in 2050 and 2080 (Figure 2) showed that, under all but the 2080 High emissions scenario, the suitability in east and west Wales, northern England, and eastern and western Scotland are projected to increase (Broadmeadow et al., 2009b). In contrast, the suitability in eastern England declines to the extent that under the 2080s High emissions scenario, there is virtually no area deemed ‘suitable’ for beech in the region. The model thus projects the likelihood of a significant regional shift in the occurrence of beech as a productive forest species. 44
The SPECIES projections also suggest possible distribution shifts, indicated by climate space expanding northwards in Scotland and decreasing in parts of southern England, although much of eastern England, where the suitability projected by the ESC models is reduced, are projected to remain as suitable climate space. As beech is sensitive to droughts (Peterken & Mountford, 1996; Raftoyannis & Radoglou, 2002), stronger summer drought projections by the UKCIP02 High emissions scenarios relative to the HadCM3 A2 scenarios may have resulted in a significant reduction of suitable areas by the ESC models. Figure 2: Projections for beech by SPECIES and ESC (Forestry Commission, 2011a, b, c) under the High emission scenarios (HadCM3 A2 and UKCIP02 High respectively). 45
Figure 3: Projections for sessile oak by SPECIES and ESC (Forestry Commission, 2011b, c, a) under the High emission scenarios (HadCM3 A2 and UKCIP02 High respectively). The ESC projections for sessile oak show that under most scenarios, the areas classified as ‘suitable’ or ‘very suitable’ mostly remain so in 2080 (Figure 3). However, under the High emission scenarios in 2080, large areas in southern England and eastern Wales decline in suitability. SPECIES indicates potential losses of climate space in the southeast and west of England and gains in the north of Scotland. 46
Figure 4: Projections for Scots pine by SPECIES and ESC (Forestry Commission, 2011a, b, c) under the High emission scenarios (HadCM3 A2 and UKCIP02 High respectively). The ESC models indicate that Scots pine mostly maintains its suitability in all regions under most climate scenarios, although under the 2080 UKCIP02 High emissions scenario, it is projected to be completely unsuitable for use in western and eastern England (Figure 4). In contrast to the ESC projections that indicate timber productivity of the species in Britain, the SPECIES projections shown in this report indicate the climate space of Scots pines based on its native range. It is, therefore, mainly confined to the areas around Caledonian forests in Scotland and these areas are projected to become unsuitable by 2080 under the IPCC A2 scenario. 47
Figure 5: Projections for downy birch by SPECIES and ESC (Forestry Commission, 2011a, b, c) under the High emission scenarios (HadCM3 A2 and UKCIP02 High respectively). The suitability of downy birch, as shown by ESC (Figure 5), is reduced in large areas of England, Wales and parts of Scotland under High emission scenarios in 2050 and 2080. The SPECIES results broadly agree with the ESC projections in that the climate space is lost in large parts of southern and eastern England. 48
Figure 6: Projections for ash by SPECIES and ESC (Forestry Commission, 2011a, b, c) under the High emission scenarios (HadCM3 A2 and UKCIP02 High respectively). Under the 2080 High emissions scenario, the ESC models project that areas suitable for ash in southern England mostly become unsuitable in 2080, while some areas in southern Scotland become suitable (Figure 6). The SPECIES projections also show that the climate space in south‐
eastern England is projected become unsuitable and the climate space expands in northern Scotland, although the climate space in eastern England, where the suitability of ESC models is reduced, is projected to remain. 49
Figure 7: Projections for alder by SPECIES and ESC (Forestry Commission, 2011a, b, c) under the High emission scenarios (HadCM3 A2 and UKCIP02 High respectively). According to the ESC models, large areas classified as ‘suitable’ for alder under the baseline climate is projected to become unsuitable by 2050 under the High emission scenario (Figure 7; Ray et al., 2010). On the other hand, the SPECIES models indicate that the species could maintain its climate space by 2080 even under the HadCM3 A2 scenario. The ESC projections indicated similar patterns of changes in suitability across the six species, with relatively stronger negative changes for Scots pine and downy birch and relatively modest changes for ash. Under the High emissions scenario for 2080, suitability is significantly reduced across much of England and Wales except for the north of England and central Wales, with modest increases in the north or central Scotland. The patterns of projected changes by SPECIES were also broadly similar, with the climate space reducing in south and south‐east England and expanding in north and north‐east Scotland, with the exception of Scots pine, for which an expansion is not projected. Thus, apart from Scots pine, 50
where the natural distribution range is significantly different from its potentially suitable areas for timber production, the general patterns depicted from ESC and SPECIES models were similar. However, ESC projected larger areas with reduced suitability under the High emissions scenarios in 2080 relative to SPECIES. This may be due to the differences in the climate scenarios, but perhaps more to the differences in projected model outputs; i.e. species’ climate space vs. relative timber productivity in the Britain. For example, reductions in timber productivity do not necessarily indicate that the areas are climatically unsuitable for species’ survival. Thus, the differences in the degrees of climate change impacts on tree species between ESC and SPECIES suggest that the extrapolation of the model results require caution. 5.3. Potential impacts on woodland structure and function 5.3.1. Woodland structure Understanding individual species' response to climate change is very useful but scaling those responses up to community responses is an uphill task. The complexity of most natural communities means that it is extraordinarily difficult to predict outcomes ‐ for example, biotic interactions (competition, facilitation, herbivory, mutualisms) between species at different trophic levels are all likely to be affected by physiological, phenological and migratory responses (Aerts et al., 2006). However, evidence of community change is occurring at some well‐studied locations. One study in the US has shown a shift in vegetation types since the 1950s due mainly to a higher occurrence of severe droughts (Allen & Breshears, 1998); and evidence from Spain where many European plant species are at the limits of their southern distribution is also burgeoning (Peñuelas & Boada, 2003; Sanz‐Elorza et al., 2003; Peñuelas et al., 2007). At the other extreme there is also evidence that alpine communities are changing too (Klanderud & Birks, 2003; Walther et al., 2005). In this area of study it is also worth noting the results of experimental work on community response to climate change. Clearly, this field has practical limitations because of the difficulties of replicating climatic effects on natural systems ‐ one approach has been to use small chambers but these are rarely used on patches containing more than a few co‐existing species (de Valpine & Harte, 2001; Dunne et al., 2003; Epstein et al., 2004; Grime et al., 2008). Another limitation is that it is very difficult (and expensive) to study anything other than small forb or grass ecosystems: examples are most common for alpine and tundra ecosystems (Harte et al., 1995; de Valpine & Harte, 2001; Convey & Smith, 2006) but also calcareous grasslands (Buckland et al., 2001; Masters et al., 2001; Morecroft et al., 2004). There have, however, been a few studies on woodland ecosystems looking at the competitive balance between species but these have been limited to the tree seedling cohort due to the obvious difficulties in working with mature trees (Fotelli et al., 2005; Saxe & Kerstiens, 2005). Similar work has also been undertaken in shrubland ecosystems but also looking at seedling 51
recruitment (Lloret et al., 2009). One pattern that is emerging from these studies is the relative importance of species interactions (mainly competition and facilitation) under climatic change in determining community assembly (Brooker, 2006; Sthultz et al., 2007) although other factors may play a part too (herbivory, mutualisms) (Tylianakis et al., 2008). Current thinking emphasises the growing importance of facilitation under higher abiotic stress conditions (e.g., the rear edge) (Pugnaire & Luque, 2001; Castro et al., 2004; Lloret et al., 2005; Brooker et al., 2008). The modelling results presented in section 4 suggest that for many of the woodland priority habitats the different responses of their component species will result in changes to woodland structure, if only due to changes in the dominance of some canopy species. The dominant tree species that will be more tolerant of climate change are likely to increase their competitive edge over those with lower tolerance to drought and heat. This could lead to increases in both oak species, both lime species (where they are locally abundant), beech (in northern Britain, but declining in southern England) and possibly some non‐native species such as sweet chestnut and downy oak. However, perhaps not all these species changes will lead to massive structural changes to communities; some species may be very suitable functional matches for the species they replace (e.g., sweet chestnut and oak). However, palaeoecological data highlights the fact that woodland composition and structure has changed considerably over long time‐scales (Willis & Birks, 2006; Willis et al., 2007) and whilst current climate change is occurring at a far greater rate than in prehistory, some change to woodland structure may not necessarily be a bad thing (Millar et al., 2007). Perhaps the greatest changes to structure will occur in the south‐east, where the most extreme temperature and drought conditions are likely. In these conditions, woodlands on free‐draining soils ‐ including Lowland Mixed Deciduous and Lowland Beech and Yew Woodland ‐ are most at risk. If planned adaptation is not a considered and proactive strategy these woodlands may even struggle to maintain a dominant canopy cover, particularly if conditions are too dry even for oak. Over a series of warmer and drier years, woodlands may eventually start to die back and more tolerant shrub species (hawthorn for example) and smaller tree species (whitebeam) may become dominant (reduced) in the canopy. 5.3.2.
Woodland Function The deeper consequences of community change are beginning to be understood now and the implications for changes in ecosystem function due to climate change are worrying (Hooper & Vitousek, 1997; Lavorel & Garnier, 2002; Schroter et al., 2005; Hughes et al., 2008a). For example, changes to climate affecting phenology in species may disrupt long‐standing synchronous ecological relationships (Visser & Holleman, 2001; Sherry et al., 2007; Rich et al., 2008; Petchey et al., 2010). This has particular importance for woodlands with such a diversity of taxa and huge trophic webs present in most woodlands (Tylianakis et al., 2008). 52
Changes to forest ecosystems resulting from climatic change may have serious repercussions for a range of ecosystem functions (Fischlin et al., 2007) including forest productivity (Boisvenue & Running, 2006), carbon sequestration (Nabuurs et al., 2003; Phillips et al., 2009), soil protection which can lead to flood control and resilience (Bradshaw et al., 2007), climate regulation (Bonan, 2008), timber production (Kirilenko & Sedjo, 2007), pest regulation (Volney & Fleming, 2000; Chapin III et al., 2007) and leisure and recreation (Lacaze, 2000). The results presented here clearly highlight that woodland function will change in the future; what remains difficult to assess is what the change will be and how fast it will occur. Competitive interactions between dominant tree species are often controlled by small differences in soil type, moisture and climate (e.g., ash and sycamore ‐ Waters & Savill, 1992), so changes in climate over the next few decades are likely to bring about gradual switches in dominance in canopy species. Whether these changes will have deleterious impacts on ecosystem function is difficult to say. New research testing Grime’s (1998) mass ratio hypothesis (which postulates that the effect of species on ecosystem function is dependent on their proportional abundance in a community) suggests that removing a functional group (e.g., canopy trees) from an ecosystem is more important than its relative dominance (McLaren & Turkington, 2010). Although this research was based on a grassland community, if this pattern proves to be reinforced in woodland communities, it suggests that replacing one dominant tree species in a woodland with another will not have major consequences for ecosystem function. Perhaps the biggest concern should be the lack of dominant canopy tree species diversity in the British Isles. If climate change opens up new niches for species, it is possible that our native flora does not have a suitable species to fill a new niche. Compared to the continent, which has considerably higher diversity of tree species, Britain is reliant on relatively few which may result in exotic or introduced species becoming more competitive (e.g., downy oak, Larches, sweet chestnut). However, although exotic species may not necessarily fulfil biodiversity conservation aims, they could possibly maintain some important ecosystem functioning roles (e.g., productivity). 5.3.3.
