Download Does dispersal capacity matter for freshwater biodiversity under

Freshwater Biology (2016)
doi:10.1111/fwb.12874
Does dispersal capacity matter for freshwater biodiversity
under climate change?
ALEX BUSH*,† AND ANDREW J. HOSKINS‡
*Macquarie University, Sydney, NSW, Australia
†
Canadian Rivers Institute, University of New Brunswick, Fredericton, NB, Canada
‡
CSIRO Land and Water, Canberra, ACT, Australia
SUMMARY
1. Freshwater ecosystems appear to be sensitive to even minor climatic shifts, and the dendritic
nature of rivers as well as patchy distribution of habitats within the terrestrial landscape could limit
the ability of species to track suitable climate conditions. Although the importance of dispersal is
recognised in theory, there is great uncertainty when quantifying the capacity of species to shift their
distributions in response to climate change.
2. The influence of dispersal capacity on species’ vulnerability to climate change was assessed, using the
modelled projections of 527 freshwater species in New South Wales (NSW), Australia. Species’ future
ranges were calculated by iteratively identifying colonisation of accessible habitats and loss of suitable
habitats within network models. The accessibility of new habitats was based on a given dispersal mode
(aquatic, semi-terrestrial and aerial). The relative impact of dispersal parameters on projected range were
evaluated alongside other known sources of uncertainty (climate and emissions scenarios, modelling
algorithm and biological group), analysed collectively in a generalised additive mixed-model, and
spatially to locate regions of NSW where projections are associated with the most uncertainty.
3. Our simulations (1.4 million scenario combinations) suggest at least a third of species will lose
more than half their range under climate change. Nevertheless, we emphasise the broad uncertainty
that any average encapsulates. Dispersal capacity only had a minor impact on projected range shifts
relative to other modelling assumptions but the network-pathways and maps of uncertainty have
value for conservation planning at large scales. Projected range losses initially decreased rapidly as
dispersal rates increased but the benefits are reduced above 2–3 km year 1. Taxa restricted to
dispersal within the stream network (aquatic) were more vulnerable to climate change than taxa
with semi-terrestrial or aerial dispersal and maps of variation due to dispersal mode and rate
indicate where habitat connectivity would be most beneficial.
4. This study demonstrates the breadth of uncertainties that challenge plans for improving ecosystem
adaptation under climate change and highlights where in the landscape those differences were
consistent. We emphasise the need for freshwater conservation studies to be ecologically
representative, to focus on broad-scale connectivity for taxa that can move between catchments, and an
accessible network of refugia for taxa with more limited dispersal.
Keywords: environmental niche model, generalised additive mixed-effects models, graph network, species
distribution modelling, uncertainty
Introduction
There have been significant declines in global freshwater
biodiversity as a result of the intensive and growing
exploitation of water resources, flow modification,
habitat destruction, pollution and introduced species
(Allen et al., 1999; Dudgeon et al., 2006). Although it is
very likely these stressors will continue to be the dominant threats to freshwater ecosystems (V€
or€
osmarty et al.,
2010; Kuemmerlen et al., 2015b), climate change will
Correspondence: Alex Bush, Canadian Rivers Institute, University of New Brunswick, Fredericton, E3B 5A3 NB, Canada. E-mail: alexalbush@
gmail.com
© 2016 John Wiley & Sons Ltd
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A. Bush & A. J. Hoskins
have significant additional impacts across entire landscapes (Dudgeon, 2010; Kingsford, 2011). As a result,
management agencies are increasingly aware that
actions will be required to aid adaptation of biodiversity
under climate change and avoid local extinctions (Lukasiewicz, Finlayson & Pittock, 2013, 2014). However, the
merits of particular management actions are difficult to
assess due to the uncertainty involved when assessing
vulnerability to climate change (Buisson et al., 2010;
Beale & Lennon, 2012). This study examines the factors
influencing projected outcomes in New South Wales
(NSW) Australia.