Other factors The impact of a changing climate is not confined to mature trees, but is likely to influence regeneration as well. Masting of beech and oak, for example, is quite likely to change in frequency and production. This has already been seen in other countries in periods of climatic change (e.g., Sweden, Övergaard et al., 2007) but numerous other factors also affect masting, such as atmospheric nitrogenous pollution, pollination success and seed predation. Resource masting theory suggests that extreme events like drought will reduce masting (Kelly & Sork, 2002; Koenig & Knops, 2005), but fruiting is not fully understood and predictions of the impact of future climate are unknown (Schmidt, 2006). Drought is also likely to affect seedling establishment in the absence of competition (Robson et al., 2009). Higher CO2 levels will encourage tree sapling growth but the effects of increasing 53
temperature are likely to have a deleterious effect. Increased droughts may have a bigger effect on saplings than on older cohorts, as competition for water is likely to be more intense [this is contradicted elsewhere – older trees suffering more than young]. 5.4. Biological adaptation to climate change Autonomous adaptation responses are those which are under taken by natural systems, without human intervention and include in situ genetic adaptation, phenological and physiological adjustments and dispersal (polewards or upwards). A number of factors can hinder this adaptation and contribute to vulnerability, including (Chown et al., 2010; Kramer et al., 2010; Nicotra et al., 2010): •
Lack of opportunity for poleward migration •
Lack of opportunity for altitudinal migration •
Lack of opportunity for inland migration •
Limited dispersal capacity •
Barriers to dispersal e.g. oceans, urban areas •
Rarity/small population numbers •
Low genetic diversity Other factors which may affect the success of adaptation include: •
Little or no overlap between present and potential future distributions •
Endemism •
Restricted range – current and/or projected future range •
Loss of critical associated species – those with monospecific relationships are most likely to be affected •
Disruption of the synchrony in the timing of life cycle events (phenology) or of species’ interactions e.g. great tits and caterpillars. •
Increase competition from invading species (both natives and exotics) (from Berry, 2008) In many instances it is likely that human intervention will be required to increase the adaptive capacity of our woodlands. Human interventions, either reactively or proactively, constitute planned adaptation strategies which are outlined below. 5.5. Adaptation strategies for biodiversity conservation A number of planned adaptation options have been suggested for natural ecosystems and protected areas that range in scope and feasibility (Hulme, 2005; Heller & Zavaleta, 2008; Seppälä et al., 2009; Hagerman et al., 2010; Lindner et al., 2010; Spies et al., 2010). Here, we briefly outline the basis for the main options possible for British woodlands. 54
5.5.1 Reduce and manage stresses from other sources and activities One of the most common adaptation options discussed in the conservation literature is perhaps the most obvious too. Simply, if other threats and stresses are reduced (such as air pollution, over‐
harvesting, habitat conversion and species’ invasions), species and habitats are likely to have greater resilience to climate change (Halpin, 1997; Shafer, 1999; McCarty, 2001; Opdam & Wascher, 2004; Hulme, 2005; de Dios et al., 2007). A good example of this is can be seen with trees under water stress: many species are quite capable of tolerating drought, however, if they are attacked by insect pests or pathogens also their resistance is reduced which may lead to increased mortality within a population (Rouault et al., 2006). This option may be difficult in densely populated areas in the UK, but some of these measures have been considered as part of the Finnish National Adaptation Strategy (Marttila et al., 2005) and for management of the Cape Flora, South Africa (Department of Environmental Affairs and Tourism, 2005) and under careful and well‐backed community co‐operation could be a useful strategy in parts of Britain. In many woodlands, particularly near urban populations, this may be the key strategy and successful implementation could be the difference between maintaining ecosystem structure and function or a breakdown of a key element. Some woodland nature reserves in the UK are already encouraging neighbouring landowners to adopt agri‐environment schemes to reduce the effects of fertiliser and herbicide drift on woodland ground flora. 5.5.2.
Habitat restoration Habitat restoration would increase the area of potential habitat for many species as well as buffer existing reserves and hence increase resilience (Harris et al., 2006). This aim links with reducing and managing stresses as well as increasing landscape connectivity. Restored habitats in the landscape may allow migrating species a suitable habitat in a stepping‐stone or corridor matrix. This is already a major aim of national conservation bodies (e.g., Natural England, the Wildlife Trusts) but further development of regional (and national) partnerships should be encouraged. The potential for this strategy in the UK is large because of a high number of privately owned degraded woodlands, and there are also grants available to woodland owners to bring woodlands back into biodiversity friendly management. However, there are certain caveats to this approach, namely that restoring a habitat may indeed be taking a backward (and possibly an all too difficult) ecological step in the light of climate change but even if this is possible it may also require data on recent historical or even palaeoecological records to set targets which are often not available (Harris et al., 2006; Willis & Bhagwat, 2009). 5.5.3.
Increase landscape connectivity As the British landscape is highly fragmented it is likely to be too much of a barrier for successful species tracking of climate change (Jump & Penuelas, 2005). The implementation of corridors and/or stepping stones to link areas of habitat or reserves is a popular objective (Machtans et al., 1996; Maciver & Wheaton, 2005; Bailey, 2007; Öckinger & Smith, 2008). Examples include: Meso‐
American corridor and binational corridors in Latin America, in Europe, a multi‐lateral initiative to establish a stronger (i.e. 'climatically robust') network of ecological areas has been set up ‐ the 55
Pan‐European Ecological Network PEEN. The Netherlands have a similar ecological network (Ecological hoofdstructuur) that is being implemented and is intended to be a climate change proof. In Britain, the Wildlife Trust is trying to implement a similar scheme by encouraging landowners and reserve managers of similar habitat type to increase their linkages to neighbouring reserves and in Scotland the Glasgow and Clyde Valley Integrated Habitat Networks scheme is attempting a similar aim (Smith et al., 2008). Clearly, this would be a more useful strategy for mobile species but even for many plant species, and in particular the wind‐dispersed species, it will prove to be an important strategy (although a lot of woodland species are slow colonisers ‐ van der Veken et al., 2007). Furthermore, by increasing colonising ability it is likely that ecosystem functioning will be maintained or enhanced by ensuring a greater genetic diversity in species populations (Bailey, 2007). However, natural or assisted migration is considered a natural response to climate change for species although the risks of undesirable species migrating as well as ‘native’ species should be considered (Kirby, 2009). 5.5.4. Increase landscape permeability 'Softening' the landscape through reduction in unfavourable management practices and increasing area for biodiversity, e.g. through agri‐environment schemes (e.g., Living Landscape scheme run by Wildlife Trusts) can be a very effective means for improving species dispersal (Noss, 2001; Hannah et al., 2007). Although not quite as desirable as creating habitat corridors, in a multi‐use landscape (and much of Britain is), it is more likely to be an adopted strategy. Questions remain over its effectiveness however, and much work needs to be done to have a greater confidence in it’s ability to improve species dispersal under a changing climate. However, although there is still conflicting evidence (Kleijn et al., 2006), the benefits agri‐environment schemes bring to biodiversity are encouraging (Gillings et al., 2005; Donald & Evans, 2006; Merckx et al., 2009; Maj et al., 2010). For woodlands, this option may not be the most obvious choice, however, many agri‐
environment schemes offer short‐rotation coppice options for farmers which, although are not in any way as diverse, can offer a more suitable habitat for mobile woodland species to disperse. 5.5.5.
Increase size and/or number of reserves Longstanding biogeographical relationships (MacArthur & Wilson, 1967) between species richness and reserve size and number show that larger and more reserves offer greater biodiversity and hence maintenance or improvement of ecosystem functioning (Hooper et al., 2005; Reiss et al., 2009). This would also have implications for reserve connectivity (Pyke & Fischer, 2005; Williams et al., 2005; Wilby & Perry, 2006; Hannah et al., 2007), which may help species dispersal and migration. Additionally, like the first two strategies above, it will also help increase ecosystem resilience towards climate change (Reusch et al., 2005; Chapin III et al., 2007; Baron et al., 2009). This strategy is perhaps more likely to be a successful strategy in the UK due to the ever‐increasing resurgence in woodland planting (e.g., the Caledonian pine project in Glen Affric); however, it is also an opportunity to re‐address conventional wisdom on plant species provenance as increasing genetic diversity in new woodlands is likely to improve climate change resilience (see 5.5.9 below). 56
Another example of this strategy working in the UK is the ever‐expanding National Forest in the East Midlands (The National Forest, 2009). 5.5.6. Increase habitat heterogeneity Increasing habitat heterogeneity within reserves increases the available niche space for a wider range of species to migrate, grow and reproduce under changing climatic conditions (Lindenmayer et al., 2008; Ackerly et al., 2010). Furthermore, it may also have an effect on reducing threats like pathogen transmission (Condeso & Meentemeyer, 2007). Landscape heterogeneity should improve migration due to shorter dispersal distances and community reassembly as heterogenous landscapes usually have greater genetic diversity (which increases adaptive variation) and species diversity (a richer range of taxa to better enable assembly into new communities) (Ackerly et al., 2010). For the future of British woodlands this option is perhaps not as difficult as it first sounds; foresters and conservation managers have been adjusting and creating new habitat structures for centuries (for example, coppice coupe rotations, shelterwood systems, ride and glade creation) and as a conservation strategy it is already commonly practised amongst many woods in the UK anyway. It should also help maintain and improve ecosystem functioning by creating habitat, and hence, species diversity (Reiss et al., 2009). 5.5.7.
Focus on conservation efforts on north‐facing slopes Very few studies have examined the effects of topography and aspect on community structure but those that have confirm anecdotal evidence that aspect can have considerable effects on species composition (Rorison et al., 1986; Bennie et al., 2006; Marini et al., 2007; Gonzalez et al., 2009; Raatikainen et al., 2009). Bennie et al (2006) reported differences of 2.5 to 3.0oC in annual mean temperature in adjacent chalk grassland slopes ‐ it would certainly seem then that focussing conservation efforts for high priority species and habitats on north‐facing slopes would be a worthwhile strategy as similar differences can be seen in woodlands. Modelling the utility of topography has rarely been done either (but see Ackerly et al., 2010), but this would help identify suitable target areas for conservation focus. Examples of this strategy are so far hard to find. 5.5.8. Translocations and re‐introduction of species Translocations have been practised on a range of species groups and are considered to be one of the main adaptation strategies (Conant, 1988; Hodder & Bullock, 1997; McLean, 2003; Edgar et al., 2005; Hoegh‐Guldberg et al., 2008; Willis et al., 2009; Minteer & Collins, 2010). It may be especially beneficial for those species providing key services such as pollination (Hodder & Bullock, 1997) or for replacing a key structural (i.e., tree or shrub) species. However, there are a number of concerns about this approach and many think it is too simplified and does not take into account unintentional consequences such as evolutionary changes, new biotic interactions or invasive species risk (Huang, 2008; Ricciardi & Simberloff, 2009). It also raises some critical ethical issues that are not easily dealt with by ecologists alone as perhaps, more than any other intervention, highlights the role of man’s intervention in natural processes (Minteer & Collins, 2010). A number of questions arise when considering the process (Minteer & Collins, 2010): how do you select the 57
species to translocate? What are the legal, ethical and policy implications of translocation? Who decides to translocate a species? Should translocation just be used for climate change reasons or others like ecosystem function? Who is responsible for the success of the project? Currently, species translocation is more popular in the United States (Hannah, 2010) although examples in the UK are rare it does have a policy prescription in place (McLean, 2003). For many British woodlands perhaps one species to answer the first question would be large‐leaf lime; the modelling results suggest it is more tolerant of climate change than oak and ash, and it has a reasonable tolerance to drought; it would also fulfil a dominant canopy role in many of Lowland Mixed Deciduous Woodland Priority Habitat and would be well adapted to the edaphic conditions in much of Britain. Furthermore, as a native to England and a one‐time dominant species throughout lowland Britain (Pigott, 1991), it may not be opposed as much by conservation authorities (compared to say, Manna ash, sweet chestnut or downy oak). 5.5.9.
Introduction of wider range of genotypes For years conservation has worked on the assumption that for re‐establishing habitats sourcing species from local provenance was crucial due to numerous generation’s worth of adaptation to the local environment (Hubert & Cottrell, 2007). Clearly, as climate change forces local conditions to change a new approach to the conservation of genetic resources will be required. Foresters have been introducing different genetic stock of species for centuries; this approach may prove to be beneficial for conservation if species have southern European populations that will already be adapted to future British climates; e.g., beech seed from the drier zones in Northern Spain, Italy or southern France (Broadmeadow & Ray, 2005; O'Brien et al., 2007). Species with wide European distributions are likely to do better if genotypes from lower latitude sources are used in Britain; those species with small or narrow distributions may not fare so well (Hubert & Cottrell, 2007). As yet, no known example exists of this occurring in British conservation. A number of studies have confirmed that European beech genotyes respond differently to drought stresses (Peuke et al., 2002; Rennenberg & Schraml, 2002; Czajkowski & Bolte, 2006; Nahm et al., 2007; Kramer et al., 2008; Meier & Leuschner, 2008; Rose et al., 2009). Sourcing provenances of beech seed from other European countries would likely bring genotypes that would be better adapted to future climate change in Britain. More importantly perhaps is that it would help maintain ecosystem function within woodlands if this strategy could be applied to a range of key species (not just trees and shrubs) within woodlands. 5.5.10. Maintaining native community ecosystem structure and function Maintaining structure in its simplest sense (i.e., a typical woodland with canopy, shrub and herb layers), if deemed a priority (for example, to maintain ecosystem services like recreation and shade provision or productivity), would not be a difficult challenge as there are numerous suitable species from warmer climates that could be used (see 5.4.11). However, maintaining structure and biodiversity value would be an altogether more complex task; many of the above options would 58
certainly enhance ecosystem resistance and resilience in the face of climate change but ultimately some species may start to suffer and possibly create gaps or changes to the previous structure. The links between ecosystem structure and function are complex (Thompson et al., 2009; Nadrowski et al., 2010) and indeed ecosystem function science is still a young discipline which is still revealing contradictory relationships (Balvanera et al., 2006; Reiss et al., 2009). We still do not know, for example, how many species in British woodlands are important components of ecosystem function and how many have ‘functional redundancy’ (Petchey & Gaston, 2002; Luck et al., 2009; Nadrowski et al., 2010). Inherent to this will be individual species tolerance of future climate change and for some authors many woodland tree species have the potential to do will simply due to their long‐lived nature; however, other studies also suggest that increasing mortality in forest tree species may increase the evolutionary adaptation response to climate change by allowing faster population turnover (older, maladapted trees dying make way for new, better adapted seedlings)(Kuparinen et al., 2010). 5.5.11. Introduce new species to maintain habitat structure Semi‐natural habitats provide a range of ecosystem services that are important for society; these services may conflict with biodiversity conservation, but often they are mutually beneficial (Paterson et al., 2008). The establishment of 'neo‐native' habitats (Millar et al., 2007; Bolte et al., 2009) is very controversial but may be an only option if maintaining certain ecosystem services is desired. For many non‐native species that have ranges in central Europe, the question of whether they become neo‐native may be academic as without assisted migration, many of them won't be able to make it to the UK. However, perhaps it is worthy of further discussion if there are suitable species that may be able to maintain habitat structure (and possible hence ecosystem function). There are a few continental European tree species, for example, that have similar ecological niches to some potentially threatened tree species in England (e.g., Downy oak for common oak; narrow‐
leaf ash for common ash). One problem (and a major ecological one) with introducing exotic species is that new species (or neophytes) may not provide suitable environmental conditions for a whole host of other species (e.g., invertebrates and fungi) and hence a breakdown in the food web may ensue. Replacement species should ideally have similar traits to the original species (e.g., same shade provision, habit, leaf decomposition rates) (Kirby, 2009). Would southern European oak species provide the same niche opportunities for fungi that common oak does? What species would offer suitable (if any) replacement niches for beech? Further research is required in this area. There is also an issue of plant hygiene to ensure that micro‐organisms are not imported. 5.5.12. Improve inter agency and regional co‐operation Perhaps one of the most crucial strategies as it has implications for translocation schemes, genetic stock harvesting, corridor and landscape permeability schemes, etc (Tompkins & Adger, 2004). Nationally, inter‐agency cooperation is aided and abetted by Natural England and as different conservation bodies are usually working for a common good, relationships are normally healthy. 59
Attempts to include private landowners in conservation adaptation plans are mainly through incentive schemes for landowners such as the ELS agri‐environment scheme; however, these schemes, so far, pay little explicit attention to climate change adaptation and do not offer opportunities for farmer members to plan across regional landscapes. This is not an impossible goal however, as Natural England manage ELS schemes in England they are in a position to adopt and encourage better regional connectivity and are increasingly involved in policy design for nature adaptation. Pan‐European cooperation is traditionally well founded in academic networks concerning biodiversity, climate change and conservation; however, in the practicing conservation arena there are comparatively few cooperative bodies. Some exceptions are ENCA (a network of the heads of European Nature Conservation Agencies) which seek to promote “management of our European landscapes to allow nature (as well as humans) to adapt to climate change… embedding ecological concepts of resilience and a healthy natural environment into the adaptation debate… aim[ing] for functioning ecosystems that are resilient to extreme events…” (Natural England are leading members); the ECNC (European Centre for Nature Conservation) which is a multi‐partner non‐profit organization seeking to promote ”conservation and sustainable use of Europe’s nature and biodiversity”; and, (in eastern Europe) CEEweb (Central and Eastern Europe) which has a strong climate change and conservation programme. At European governance level the EU is promoting cooperation through various activities including the NATURA 2000 policy directive which heavily invests in climate change adaptation research. Also, the Pan‐European Biological and Landscape Diversity Strategy which also seeks to address climate change but also aims to involve European NGOs. Finally, The EU’s recent white paper on Adapting to climate change: Towards a European framework for action explicitly sets out a number of adaptation aims across sectors (including conservation) but importantly calls for: “employing a combination of policy instruments (market‐based instruments, guidelines, public‐
private partnerships) to ensure effective delivery of adaptation; stepping up international cooperation on adaptation… the EU, national, regional and local authorities must cooperate closely.” 5.5.13. Ex situ conservation The use of ex situ seed banks, captive breeding or zoos is seen as a last resort by most however it may be the only realistic response for some rare species (Maunder et al., 2001). The UK has a strong history in ex situ conservation and many of the oldest and largest botanical gardens, herbaria and arboreta (e.g., Kew Gardens, Bedgebury National Pinetum and Westonbirt National Arboretum) have active conservation programmes. Ex situ conservation is however expensive and can create population health problems caused by a lack of genetic mixing (Hamilton, 1994; Maunder et al., 2001). 60
5.5.14. Maintain and increase monitoring This strategy is vitally important as it provides the only really method of understanding what change is occurring in the site and in what direction that change may be heading. Baseline data is necessary to understand change (and the direction of change) and also makes adaptive management easier (see below) (Suffling & Scott, 2002; Wilby & Perry, 2006). Monitoring species’ response to climate change is doubly important now and ideally should include measurements for growth (above‐ and below‐ground), health (crown condition, pathogens), species’ relative abundance, mortality, regeneration, soil water conditions and phenology. Unfortunately, monitoring is an expensive operation and very few programmes exist to track changes to species and habitats (Lepetz et al., 2009); however, although funding for monitoring is dwindling in some organisations (Read, pers. com.), the UK does have an extensive Environmental Change Network with twelve monitored sites (including woodlands) throughout the UK (ECN, 2011). 5.5.15. Adopt adaptive management approach Heavily dependent on monitoring, this approach is essentially an iterative process that adapts to and responds to ecological and environmental situations as they occur. It is seen as a crucial response in a range of sectors including conservation and forest management (Lasch et al., 2002; Scott et al., 2002; Tompkins & Adger, 2004; Hulme, 2005; Maciver & Wheaton, 2005) although it requires an ability to analyse data and have the facilities to respond to changes so it is not a viable option for organisations with minimal resources (Spies et al., 2010). It also relies on an underlying faith in the further commitment to conservation in the face of climate change and as such can be sensitive to the vagaries of future environmental policy and funding (Hagerman et al., 2009). Ironically, many adaptive management practices can be learned from the forestry industry (Heinimann, 2010) which in recent decades has sought to manage forest in a more holistic and integrative manner. This process requires a good understanding of the ecological processes (e.g., regeneration, growth, competition, mortality), ecosystem processes (biogeochemical cycles, succession, disturbance, etc.) over various time‐scales (it is also important to appreciate ecosystems are dynamic and not static). Heinimann (2010) suggests that managers should realise that most ecosystems will require the adoption of ‘moving targets’ as many ecosystem attributes (functions and services) will change over time and space. He also highlights the concept of ‘rule discovery’ which in essence allows new rules (or hypotheses for ecological approaches) to replace old or disconfirmed ones. 5.6. Non‐intervention 5.6.1. Laissez‐faire approach This approach stresses the importance of allowing ecological community processes to take their course (i.e., change), rather than allowing human intervention to preserve structural “habitat ideals” or particular species (Scott et al., 2002; Suffling & Scott, 2002; Hossell et al., 2003). This may result in a new suite of species and thus altered community composition (Kirby, 2009). Although outcomes may be hard to predict, the approach may well be beneficial for ecosystem function if alternative management options are infeasible in terms of complexity, time, resource 61