Species distribution models (SDMs) are one of the
most prevalent methods being used to assess the potential impacts of climate change on species and guide conservation planning (Wiens et al., 2009; Kujala et al., 2013;
Maggini et al., 2013). However, many decisions in the
modelling process can influence outcomes, including the
choices of environmental variables used (Synes &
Osborne, 2011), presence/absence threshold (Liu, White
& Newell, 2013), modelling algorithm (Elith et al., 2006),
model complexity (Warren et al., 2011), and for future
climate data, the emission scenario and global climate
model (GCM) used (Buisson & Grenouillet, 2010; James
et al., 2013). In addition, an important limitation of most
studies is that realistic rates and modes of dispersal are
not accounted for (Saupe et al., 2012), and therefore projections indicate the overall shift in favourable climates,
but not the extent to which these would be accessible
(Reside, Vanderwal & Kutt, 2012). Model training is also
heavily influenced by the geographic extent of the analysis which should reflect the region available to the species (Elith, Kearney & Phillips, 2010), and therefore
relates to the assumptions behind species’ dispersal
capacity (Barbet-Massin et al., 2012). As there is no universal agreement on an optimal modelling strategy,
users are strongly advised to be mindful of model
uncertainty (Wiens et al., 2009) but without being quantified, multiple sources of potential error can contribute to
a sense of mounting uncertainty in all projections posing
challenges for decision-making. Thus, it is important to
understand which factors are most influential, so we can
assess confidence in a particular model, and whether the
spatial distribution of uncertainty could potentially alter
preferred management actions (Kujala et al., 2013;
Wright et al., 2015).
As the availability of fine-resolution geographic data
relevant to freshwater ecosystems has increased (e.g.
hydrology or fine scale topography), the number of
SDM applications to freshwater taxa has rapidly
expanded (Elith et al., 2011; Kuemmerlen, Petzoldt &
Domisch, 2015a). However, a number of challenges
remain, such as correcting for patchy and biased species
records within the nested spatial structure of river systems and combining information from multiple sources
at different temporal and spatial scales (Domisch et al.,
2015). Large-scale distribution patterns such as shifts in
species ranges due to climate change can be approximated by broad-scale predictors (Comte et al., 2012;
Domisch et al., 2012; Simaika et al., 2013), but species
ranges can still be overestimated if modelling decisions
and environmental variables are not appropriate to
freshwater systems (Domisch et al., 2013).
Dispersal is a particularly important process in freshwater systems due to their patchy or structured distribution (Heino, 2011; Gr€
onroos et al., 2013). Over shorter
time-scales, dispersal capacity limits the successful recolonization of sites following restoration (Tonkin et al.,
2014), and has clear implications for taxa expected to
expand their ranges under climate change (Hein,
€
Ohlund
& Englund, 2011; Comte et al., 2012). Unfortunately, quantitative estimates of dispersal capacity are
lacking for most species meaning their ability to track
shifting climates in the future remains highly uncertain
(Driscoll et al., 2014). This study aims to establish
whether dispersal capacity in freshwater taxa is likely to
affect management planning decisions by identifying (i)
what differences in dispersal capacity have the greatest
impact on their vulnerability to climate change, (ii)
how important dispersal capacity is likely to be versus
other sources of uncertainty in model projections,
and (iii) where different sources of uncertainties are
concentrated?
In Australia, significant temperature increases and
reductions in rainfall (Hobday & Lough, 2011) are likely
to alter habitat suitability for many species and a number of studies have applied SDMs to freshwater taxa,
including fish (Bond et al., 2011), crayfish (James et al.,
2013), and Odonata (Bush et al., 2014). The capacity for
freshwater communities to naturally adapt to climate
change is a particular concern in New South Wales
because the coastal rivers flow eastwards and do not
encompass a large latitudinal range that may restrict
movement to track suitable climatic conditions (Turak
et al., 2011; Bush et al., 2012). We employ a network-flow
approach inspired by Williams et al. (2005) to track suitable dispersal pathways under changing environmental
conditions. The process was repeated, using a variety of
modelling assumptions (emissions scenarios, climate
models, modelling algorithms, dispersal rates and dispersal mode) to examine the influence of each assumption on the extent of ‘accessible’ future habitat. Finally,
© 2016 John Wiley & Sons Ltd, Freshwater Biology, doi: 10.1111/fwb.12874
Dispersal ability and projected impacts of climate change
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to identify biodiversity patterns that are not biased spatially due to taxonomy, we applied the analysis to 527
species across a wide variety of ecological groups that
also represent the breadth of dispersal modes present.
Methods
Species data
The study used occurrence records collated from a wide
range of sources, including state monitoring programs,
museum collections, federal government survey records,
local catchment authorities, scientific literature and private collectors (see Appendix S1). The final assessment
included models of 109 species of Odonata, 62 species of
Hemiptera, 92 frogs, 44 crayfish, 57 fish, 118 aquatic or
semi-aquatic plants and 45 freshwater molluscs. Modelled species represented a high proportion of the
group’s regional diversity (83–100%), although plants
(c. 70%) and molluscs (c. 20%) were less complete. The
primary reason for including many species was to
account for differences in habitat requirements and
range sizes, and to reduce the effect of taxonomic bias on
the estimated spatial distribution of biodiversity. We
would have liked to include a broader range of invertebrate taxa but major biases in their collection and recording meant they were unsuitable for spatial modelling.
The study focused on species with at least one record
within an NSW catchment, including those catchments in
which the state has a management role (Snowy, Genoa,
and Murray–Darling) (Fig. 1). Each species modelled also
needed to have been recorded in 15 or more sub-catchments in Australia. Although the majority of records were
collected within the last 20 years (95%), records as far
back as 1960 were also used if native vegetation at those
sites was still intact, and the species had not been
recorded in more than 14 sub-catchments more recently.
Environmental data
Organising the modelling environment and predictor
variables to reflect the structure of a freshwater system
is important when predicting habitat suitability because
it can influence the accuracy of freshwater SDMs without necessarily affecting performance metrics (Domisch
et al., 2013). Therefore, rather than using gridded data,
models were based on the stream network from the
Australian Hydrological Geospatial Fabric (Geofabric,
2011) that included upstream and downstream connectivity, catchment topography and geological composition. Climatic and hydrological parameters (mean
Fig. 1 Point density of occurrence data in Australia for freshwater
species found in New South Wales, including the boundaries of the
study catchments.
annual flow, dry-season length) as well as forecast for
each parameter under climate change were provided at
the same scale by James et al. (2013). Finally, given
bivalve larvae may be more dependent on the community of fish present than the environmental conditions
themselves, we added a species-specific estimate of fish
community dissimilarity (Lois et al., 2014). Using the
projected habitat suitability of all modelled fish, the
mean dissimilarity (Bray–Curtis Index) was calculated
between each location and all recorded sites. The importance of fish community dissimilarity to a bivalve’s distribution thus depended on whether fish communities
were similar among recorded sites, and whether they
differed at the locations of pseudo-absences.
To compare the variation among projections driven by
choice of climate models, future projections included a
wide range of plausible future scenarios. Selecting multiple GCMs can span the range of uncertainty in predicting future climates (Buisson et al., 2010), but some
studies suggest climate ensembles can perform better
simulating observed conditions (Fordham, Wigley &
Brook, 2011; Fordham et al., 2012). Therefore, in addition
to a seven-GCM mean ensemble that had performed
well for the region (Fordham et al., 2011), four further
GCMs were chosen based on their predictive performance in south-east Australia (Evans & Ji, 2012); MIROC
3.2 (med), MPI-ECHAM 5, CCCMA CGCM 3.1 and
CSIRO MK3.0. The climate change projections were
based on two representative concentration pathways
(RCPs; Van Vuuren et al., 2011) that describe a medium
© 2016 John Wiley & Sons Ltd, Freshwater Biology, doi: 10.1111/fwb.12874
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A. Bush & A. J. Hoskins
and a high emissions scenario that result in radiative
forcing reaching 6 and 8.5 W m 2, respectively, by 2100,
equivalent to global average temperatures increasing 3.0
and 4.9 °C, respectively (Rogelj, Meinshausen & Knutti,
2012). Lower emissions scenarios were considered unlikely (Peters et al., 2013).