or space availability. It also provides opportunities for those species which are adapting autonomously to climate change, although their arrival could be facilitated by changing management regimes. The conservation priority of a given species in decline will depend on a number of factors, including: 1) the perceived importance of the species, 2) the species’ remaining geographical extent, 3) the time‐scale and severity of the decline, and 4) the significance of knock on effects on other species/habitats. In practice, the first and second of these factors will be reflected in whether the species is recognised as of local, regional, national (e.g. BAP species) or international significance (listed in EU Birds or Habitats Directive). This list could be seen as indicating an increasing level of priority in order to meet conservation commitments and would make the laissez‐faire approach less likely to be appropriate. Combining these priorities for the species with information on a species’ projected future distributions will further assist priority ranking, with rare species with little available future space warranting greater attention. In the context of climate change, a pervasive threat to most species, this can be assessed in terms of the degree of loss of suitable climate space. The final factor for consideration in determining the species’ priority is its role in relation to the wider ecosystem. This is often difficult to assess, but a single species may be critical to other dependant species or to particular ecosystem functions or services. For habitats, the equivalent question would be: Is the habitat critical to maintaining species of high conservation concern? For example, many tree species, such as beech and pedunculate oak, have saproxylic species associated with them, which would be adversely affected by their loss. Once the level of priority has been decided, then the feasibility of undertaking adaptation needs to be evaluated. There are number of potential climate change adaptation options (Hopkins et al., 2007; Smithers et al., 2008). In order to determine whether these management options are prioritised over the laissez faire approach the feasibility of each management option needs to be assessed. The flowchart in Figure 8 has been devised in order to facilitate this consideration. The level of priority, as detailed above, should be used first to decide whether a particular decline is a management priority (1). If it is (2), then the multiple management options available should be considered in turn, with priority given in order to those with the potential to reverse, prevent and slow the decline. These management options could include reducing pressures, such as grey squirrel damage, or climate change through managing water levels in beech woodlands. If decline is not a priority (3), then local measures might be taken to counter adverse impacts and monitoring undertaken to see if the species’ priority warrants upgrading. Where a suitable management option is available (4), then its feasibility needs evaluating as there may be barriers to implementation, which make it unlikely to be undertaken (Berry et al., 2011). 62
Figure 8: Flowchart for assessing the feasibility of adaptation to climate change. Available geographical space is a key barrier. For example, in a climate change context, if there is little or no projected suitable climate space, then undertaking extensive measures might not be appropriate. For example, in a climate change context, pressures related to land use and management may reduce the likely success of climate change adaptation measures, thus reducing the appropriateness of a management intervention beyond laissez faire. This could be especially 63
relevant for certain Arctic Alpine species in Scotland if they are maintaining their range elsewhere e.g. in Scandinavia, even though the species, such as twinflower, may be of national importance. Similarly if a species losing future suitable climate space is also experiencing decline due to other pressures, and these pressures cannot be feasibly addressed and/or are projected to increase in the future then letting go might be more pragmatic. In some cases, habitat recreation could be an option, but when determining feasibility, time constraints need to be considered. Broadleaved woodland, for example may take 100+ years to get the necessary structure and function. Another measure, more applicable for species, is translocation, but this may have to be repeated to maintain new populations and will have financial and resource implications. It could be possible for lichens be transported northwards on suitable branches and then wait to see if they adapt to growing on a new tree species. If there are no options (5), then this could be considered grounds for adopting the laissez faire approach and alternative species could be sought that would have similar functions within the ecosystem. This could include species from within the community that may be better adapted to the climate or new arrivals, as discussed in Section 4. If action is feasible (4), then the level of feasibility of the action needs assessing (6). By balancing the conservation priority of a species with the feasibility of the management option a decision can then be made as to whether the species warrants the selected management option. If it is consider unreasonable (7) or if it not feasible for any reason (6), then alternative management options of lower effectiveness (2) could be considered. If the action is deemed appropriate (8), then its impacts on other species, the community and habitat could be taken into consideration to ensure that unwanted consequences and trade‐offs are not involved. An example of such a trade‐
off occurs in pine woodland, where if climate‐related mortality in young Scot’s pine could be reduced to assist its survival in Scotland (Persson & Ståhl., 1990), this would have knock on effect of reducing sessile oak’s potential to increase in abundance. If such knock‐on effects are seen as undesirable then an alternative management option needs to be considered that either mitigates these effects or takes a different approach (2). If the impacts are considered acceptable, then adaptation management can be undertaken (10). Thus the decision whether to “let go” of a species, by taking the laissez faire approach, should be driven by the conservation priority given to that species and the feasibility of undertaking the most appropriate management strategies. These decisions will necessitate subjective judgement and may depend on context of the ecological community and other demands on management resources. Decisions to let a species go should not be taken lightly, and would need to involve consultation of appropriate interested parties from both governmental and non‐governmental organisations and academic spheres. 64
5.7. Data gaps One 178 woodland species were modelled in this study but there are distribution data available for many more species associated with other woodland communities. Increasing the number of species modelled would therefore improve the power of the analysis. However, there is still a lack of distribution data for the vast majority of European species. Similarly, pursuing functional trait approaches to understanding ecosystem response to climate change requires good quality data on plant (and animal) traits not only for individual species but also to cope with intra‐species variation (particularly for studying community assembly patterns or evolutionary processes) (Albert et al., 2010). 5.8. Further Study 5.8.1. Developing better modelling techniques Modelling responses of species interactions to climate change Another important area of research in improving projections of the possible consequences of climate change is the effect of species interactions. It is increasingly acknowledged that climate change affects species interactions and that these interactions affect species ranges and abundance, as well as ecosystem functions (Tylianakis et al., 2008; van der Putten et al., 2010; Walther, 2010). Models incorporating biotic interactions have been built, which indicate that the inclusion of the interactions improve the predictive performance (Heikkinen et al., 2007; Meier et al., 2011). These models incorporate parameters indicating the effects of species' co‐occurrence as biotic predictors, such as occurrence (Heikkinen et al., 2007) and basal area (Meier et al., 2011), in order to project the future changes reflecting the competitive and facilitative interactions. However, most of these models have been applied to a limited number of species within the same trophic level (but see Araújo & Luoto, 2007; Kissling et al., 2010), whereas species actually interact in complex food webs encompassing different trophic levels. Thus, it remains a major challenge to include knowledge on multitrophic level interactions (Tylianakis et al., 2008) One of the useful approaches in exploring the consequences of climate change on population and community dynamics across trophic levels is ecological networks. Ecological networks as used here are not explicitly spatial networks, but describe the species present in a network, their abundance, and the frequency of interactions between them, as well as the functions that these species perform (Morris, 2010). Ecological network research focuses on a guild of interacting species, e.g. food webs, mutualistic networks (e.g. plant‐pollinator networks) and host‐parasitoid networks (Ings et al., 2009). For example, Memmott et al. (2007; 2010) have shown that temperature‐
induced advanced flowering of plants reduces floral resource availability for many of their pollinators, and this mismatch is likely to result in curtailment of the field season for the pollinators and in some cases extinction of plant or pollinator species, and their interactions. On the other hand, Devoto et al. (2009) have found that plants and their pollinators respond similarly. Although the application of a network approach in the context of climate change is increasing (Montoya & Raffaelli, 2010), studies for terrestrial species are still limited. More studies are available for 65
marine organisms, for example, on the effects of temperature warming on host‐parasite system (Mouritsen et al., 2005; Studer et al., 2010) and on the effects of induced range shifts of invasive species on trophic interactions (Occhipinti‐Ambrogi, 2007). Limitations of ecological networks in projecting biodiversity changes include their tendency to be biased towards species at the higher trophic levels rather than at the base of the web and that well‐defined networks are often from species‐poor habitats (Ings et al., 2009). In addition, ecological network research often focuses on a subset of interactions and does not include different types of networks, e.g. food webs and host‐parasitoid networks (Ings et al., 2009), and thus fails to describe complete networks in the ecosystem. Furthermore, ecological networks do not take account of behavioural flexibility, for example, pollinators’ behavioural changes leading to foraging on and pollination of new plant species (Burkle & Alarcon, 2011). The network approach can be incorporated with bioclimatic envelope models using a conceptual framework. For example, in MONARCH 2, a hybrid approach was taken, whereby outputs from SPECIES were used to identify species which might be arriving in or leaving a habitat and then a conceptual framework was developed for each situation, based on species interaction matrices, particularly trophic interactions, and included the development of work on food web and niche space as mechanisms in community ecology (Figure 9). The outcome for the community depended on the position of the arriver/leaver within that community and this is illustrated for the case hairy wood ant, which was projected to arrive in the pine woodlands around Rannoch (Figure 10). So‐called ‘hybrid’ models (Thuiller et al., 2008; Fontes et al., 2010) are emerging, which are an alternative approach for including biotic interactions into the projection of future range changes. The hybrid models are developed from process‐based models incorporating physiological responses and consist of various sub‐models working at different spatial scales. These models can project dynamic range changes taking account of the effects of dispersal and competition (Lischke et al., 2006; Hughes et al., 2008b). Complex interactions can also be expressed, including a novel interaction by a species encountering another at the leading edge of its range due to climate‐