Species distribution models
Species distributions were modelled, using five common
algorithms: generalised linear models (GLM), generalised boosted models (GBM), generalised additive models (GAM), multivariate adaptive regression splines
(MARS) and Maxent (Elith et al., 2006). Algorithms were
fitted, using 10 replicates for cross-validation using a
standard 70:30 split, weighting pseudo-absences and
presences equally, and using the default settings in the
R packages dismo (Hijmans et al., 2013) and biomod2
(Thuiller et al., 2009). The true skills statistic was used
for model evaluation (Allouche, Tsoar & Kadmon, 2006)
and to weight ensemble projections. To avoid overfitting,
the location of up to 10 000 pseudo-absences were chosen from the range of environments historically accessible to that species (Barbet-Massin et al., 2012) based on
their dispersal mode. Pseudo-absence were selected at
random, but weighted by density of other species
records in the same group to account for sampling bias.
For the purposes of model fitting, species were split into
three broad modes: ‘aquatic’ taxa dependent on standing
water (fish and molluscs), ‘semi-terrestrial’ taxa with the
some capacity to disperse overland (crayfish, frogs), and
‘aerial’ taxa with either passive or active aerial dispersal
(plants and insects). For ‘aerial’ groups, we used the
results of a previous study, that modelled odonates,
which suggested pseudo-absences be excluded from
sub-catchments more than 300 km from occurrence
records (Bush et al., 2014). Pseudo-absences for ‘semiterrestrial’ species were also selected within 300 km of
occurrence records, and from within the same catchments as occurrence records for ‘aquatic’ species (see
description of connecting pathways below). Although
restricting the extent of pseudo-absences improves
model sensitivity within a species range, it also increases
the likelihood models can extrapolate beyond species–
environment relationships (Vanderwal et al., 2009). To
limit extrapolation, projected suitability was reduced if
conditions occurred beyond the environmental limits of
the training data, either by 50% if extrapolation was necessary for only one parameter, or reduced to zero if conditions of multiple parameters extended beyond the
ranges of the training data (Elith et al., 2011).
Predictor variables were selected using the Akaike
Information Criterion (AIC) (Warren & Seifert, 2011) to
complement a set of three parameters considered central
to environmental suitability; one thermal (typically maximum or minimum temperature), one rainfall (typically
precipitation seasonality) and one hydrological (mean
annual flow). After removing strongly correlated factors
(>0.7), additional parameters were added through forward selection. Selected variables and model statistics
are provided for all species in Appendices S2 and S3.
Dispersal rate, mode and connectivity
When assessing the vulnerability of freshwater taxa to
climate change, there is a clear need to distinguish
between species with differences in dispersal ecology as
this is expected to affect both model fitting, and capacity
to track climatic shifts. Straight-line measurements of the
distances separating sites (i.e. Euclidean) are often used
for convenience but do not reflect species’ habitat
requirements or dispersal pathways (Grant et al., 2010),
a feature especially true of structured landscapes such
as river networks (Sutherland, Fuller & Royle, 2014). To
manage this issue, the distances of each sub-catchment
to all others within a 75 km radius were described based
on three dispersal modes: ‘aquatic’, ‘semi-terrestrial’ and
‘aerial’. Naturally the processes driving passive and
active dispersal are likely to differ, and the movement of
some aquatic insects and plants for instance may be better reflected by stream-distances. Whilst in some cases, it
may be possible to use more specific dispersal traits
(Radinger & Wolter, 2014), this is not true of all biological groups (Angert et al., 2011) and therefore dispersal
modes had to be generalised for each biological group
to define physical limits to the training environment
(‘background-mode’).
The ‘aquatic’ dispersal mode measured distances
between nodes of the stream network, calculated in R
using the package igraph (Csardi & Nepusz, 2006).
Stream network distances maximised the distances
between headwaters within a catchment, and disconnect
sites from sub-catchments in neighbouring catchments.
However, diadromous fish were allowed to move to
new river catchments whose outflow was within 200 km
of an occupied catchment’s river mouth; given this was
the minimum distance populations of some species must
have dispersed to colonise Tasmania (Hammer et al.,
2014). Upstream movements were considered as likely
as downstream but prevented where the location of natural barriers such as waterfalls was known (1312 listed
in eastern Australia; Geofabric, 2011). The exceptions
© 2016 John Wiley & Sons Ltd, Freshwater Biology, doi: 10.1111/fwb.12874
Dispersal ability and projected impacts of climate change
were five species of fish (Anguilla australis and A. reinhardtii, Galaxias brevipinnis, Gobiomorphus coxii and Mordacia mordax) that are able to navigate around or over
river barriers (Raadik, 2013). For the purposes of this
study, artificial barriers were not included because
the data available did not cover the majority of eastern
Australia.
Distances moved in the ‘semi-terrestrial’ dispersal
mode also followed the stream network, but further connections were added to catchment headwaters. Species
could therefore move between adjacent catchments, as
well as between headwaters within catchments separated
by large distances along the stream network. Multiple
headwater crossings were possible but, to emphasise
their lower likelihood and moderate the training extent
of some species, the real distance of these connections
was increased by 25 times. Lastly, the connectivity
among sub-catchments for ‘aerial’ species simply used
the straight-line distance between sub-catchments.
Modelling dispersal pathways in future time intervals
As originally demonstrated by Williams et al. (2005), dispersal chains were calculated for each species based on
connections between occupied sub-catchments at successive future time-slices (each decade from 2025 to 2085).
To begin with, the projected habitat suitability of current
and future conditions for a species, using a given SDM
algorithm, GCM and RCP were transformed to presence/absence maps of a species’ potential range. Thresholds were selected to optimise the true skill statistic
based on the review by Liu et al. (2013). Starting from
the occurrence records used to fit the SDM, other suitable sub-catchments were added to the species’ range if
they were accessible from those locations given the designated mode and rate of dispersal (after examining the
range spread, annual rates were aggregated to a 10-year
period to reduce computation time). This process was
repeated 10 times to establish a historic current range
equivalent to 100 years, before introducing climate
change projections. Thereafter, the distribution of suitable habitat was replaced by the projection for 2025, and
after allowing new sub-catchments to be colonised, occupied cells that had become environmentally unsuitable
were subtracted from the species’ range. This process
was repeated for each decade from 2025 to 2085 before
recording the final range.
Given the uncertainty regarding species dispersal
capacity, we simulated range shifts based on 45 different
dispersal scenarios (three dispersal modes and 15 different rates: 0.5–7.5 km year 1 in 0.5 km increments). To
5
account for other sources of uncertainty, all dispersal
scenarios were repeated for each combination of modelling algorithm (n = 6), emissions scenario (n = 2) and
climate model (n = 5); a total of 2700 runs per species,
more than 1.4 million in total. The code used to run this
analysis in R is provided in Appendix S4. The variation
in species’ final occurrence was calculated as the average
difference in the occupancy of each treatment, accounting for differences in the number of species modelled
when comparing biological groups.
Data analyses
Given the nested nature of the data and the nonlinear
impact of dispersal rate, generalised additive mixedeffects models (GAMM; via the R package mgcv;
Wood, 2004), using a Gaussian error distribution and
identity link, were used to test how much the rate of
dispersal affected species’ projected vulnerability (percentage change in extent), and the potential for this
effect to differ among dispersal modes (Wood &
Scheipl, 2014). Variation in projected range due to dispersal was placed in the context of alternative sources
of uncertainty by including the GCM and SDM algorithm as random effects, and RCP. Individual smooth
terms were fitted to dispersal rate grouped by dispersal
mode, giving a unique smooth function for each of the
three dispersal modes being assessed. An additional
factor ‘background-mode’ was used to specify the
assumed mode of dispersal that controlled selection of
pseudo-absences. We also added current range size and
altitude as fixed effects given their known influence on
species’ vulnerability to climate change and to account
for inter-specific variation. Unfortunately, with so many
factors and dispersal combinations, computational limitations meant GAMMs could not be fitted for the
whole dataset. Testing with subsets did not show a significant additional effect of ‘background-mode’ and the
effect of biological group was extremely weak so models were fitted to each biological group separately (see
Appendix S5). Model selection was based on the AIC
and relative variable importance was estimated from
marginal and conditional R2 (Nakagawa & Schielzeth,
2013).