induced range shift (Thuiller et al., 2008). Further development is promising, although these models are data hungry and require detailed knowledge of ecological processes that is usually unavailable for large numbers of species and regions (Thuiller et al., 2008). Including species interactions is essential in improving projections of climate change impacts (Lavergne et al., 2010). Conceptual models integrating ecological networks into bioclimatic envelope models and the hybrid models can provide more realistic projections of climate change impacts than simple envelope models. However, both approaches are limited by data availability; defining a complete ecological network requires extensive search efforts and constructing a hybrid model requires detailed information on the physiology of organisms. Thus, more and better empirical data are necessary to parameterise and test these models (Petchey et al., 2010), which 66
Figure 9: The Arriver conceptual model. This conceptual framework explores the consequences of a species arriving (migration/dispersal) in a community due to climate change. The model recognises that communities are dynamic entities changing over time (e.g. succession) and that climate change is going to be a major disturbance on community structure, function and dynamics (from Masters and Ward, in Berry et al., 2005). Figure 10: A possible outcome on the composition of the species community of the colonisation by hairy wood ant, based on Fowler and Macgarvin’s (1985) suggestion that this species is a keystone species. 67
would require further laboratory and field‐based research and long‐term monitoring (Traill et al., 2010). 5.8.2. Understanding community response: species or traits? A number of strands in ecology have been trying to provide better generalisations and models of community dynamics by moving away from a traditional and individualistic taxonomic or species‐
based approach to one based on the response (or effect) of functional traits or groupings of traits (McGill et al., 2006; Westoby & Wright, 2006). This approach is not new in ecology and although its development gained ground in the 1970s and 1980s (Knight & Loucks, 1969; Grime, 1977; Gaudet & Keddy, 1988), it wasn't until relatively recently that ecologists started to adopt it as a method to address a number of important issues in ecology and conservation (Lavorel et al., 2007). The trait approach in comparative ecology is not confined to plants alone and research into different taxa is ongoing (e.g., Bonada et al., 2007; Cleary et al., 2007; Heino et al., 2007; Jiguet et al., 2007). Taxonomic approaches are often not able to produce generalised community responses to change, for example, congeneric species may have greater differences in response to environmental gradients than species totally unrelated but with similar ecological niches (Weiher et al., 1999). Community descriptions that are based on taxonomy can fail to recognise if two spatially separated communities with different species composition are structurally similar (Duckworth et al., 2000). Furthermore, species‐based community analyses can sometimes suffer from redundancy (Walker et al., 1999) and noise within the data (Gauch, 1982; McCune, 1997) which can reduce the power of the analysis (Kent & Coker, 1992). Ultimately, therefore, as long as trait variation is greater between species than within species, traits should provide an easier route to defining ecological generalities and hence definable rules in ecology (Pakeman, 2004; McGill et al., 2006; Shipley, 2007). When species are grouped into functional types or guilds based on their shared or similar response to environmental factors these groupings are generally more useful for analysing responses to, for example, disturbance (McGill et al., 2006; Westoby & Wright, 2006) or to ecosystem functions like productivity (Bailey et al., 2004). Hence, rules developed in one community may be applicable in another community in a different region or continent. Trait‐based approaches provide a more direct link between the environment and the community ‐ as species presence is determined by so called 'environmental filters' for traits (Keddy, 1992; Díaz et al., 1998; Lavorel & Garnier, 2002; Mouillot et al., 2007). This filtering acts hierarchically with traits that survive the overarching climatic filter then passing through further disturbance and/or interaction (with other organisms) filters (Díaz et al., 1999) and can provide a predictive link between vegetation and environmental conditions (Dyer et al., 2001). Traits are also ubiquitous whereas species vary immensely across geographic space (and time), this is particularly important in communities that lack detailed taxonomic description or the flora is understudied (Pakeman, 2004). 68
The utility of traits in prediction ecology has made them a popular base for studying community dynamics in response to a range of disturbances and environmental change (Lavorel et al., 2007). Trait composition in communities has been analysed across gradients of fertility (Reader, 1998; Pakeman, 2004; Clarke et al., 2005) and disturbance (Ackerly, 2004b; de Bello et al., 2005; Mabry & Fraterrigo, 2009), or both (Craine et al., 2001; Fynn et al., 2005; Gross et al., 2007; Lehsten & Kleyer, 2007). They have also proved useful for understanding invasive species ecology (Lloret et al., 2004; Lambdon & Hulme, 2006; Moles et al., 2008) and community responses to fire (Falster & Westoby, 2005; Moretti & Legg, 2006; Keith et al., 2007). An important aspect of trait‐based research is to understand trade‐offs in plant design and functioning that appear consistent across biogeographic regions (Westoby & Wright, 2006). Some of the most recognised patterns are: specific leaf area and leaf life span (leaves with higher leaf mass per leaf area tend to have a longer life span but require greater strength to maintain them, however short‐lived leaves are easier to produce) (Reich et al., 1997; Craine et al., 2001; Wright et al., 2004); seed mass and fecundity (smaller seeds are produced in greater numbers but their chances of survival under different conditions are far lower than large seeds) (Turnbull et al., 1999); and, potential plant height and growth rate or shade tolerance (taller trees are often slower growers and shade tolerant when juvenile, smaller trees grow faster but will eventually be overtaken by slower growing trees) (Poorter et al., 2003). Gradients across varying climatic scales have provided ecologists with useful insights into plant responses to factors like temperature and precipitation (Fonseca et al., 2000; Choler, 2005; de Bello et al., 2005; Davidar et al., 2008; Oyarzabal et al., 2008). The growing interest in ecosystem function is also providing another avenue of research for trait‐based projects. It is increasingly recognised that some traits not only respond to environmental factors, but can have an effect on ecosystem processes too; as they are measurable they can provide a quantifiable link to ecosystem function (Eviner & Chapin III, 2003; Garnier et al., 2004; Quetier et al., 2007; Suding & Goldstein, 2008). One of the main aims in trait‐based ecology is to identify which functional traits are important for predicting population, community and ecosystem processes (Lavorel & Garnier, 2002; McGill et al., 2006). However, as important as this aim is, it must also be couched in practicality for ecologists if they are to fulfil the aim of reducing complexity too (McGill et al., 2006; Lavorel et al., 2007). Hence, the need to provide relatively easy trait measurement, which is also inexpensive and can be standardised for ecologists anywhere, is paramount (Lepš et al., 2006). These trait data are obtained from either field sampling, plants grown in a 'garden' environment with little environmental heterogeneity, or from laboratory screening (with even better control of the environment and also plant ontogeny) (Cornelissen et al., 2003). Plants for measuring should be healthy, well‐grown and in well‐lit environments (which may be difficult for woodland under‐story plants) (Cornelissen et al., 2003). Measurement data may be continuous (e.g., height, seed mass) or categorical (e.g., life form, dispersal mode); further consideration must also be given to 69
phenological data due to its circular nature (i.e., to better identify the relative closeness of December and January values which may not be clear with numerical assignations such as '12' and '1') (Podani & Schmera, 2006). The need for relatively easy and rapid quantification of trait measurements has given rise to them being termed soft traits (Hodgson et al., 1999) which are more useful if they correlate with hard traits (i.e., difficult to measure traits). For example, seed mass and seed shape are soft traits that have been shown to correlate with the seed persistence (Thompson et al., 1993). Twelve years ago Weiher et al (1999) set out to define the most useful traits that would capture the large majority of the functional characteristics of plants by summarising research up to that point (table 5.1). This list provides a useful a priori starting point for choosing soft traits as easier representatives of hard trait characteristics; however, choosing the correct traits for any given study is very important if the the true nature of environment/trait relationships is to be determined and the use of ordination techniques can improve the explanatory power of the analyses (Bernhardt‐Romermann et al., 2008). Consensus on techniques for obtaining trait data from the field or laboratory is important (Cornelissen et al., 2003); however, in recent years a range of trait databases have become available to European ecologists (e.g., Fitter & Peat, 1994; Klimes & Klimesova, 1999; Poschlod et al., 2003; Hill et al., 2004; Kühn et al., 2004; Niinemets & Valladares, 2006; Grime et al., 2007; Kleyer et al., 2008) which have proved to be very popular in many comparative ecological studies. Table 5.1: The common challenges faced by plants and some suggested traits (Weiher et al., 1999) Challenge Hard trait Easy Trait 1. Dispersal Dispersal in space Dispersal distance Seed mass, Dispersal mode Dispersal in time Propagule longevity Seed mass, seed shape 2. Establishment Seedling growth Seed mass, Relative Seed mass, Specific Leaf Area (SLA), Leaf Growth Rate Water Content (LWC) 3. Persistence Seed production Fecundity Seed mass, Above‐ground biomass Competitive ability Competitive effect Height, Above‐ground biomass and response Plasticity Reaction norm SLA, LWC Holding space / longevity Life span Life history, Stem density Acquiring space Vegetative spread Clonality Response to disturbance; Resprouting ability, Resprouting ability, Onset of flowering, SLA, stress and disturbance Phenology, LWC avoidance Palatability 5.8.3.
Traits and community assembly A major goal in ecology is to understand the processes behind community structure and assembly (Keddy, 1992). Historical approaches to this goal have been quite varied (e.g., compare Clements, 70
1916; Gleason, 1926) and still continue to follow either a stochastic demographic route (Hubbell, 2005), or a more deterministic path (Tilman, 1985), or both (Weiher & Keddy, 1995; Fukami et al., 2005). Community assembly using plant traits is a relatively young aspect of trait‐based ecology and is seen as one possible way of reconciling the approaches above (Shipley et al., 2006). This has seen the creation of 'assembly rules' (Keddy & Weiher, 1999) which are defined by abiotic factors first and then biotic controls that set the levels of dominance in a community (Grime, 2001; Cingolani et al., 2007). This approach is particularly useful because it defines relationships between traits ‐ not species ‐ and environmental conditions; in theory the concept thus fulfils the need for generalised principles (McGill et al., 2006) as these relationships can then be applied to regions with different species pools (Keddy, 1992). The ongoing development of the concept is building on the known relationships between traits and climate, disturbance and biotic interactions. For example, potential plant height is a long‐
standing trait associated with competitive ability (Grime, 2001), however it is also known that trait/competition associations may vary across different environments (Tilman, 1988; Goldberg, 1996): e.g., leaf and root traits are more important in dry (Ackerly, 2004a; Funk & Vitousek, 2007) and nutrient poor environments (Craine et al., 2001). In some environments other biotic factors may be more important for community assembly; e.g., facilitation is known to be a key interaction in high stress environments (Brooker et al., 2008). The study of the role of traits in different environments is slowly helping to identify the traits that respond to different environmental conditions. For example, identifying the traits that convey drought tolerance in communities (Barboni et al., 2004; Valladares & Sánchez‐Gómez, 2006; Sánchez‐Gómez et al., 2008) may help to predict community outcomes under future climate change. But it is also worth noting that it is not just environmental parameters that need to be modified for climate change prediction as other filters may be changed indirectly by climate too (e.g., new pest attacks, change in fire frequency, changes in species' competitive abilities) (Díaz et al., 1999; Bond & Keeley, 2005). New methods have been developed to model community assembly using traits from the local species pool (Shipley et al., 2006; Cornwell & Ackerly, 2009). Under the assumption that abundant species are more important drivers of ecosystem processes in any given environmental state (derived from Grime's (1998) mass‐ratio concept), community weighted means of the traits selected can be calculated from species abundance data (Garnier et al., 2007; Lavorel et al., 2008). This quantitative tool can then be applied to predicting the species' composition from different species pools using the most useful functional traits representing most of the total biomass (Shipley et al., 2006) and it has the potential for predicting community structural change in response to different kinds of environmental (including climate) change. 5.8.4.
Traits and ecosystem function Plant traits have been shown to have responses to abiotic factors but they also have implications for defining the relationship between community structure and ecosystem function (Grime, 1997; Loreau, 2000; Loreau et al., 2001). In a time when ecosystem services are recognised as being vital 71
for human welfare (Millennium Ecosystem Assessement, 2005), and concerns that climatic change and other global environmental drivers are having deleterious effects to society, the links between functional traits and ecosystem function are more important than ever (Díaz et al., 2007; Fortunel et al., 2009). Community weighted means of traits is also key to understanding ecosystem function as important traits for resource acquisition and use are related to their overall contribution to the community (Lavorel & Garnier, 2002) which can also be 'scaled up' to the ecosystem level (Hooper et al., 2005; Vile et al., 2006; Suding & Goldstein, 2008). The key aspect of this research is that it can be applied to different scenarios of land‐use change or disturbance so ecosystem function can be quantified across environments and has important implications as a tool in valuing and protecting ecosystem services (Quetier et al., 2007). Trait‐based approaches in ecology have also contributed to the relationship between biodiversity and ecosystem function (Grime, 1997). There is increasing confidence that it is not species diversity that is the main component affecting ecosystem function, but rather functional diversity (Tilman et al., 1997; Díaz & Cabido, 2001; Hooper et al., 2005; Mokany et al., 2008). Definitions of functional diversity vary but essentially it can be described as the functional trait variation among species in a community (Petchey & Gaston, 2002). Petchy and Gaston (2006) have outlined four main requirements for measuring functional diversity: the need for pertinent trait data for the species under study; traits weighted according to their functional importance; a statistical measure to cope with the form of measurement; and measurements that explain ecosystem processes. Much work has gone into devising suitable methodologies for measuring functional diversity (Petchey et al., 2004) but perhaps the two most common methods are assessing dissimilarity among species using their traits (the Rao Index) (Lepš et al., 2006) or assessing trait variance in a community (Mason et al., 2005). In fact, Mason et al (2005) suggest that stricter definitions and components of functional diversity should be ascribed by ecologists to break down functional diversity into (analogous to species diversity indices) functional richness, functional evenness and functional divergence. However, already the concept functional diversity has been applied to predictions and measurements of various aspects of ecosystem function such as ecosystem stability, nutrient cycling, decomposition and productivity (Cornelissen & Thompson, 1997; Tilman et al., 1997; Díaz et al., 2004; Vile et al., 2006; Quetier et al., 2007; Nadrowski et al., 2010; Zavaleta et al., 2010). The burgeoning use of traits in ecology reflects their versatility in explaining plant responses to environmental factors (Ackerly & Cornwell, 2007) as well as their utility in understanding ecosystem function (Garnier et al., 2007), although the traits that confer these roles may not overlap (Lavorel et al., 2007). The growth of trait databases will be an important aspect of the further development of trait‐based research, but so too will the refinement of our understanding 72
of how different traits are important to the assembly of communities under changing climates (McGill et al., 2006; Suding & Goldstein, 2008). 5.8.5.
Traits and British woodlands In the context of this report a discussion of a trait‐based approach is no good good if it cannot be applied the future of woodlands in Britain. Certainly the relatively easy methodology for using traits commends it working in many habitat types (Lavorel et al., 2008), and the burgeoning development of trait databases in the UK and Europe allows for easier analysis. In fact, most of the plant species studied in this report already have extensive autoecological data available (see list above in section 5.8.2) as do many of the non‐plant species. In addition, new research has already adopted trait‐based approaches in woodlands or wooded landscapes (Burton et al., 2009; Mabry & Fraterrigo, 2009; Sciama et al., 2009; Walker et al., 2009; Baraloto et al., 2010; Tanaka & Koike, 2011). A next step then could be to apply this model to understanding the effects of climate change on woodlands; a number of approaches are possible: •
Calibrate bioclimate models of species distributions further by analysing if specie’s performance is affected by their traits (Pöyry et al., 2008; Pöyry et al., 2009; Hanspach et al., 2010); •
Predict species composition of woodlands based on traits (Tanaka & Koike, 2011); •
Analyse woodlands across climatic gradients using community‐weighted trait composition (Pakeman et al., 2008) to infer future changes in structure and function (Paterson, unpublished); •
Relate trait composition of woodlands to changes in ecosystem services (Quetier et al., 2007; de Bello et al., 2010; Lavorel et al., 2011). Exploratory work in trait approaches to future woodland composition (Paterson, unpublished) suggest that because the pace of climate change is such that migration of many plant species will not track climate sufficiently well, particularly in the fragmented British landscape, the future community assembly of woodlands in Britain may well draw from non‐native plant sources (including our introduced garden flora). If community assembly filters can be predicted for future climates in Britain, further screening the plant traits of our common garden flora may provide an idea of the the possible species likely to compete with native species. Alternatively, if species' translocation from continental European countries becomes financially and politically expedient, knowledge of these trait characteristics will help identify candidate species from other lower latitude woodland habitats. 6. Conclusions The evidence for the contribution of humans to climate change is now overwhelming and evidence of biotic responses is burgeoning too. Species respond to climate change in a number of ways including physiological, phenological, range shifts and evolutionary changes. While all these responses have been documented in European and UK flora and fauna in the last few decades, the 73
evidence of changes to habitats in the UK is not yet conclusive. Projecting biotic responses to climate change is problematic, but a number of tools are available. One of the most popular is a modelling technique that projects new bioclimatic space for a specie’s distribution under different climate scenarios. As this does not take into account factors, such as biotic interactions, caution is necessary when interpreting the results. A total of 178 woodland‐
related species for which modelling results were available demonstrated that many woodland species have quite different responses to climate change; some will be ‘winners’, but a considerable number will lose potential bioclimatic space in the British Isles in the forthcoming decades. Most species gained climate space, although there are some significant losers including pied flycatcher, twinflower and narrow‐headed ant. Of the main winners, the two native lime species both stand out as potential future dominant canopy species, although beech and oak also make very small gains. South‐east England potentially will be hit hardest by species losses, with many species, including major canopy trees losing bioclimate space here from 2050. Many species that are dominant in the south today, however, will be more successful competitors in Scotland in the future. Although beech is already naturalised in Scotland, management of it in semi‐natural woodlands may need to be re‐evaluated. Most woodland shrub species do very well, although one of the main losers could be bramble which would have implications for ecosystem functioning in many woodlands. A number of mobile species gain bioclimate space, including some charismatic species, such as the red squirrel and dormouse, but other factors may prevent them from realising this. However, a number of species with strongholds in Scotland are likely to suffer including the Scottish crossbill, Scottish wood ant and capercaillie. On the basis of the modelling of the impacts of climate change on a range of species associated with the woodland PH, it is likely that Wood Pasture and Parkland habitats, Upland Oakwoods and Upland Mixed Ashwoods will see few changes. Lowland Beech and Yew woodland could extend into northern Britain, but it may suffer losses, as although beech is projected to have suitable future climate space, in reality, its intolerance to drought may reduce its growth and competitiveness, especially in south‐east England. This may result in shifts to more oak dominated woodlands. Yew woods, although comparatively rare, should remain fairly resistant to climate change. The species found in Wet woodlands have a mixed response in the modelling results, as both willow species suffer bioclimate space losses, although alder, a major component of many wet woodlands gains slightly. Lowland Mixed Deciduous Woodland, which is possibly the most ubiquitous woodland type in Britain, should remain relatively stable. A number of important tree species could experience small losses which may affect the competitive balance in some woodlands (e.g., between ash and oak in ash woodlands) although there are some striking gains including three rare species (large‐leaf lime and both service trees). This has implications for conservation, not least because lime used to be a dominant woodland canopy tree throughout much of Britain several thousand years ago. Upland Birchwoods and Scots Pine woodlands are possibly the major losers in this study. Both the iconic species in Upland Birchwoods (silver and downy birch) lose bioclimate space as well as the other major trees (rowan, grey willow and wych 74
elm). The two winners are both oak species which suggests that even on the less stable soils and in canopy gaps, the birches may not be successful colonisers and oak may dominate. For Scots Pine woodlands, Scots pine is projected to lose significant amounts of climate space under the high emission scenario by 2080, although it is unlikely that this will lead to immediate range reduction due to the species’ longevity. Pests and diseases of Scots pine are, however, likely to increase with warmer temperatures, but incidents of fire may increase, which often aid the regeneration of Scots pine and silver birch. Woodland structure, therefore, may change, with sessile oak stands possibly increasing, while Scots pine decreases, and subsequently, the species associated with Scots pine could also decrease, including Scottish crossbills and wood ants. Of the 11 rare species projected to lose suitable climate space (Table 4.11) little is known about the potential effects on woodland functioning, although there is some evidence that it could be affected by the possible decline or loss of wood ants. Many of these rarer species, however, are currently more affected by land use and management practices, with climate change acting as an additional pressure. These potential losses need to be balanced against the possible expansion of some species associated with woodlands, such as some bats. In addition, there are continental species, such as downy and holm oak and manna ash, which could have future suitable climate space in the UK. These have similar ecological niches to some tree species potentially threatened by climate change in Britain, so while they may lead to a change in woodland composition there may be little change in some functions, but this needs further investigation. In terms of functional groups, trees are projected to have a mean gain of 15%, while shrubs and ground flora species show a mean 1% loss/gain respectively. It should be noted, however, that these findings are based on a selection of British woodland species, although almost all major tree and shrub species are included. For the other taxa, where the number of species is smaller, mammals have potential gains in climate space, while birds and ants show losses. It is difficult to predict whether new specialist species will replace those that are lost, as specialist species are often poor at dispersal; thus generalist species are more likely to be successful colonisers. If replacement does occur it is also hard to assess whether community structure and function will be maintained, as we have no (few) examples of this and is likely to be situation specific. The possibility of more competitive, possibly invasive, species becoming established could best be ascertained by checking successful species against their traits. Predicting changes to woodland structure are difficult, partly because the model does not take into account biotic interactions, which are very important determinants of community structure at a small scale. Some inferences can be made by drawing on changes to woodland structure throughout recent and palaeoecological history, but even this is challenging. It is possible that non‐
analogue woodlands may arise consisting of species that hitherto have never been a major component of Britain’s native woodlands (e.g., species from our garden flora or introductions from overseas for forestry). It is likely, however, that the major structural elements of our woodlands will, or can, be maintained. 75
Changes to ecosystem function are possibly even harder to predict, although it will be contingent on which functions are examined (e.g., a long and developed forestry science history allows us to predict biomass productivity very well). While many functions could be maintained, some on which specialist species rely, such as specific decomposition pathways, could be more challenging and require further research. If a non‐intervention laissez faire approach is adopted then gradual community changes will be harder to predict. Where climate change is identified as a potential threat to woodland composition, structure and functioning, then there are a range of adaptation measures (Section 5.5), which could culminate in adaptive management to ensure that desired outcomes are achieved. It is also possible that the conservation of some vulnerable species may not be viable in the future and consideration should be given to non‐intervention and seeking alternative species to maintain the habitat (Section 5.6). The adoption of this approach can partly depend on whether interest is in a particular species or habitat composition or on ecosystem functioning. As this research has shown, there are still a number of research questions to be addressed especially in relation to the impact of climate change on woodland structure and functioning. One promising approach for understanding ecosystem change and its consequences for functioning and ecosystem services is the use of traits. This offers an opportunity to understand how communities assemble, which is particularly useful for climate change studies when the future pool of suitable species may be novel or unknown. For more complex interactions between species at different trophic levels, ecological networks can provide insights into the potential changes on food webs, plant‐pollinator networks and host‐parasitoid networks and they can be incorporated into bioclimatic envelope models using a conceptual framework. Alternatively, hybrid models which are built on species’ physiological responses can also take account of species interactions. For all these approaches data limitations are a serious issue and further laboratory and field‐based research and long‐term monitoring is needed. Based on BEM outputs this report has highlighted those PH which are considered most vulnerable to climate change, and has shown that while it is possible to give indicative outcomes, especially at the species level, there is still much to discover about the more complex habitat level. Also, using the literature, it has qualitatively explored the implications for woodland composition, structure and functioning. This is an area for much fruitful further work, both to increase our understanding of how ecosystems operate and how they affect the delivery of ecosystem services. 76
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8.2. Modelling results for 8 climate change scenarios Percent change bioclimate space in British Isles for each scenario Species Trees Acer campestre Acer pseudoplatanus Alnus glutinosa Alnus incana Betula pendula Betula pubescens Buxus sempervirens Carpinus betulus Fagus sylvatica Fraxinus excelsior Ilex aquifolium Juniperus communis Malus sylvestris Pinus sylvestris Populus tremula Prunus avium Prunus padus Pyrus pyraster Quercus petraea Quercus robur Salix caprea Salix cinerea Sorbus aria Sorbus aucuparia Sorbus domestica Sorbus torminalis Taxus baccata Tilia cordata Tilia platyphyllos Ulmus glabra Shrubs Calluna vulgaris Cornus sanguinea Corylus avellana Crataegus laevigata Crataegus monogyna Erica cinerea Erica tetralix Euonymus europaeus Ligustrum vulgare Prunus spinosa Rhamnus cathartica Ribes uva‐crispa Rosa canina Had A2 2020 Had A2 2050 Had A2 2080 Had B1 2050 Had B1 2080 PCM A2 2020 PCM A2 2050 PCM A2 2080 Field Maple 61 62 38 52 46 50 47 29 Sycamore ‐6 ‐20 ‐31 ‐19 ‐24 0 ‐5 ‐18 Alder Grey Alder Silver birch Downy birch 8 ‐78 0 3 6 ‐100 ‐10 ‐6 ‐1 ‐100 ‐53 ‐50 4 ‐89 ‐3 ‐7 ‐1 ‐97 ‐16 ‐24 7 ‐73 ‐1 2 3 ‐100 ‐9 ‐6 ‐11 ‐100 ‐23 ‐30 Common box 206 256 280 254 272 150 149 134 18 5 ‐52 11 ‐4 12 9 ‐4 11 8 1 9 4 1 ‐2 ‐26 1 7 3 1 5 ‐5 1 10 8 1 7 3 1 ‐2 ‐15 1 ‐3 ‐7 ‐21 ‐7 ‐10 ‐3 ‐9 ‐22 17 14 2 11 ‐49 56 6 13 3 ‐3 86 0 1128 112 10 39 215 ‐11 15 ‐39 ‐3 9 ‐77 54 ‐0 10 ‐16 ‐24 93 ‐4 1430 141 11 29 250 ‐40 7 ‐78 ‐18 0 ‐97 7 ‐30 ‐14 ‐61 ‐73 99 ‐17 1632 130 8 ‐20 271 ‐71 13 ‐19 ‐3 6 ‐65 61 0 8 ‐12 ‐20 93 ‐4 1268 121 10 28 246 ‐35 9 ‐47 ‐10 3 ‐80 63 ‐7 0 ‐32 ‐38 96 ‐10 1337 124 10 16 272 ‐48 16 22 1 10 ‐39 49 6 13 3 ‐1 66 0 840 84 9 32 171 ‐4 14 ‐31 ‐5 5 ‐64 51 1 11 ‐5 ‐16 61 ‐6 865 96 8 16 191 ‐20 ‐1 ‐67 ‐24 ‐9 ‐87 51 ‐13 1 ‐36 ‐47 50 ‐24 744 91 6 ‐16 202 ‐55 Heather Dogwood Hazel 15 19 2 12 23 ‐0 8 25 ‐7 9 20 ‐0 6 21 ‐2 13 18 2 7 22 ‐2 ‐7 22 ‐12 English Hawthorn 14 14 4 19 21 7 1 ‐12 Hawthorn 4 3 2 2 2 4 2 ‐5 Bell heather Cross‐leaved heath 1 1 ‐0 1 0 1 0 ‐2 0 0 ‐0 0 0 0 0 0 Spindle 26 29 13 28 27 21 26 17 European privet Blackthorn Common buckthorn Gooseberry Dog rose 13 10 14 5 14 ‐10 12 4 11 ‐0 11 9 11 4 5 ‐12 25 28 19 27 26 18 17 11 0 4 ‐23 2 ‐60 ‐2 ‐18 2 ‐36 0 0 3 ‐21 ‐1 ‐57 ‐10 Common name European hornbeam Beech Ash English holly Juniper Crab apple Scots pine Aspen Wild cherry Bird cherry Wild fruit trees Sessile Oak Common oak Goat willow Grey sallow Whitebeam Rowan Service Tree Wild Service Tree English Yew Small‐leaf lime Large‐leaf lime Scots Elm 104
Percent change bioclimate space in British Isles for each scenario Species Rubus fruticosus Rubus idaeus Ulex europaeus Ulex gallii Vaccinium vitis‐
idaea Mammals Apodemus flavicollis Barbastella barbastellus Capreolus capreolus Lepus europaeus Lutra lutra Muscardinus avellanarius Myotis bechsteinii Pipistrellus pipistrellus Rhinolophus hipposideros Sciurus vulgaris Vulpes vulpes Lichens & Mosses Biatoridium monasteriense Caloplaca luteoalba Cladonia_botrytes Schismatomma graphidioides Sphagnum cuspidatum Thelenella modesta Forbs Agrostis capillaris Ajuga reptans Allium ursinum Anemone nemorosa Angelica sylvestris Anthoxanthum odoratum Arrhenatherum elatius Arum maculatum Blechnum spicant Brachypodium sylvaticum Bromus ramosa Campanula latifolia Carex elata Had A2 2020 Had A2 2050 Had A2 2080 Had B1 2050 Had B1 2080 PCM A2 2020 PCM A2 2050 PCM A2 2080 Blackberry Wild red raspberry Gorse Western gorse ‐10 ‐36 ‐77 ‐28 ‐52 ‐5 ‐19 ‐50 4 ‐16 ‐57 ‐13 ‐31 5 ‐9 ‐50 2 11 3 11 3 ‐0 2 4 1 ‐3 2 14 3 17 4 20 Cowberry ‐33 ‐53 ‐70 ‐46 ‐56 ‐29 ‐44 ‐63 Yellow‐necked mouse 20 ‐24 ‐44 ‐16 ‐26 16 ‐26 ‐60 Barbastelle Bat 121 152 182 144 161 108 134 160 Roe deer 55 47 ‐24 54 33 36 31 7 Brown Hare Otter ‐10 ‐0 ‐17 ‐0 ‐37 ‐1 ‐2 ‐2 3 ‐3 ‐9 ‐0 ‐6 ‐0 15 ‐2 Dormouse 35 ‐6 ‐70 30 23 46 52 68 Bechstein`s Bat 144 174 128 171 187 104 133 158 2 1 ‐2 1 0 2 1 ‐7 124 156 183 142 159 105 135 158 110 3 131 3 121 ‐6 130 3 134 2 89 3 119 3 148 ‐1 ‐30 ‐56 ‐85 ‐50 ‐67 ‐25 ‐47 ‐74 Common name Pipistrelle Bat Lesser Horseshoe Bat Red Squirrel Red fox a lichen Orange fruited elm lichen Stump lichen 7 7 ‐14 5 ‐1 7 5 ‐0 ‐20 ‐100 ‐100 0 ‐20 ‐20 ‐100 ‐100 a lichen ‐9 ‐15 ‐21 ‐15 ‐18 ‐4 ‐7 ‐10 Bog‐moss ‐0 ‐9 ‐32 ‐8 ‐18 0 0 ‐1 Warty wax lichen 0 3 5 2 3 0 2 3 Common bent‐
grass Bugle Wild Garlic ‐0 ‐1 ‐3 ‐1 ‐2 0 ‐1 ‐6 9 12 11 9 10 ‐16 10 9 11 2 9 11 11 9 12 4 Wood anemone 6 2 ‐19 2 ‐4 5 3 ‐5 Wild Angelica Sweet vernal grass ‐5 ‐15 ‐41 ‐13 ‐22 ‐5 ‐15 ‐31 0 0 0 0 0 0 0 0 Tall oatgrass 4 ‐1 ‐15 ‐3 ‐12 3 ‐4 ‐26 Lords and Ladies Deer fern Slender Flase brome Hairy brome 13 0 12 ‐0 ‐24 ‐1 7 ‐0 ‐2 ‐1 11 ‐0 9 ‐0 ‐4 ‐0 13 13 10 12 10 13 11 2 11 7 ‐4 5 0 9 2 ‐12 Bellflower ‐38 ‐57 ‐85 ‐54 ‐67 ‐30 ‐53 ‐76 Tufted sedge 10 16 9 14 16 9 16 23 105
Percent change bioclimate space in British Isles for each scenario Species Carex paniculata Carex sylvatica Chrysosplenium oppositifolium Circaea lutetiana Clematis vitalba Conopodium majus Convallaria majalis Cypripedium calceolus Deschampsia cespitosa Deschampsia flexuosa Digitalis purpurea Dryopteris filix‐
mas Elymus caninus Empetrum nigrum Eriophorum angustifolium Eriophorum vaginatum Euphorbia amygdaloides Festuca gigantea Filipendula ulmaria Fragaria vesca Galium aparine Galium odoratum Geranium robertianum Geum urbanum Glechoma hederacea Glyceria maxima Gymnocarpium robertianum Hedera helix Heracleum sphondylium Holcus lanatus Hyacinthoides non‐scripta Hypericum maculatum Hypericum perforatum Lamiastrum galeobdolon Linnaea borealis Listera cordata Had A2 2020 Had A2 2050 Had A2 2080 Had B1 2050 Had B1 2080 PCM A2 2020 PCM A2 2050 PCM A2 2080 34 34 24 31 28 29 26 13 19 14 ‐9 11 5 17 9 ‐12 0 ‐0 ‐4 0 ‐0 0 0 ‐0 4 4 ‐2 3 2 4 4 1 39 54 67 50 57 33 46 62 Earth Chestnut 0 0 ‐0 0 ‐0 0 0 0 Lily of the valley 153 216 230 212 245 108 127 148 Lady`s Slipper Orchid ‐100 ‐100 ‐100 ‐100 ‐100 ‐100 ‐100 ‐100 Tufted hair grass 3 ‐2 ‐17 ‐3 ‐8 2 ‐5 ‐23 Wavy hair grass 0 0 0 0 0 0 0 0 Purple foxglove Common male fern Bearded Couch Crowberry Common cottongrass Hare's‐tail cotton grass 0 0 0 0 0 0 0 0 ‐0 ‐5 ‐25 ‐5 ‐11 ‐1 ‐7 ‐19 38 ‐26 2 ‐41 ‐71 ‐63 8 ‐38 ‐29 ‐46 29 ‐19 2 ‐30 ‐46 ‐46 ‐1 ‐6 ‐32 ‐6 ‐13 ‐1 ‐2 ‐11 7 ‐1 ‐42 ‐2 ‐14 6 ‐3 ‐22 Wood spurge 95 121 141 111 124 87 102 127 Giant Fescue 26 14 ‐39 17 0 21 17 1 Meadow sweet 0 ‐2 ‐25 ‐2 ‐10 0 0 ‐1 ‐0 ‐2 ‐14 ‐1 ‐4 ‐0 ‐3 ‐12 1 9 ‐1 ‐2 ‐6 ‐33 ‐1 5 ‐3 ‐2 1 8 ‐2 ‐0 ‐9 ‐19 Herb robert 2 2 1 2 1 2 2 1 Wood Avens 1 ‐5 ‐18 ‐5 ‐8 0 ‐8 ‐21 Ground ivy 16 16 ‐0 16 11 14 15 13 Reed sweet grass 8 ‐4 ‐49 ‐1 ‐7 9 8 ‐1 1260 1144 344 1308 960 792 812 652 English ivy 5 5 3 4 3 5 4 ‐4 Hogweed ‐14 ‐30 ‐70 ‐26 ‐41 ‐12 ‐25 ‐43 Yorkshire fog 3 0 ‐6 ‐1 ‐4 3 ‐2 ‐14 Bluebell 1 1 ‐1 1 0 1 1 1 ‐24 ‐59 ‐87 ‐51 ‐64 ‐14 ‐36 ‐68 15 15 12 13 12 13 12 4 Yellow archangel 65 80 40 84 82 49 70 102 Twinflower Lesser twayblade ‐78 ‐28 ‐94 ‐44 ‐100 ‐68 ‐83 ‐41 ‐91 ‐54 ‐71 ‐22 ‐85 ‐37 ‐97 ‐61 Common name Greater Tussock Sedge Wood sedge Opposite leave golden saxifrage Enchanters Nightshade Old Man's beard Woodland strawberry Cleavers Sweet woodruff Limestone Fern Imperforate St. John's‐wort Common St Johns wort 106
Percent change bioclimate space in British Isles for each scenario Species Lonicera periclymenum Luzula sylvatica Melampyrum pratense Melampyrum sylvaticum Melica uniflora Mercuralis perennis Molinia caerula Orchis mascula Oxalis acetosella Paris quadrifolia Parnassia palustris Phragmites australis Phyllitis scolopendrium Poa nemoralis Poa trivialis Polypodium vulgare Potentilla sterilis Primula elatior Primula vulgaris Prunella vulgaris Pteridium aquilinum Ranunculus ficaria Rumex acetosella Rumex sanguineas Sanicula europaea Silene dioica Stachys sylvatica Stellaria holostea Teucrium scorodonia Trichomanes speciosum Trollius europaeus Urtica dioica Valeriana dioica Veronica montana Vicia sylvatica Viola reichenbachiana Viola riviniana Birds Caprimulgus europaeus Had A2 2020 Had A2 2050 Had A2 2080 Had B1 2050 Had B1 2080 PCM A2 2020 PCM A2 2050 PCM A2 2080 ‐1 ‐3 ‐14 ‐3 ‐6 ‐1 ‐3 ‐16 0 0 0 0 0 0 0 0 0 0 ‐7 0 ‐0 0 0 0 Small Cow‐wheat ‐54 ‐92 ‐100 ‐82 ‐92 ‐43 ‐76 ‐98 Wood melick 17 13 3 11 7 14 9 ‐3 Dog's mercury 18 18 6 16 11 16 15 10 Purple Moor grass Early purple orchid Wood sorrel Herb paris Grass of Parnassus 4 1 ‐8 ‐0 ‐4 3 ‐3 ‐16 10 9 5 8 8 9 8 2 2 1235 ‐2 1670 ‐31 1105 ‐2 1695 ‐10 1645 1 780 ‐4 1165 ‐18 1795 ‐2 ‐31 ‐78 ‐29 ‐57 ‐1 ‐26 ‐68 Common reed 1 2 1 1 0 1 2 1 Harts tongue fern 2 2 1 1 1 2 2 1 Wood bluegrass Ryegrass Common polybody Barren Strawberry Oxlip Primrose Selfheal 25 7 11 2 ‐29 ‐14 13 ‐1 ‐1 ‐9 21 5 8 ‐3 ‐21 ‐24 42 42 37 39 39 34 30 21 1 174 0 2 1 197 0 0 ‐2 171 0 ‐5 1 242 0 ‐0 0 276 0 ‐2 1 92 0 2 1 35 0 ‐2 ‐2 0 0 ‐10 Bracken 0 1 ‐1 1 1 0 0 ‐3 Lesser celadine Sheep sorrell 25 1 28 0 19 ‐4 27 0 26 ‐1 18 1 17 ‐0 11 ‐6 Bloody dock 7 6 ‐6 5 2 7 5 ‐1 Wood Sanicle Red campion Hedge woundhort Greater Stitchwort 13 12 7 6 7 ‐1 ‐15 ‐16 ‐16 5 7 ‐1 ‐2 ‐2 ‐5 11 10 5 4 6 ‐3 ‐11 2 ‐17 9 8 ‐7 7 4 9 11 13 Wood sage 2 2 2 2 2 2 3 2 Killarney fern ‐2 ‐3 ‐5 ‐3 ‐5 ‐1 ‐1 ‐1 Globeflower Stinging nettle Marsh valerian Speedwell Wood Vetch ‐14 0 16 6 ‐45 ‐46 ‐2 19 6 ‐58 ‐84 ‐8 14 ‐4 ‐78 ‐47 ‐2 17 5 ‐25 ‐68 ‐4 18 5 ‐25 ‐6 ‐1 9 6 ‐45 ‐38 ‐5 4 6 ‐60 ‐70 ‐12 ‐7 3 ‐83 Early dog violet 26 30 20 28 28 18 20 10 Common dog violet ‐1 ‐2 ‐7 ‐2 ‐4 ‐1 ‐4 ‐10 162 561 816 1256 1269 1207 1311 1435 Common name Honeysuckle Greater Wood Rush Common cow wheat Nightjar 107
Percent change bioclimate space in British Isles for each scenario Species Carduelis cannabina Ficedula hypoleuca Jynx torquilla Lanius collurio Loxia scotica Muscicapa striata Had A2 2020 Had A2 2050 Had A2 2080 Had B1 2050 Had B1 2080 PCM A2 2020 PCM A2 2050 PCM A2 2080 1 1 2 2 2 2 2 2 Pied flycatcher ‐25 ‐61 ‐95 ‐98 ‐98 ‐98 ‐100 ‐100 Wryneck Red‐Backed Shrike Scottish Crossbill Spotted Flycatcher Tree Sparrow Bullfinch ‐80 4840 8920 13780 14520 11780 12780 17740 33 260 1340 ‐100 ‐100 ‐100 ‐100 ‐100 ‐32 ‐51 ‐70 ‐63 ‐70 ‐34 ‐46 ‐59 ‐6 ‐15 ‐27 ‐12 ‐16 0 ‐13 ‐25 1 ‐0 4 ‐10 3 ‐27 26 ‐65 26 ‐68 25 ‐39 26 ‐36 26 ‐32 30 75 90 149 173 104 165 217 ‐29 ‐57 0 ‐100 ‐57 ‐85 ‐4 ‐100 ‐84 ‐100 ‐32 ‐100 ‐37 ‐13 ‐43 ‐100 ‐54 ‐37 ‐49 ‐100 ‐7 19 ‐11 ‐100 ‐48 ‐42 ‐12 ‐100 ‐74 ‐81 ‐11 ‐100 12 13 ‐7 ‐50 ‐49 ‐51 ‐42 ‐37 2077 2062 100 2669 2192 985 2408 2562 42 96 91 ‐6 52 113 115 ‐6 63 116 146 ‐7 51 108 105 ‐6 58 113 122 ‐5 38 76 81 ‐6 49 83 105 ‐6 63 81 140 ‐6 ‐39 ‐62 ‐79 ‐58 ‐72 ‐33 ‐54 ‐78 ‐72 ‐94 ‐100 ‐85 ‐93 ‐58 ‐84 ‐98 ‐31 ‐50 ‐78 ‐48 ‐63 ‐25 ‐40 ‐73 ‐28 ‐49 ‐73 ‐49 ‐60 ‐15 ‐32 ‐62 287 354 349 353 395 248 303 379 ‐8 ‐38 ‐86 ‐10 ‐26 ‐1 ‐8 ‐23 Common name Linnet Passer montanus Pyrrhula pyrrhula Streptopelia Turtle Dove turtur Tetrao tetrix Black Grouse Tetrao urogallus Capercaillie Turdus philomelos Song Thrush Turdus pilaris Fieldfare Butterflies, beetles, ants and amphibia Boloria Pearl‐bordered euphrosyne Fritillary Carterocephalus Chequered palaemon Skipper Erynnis tages Dingy skipper Ochlodes venata Large Skipper Lucanus cervus Stag Beetle Andrena ferox a Mining Bee Scottish Wood Formica aquilonia Ant Narrow‐headed Formica exsecta Ant Formica lugubris Hairy wood ant Southern wood Formica rufa ant Rana lessonae Pool Frog Great Crested Triturus cristatus Newt 108