Results
Climate change is likely to become a major threat to
freshwater biodiversity in NSW, although we emphasise
the precise degree of threat projected depends on
numerous factors. For example, averaged across all
© 2016 John Wiley & Sons Ltd, Freshwater Biology, doi: 10.1111/fwb.12874
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A. Bush & A. J. Hoskins
combinations, projected range losses were >50% used
MPI-ECHAM 5, but only 7% using MIROC 3.2 (med).
Changes in range size also tended to be greatest in species with smaller current ranges, which were more common among the crayfish and frogs, and to a lesser
extent Odonata (Fig. 2). Dispersal mode and rate also
had a significant impact on range losses. Averaged
across climate scenarios and SDM algorithms, the ranges
of 231 species (44%) were projected to decline by over
50% by 2085 (RCP 8.5) based on dispersal rates of just
0.5 km year 1, but was reduced to 39% at 2 km year 1,
and 35% at 7.5 km year 1. More than 40% of species lost
all suitable habitat in at least one projected scenario.
Given the breadth of climate scenarios, and potential
limiting dispersal constraints, few species could be
expected to consistently expand their ranges under climate change, but if each scenario was considering
equally likely then at least 26 species were more likely
to expand their range rather than decline.
Inevitably the GAMM analysis confirmed all the factors being varied in each projection had a significant
effect on the projected future change in a species range.
Naturally higher rates of dispersal enabled range declines to be offset in more cases, but those benefits diminished rapidly and dispersal rate had little further effect
above 2–3 km year 1. A similar relationship was
Fig. 2 Summary of the projected change to the range of all 527 species modelled in this study at three rates of dispersal 0.5, 2 and
7.5 km year 1. Colours indicate the number of projections, out of 180 for each species/dispersal rate combination, that predicted a particular
range shift.
© 2016 John Wiley & Sons Ltd, Freshwater Biology, doi: 10.1111/fwb.12874
Dispersal ability and projected impacts of climate change
common to each dispersal mode (Fig. 3 and
Appendix S5), but most prominent if taxa had ‘aerial’
dispersal. Range declines were not offset to the same
extent when taxa were confined to ‘aquatic’ or even
‘semi-terrestrial’ dispersal pathways, although in the latter case higher dispersal rates continued to reduce range
losses making the trend more linear.
Although in some circumstances dispersal mode and
rate could have a significant effect on the projected
range of a species under climate change, their relative
importance was relatively low (5 and 1.5%, respectively).
By comparison inter-specific differences in current range
were responsible for 32.5% of the variation, GCMs for
29%, current mean altitude 16%, modelling algorithm
(a)
(b)
(c)
Fig. 3 Predictions of the percentage change in the range of Odonata (green), crayfish (purple) and frogs (red) due to climate
change by 2085 (RCP 8.5), in response to increasing rates of dispersal. Predictions are split between the three dispersal modes (a)
aquatic, (b) semi-terrestrial and (c) aerial, and assume the same
mean current range size and altitude. Colour intensity is weighted
by the standard error of the generalised additive mixed-effects
model predictions. Other biological groups were not plotted due to
overlaps, but are available in Appendix S5.
7
9% and RCP 6.5%. The relative importance of modelling
factors was consistent among biological groups, but varied most (6%) for current range size. Although many
species could shift their distribution to higher elevations
with just low rates of dispersal, species currently at
higher altitudes were more vulnerable under climate
change because the area of remaining suitable habitat
declined rapidly. The GAMMs for crayfish and frogs
suggest they are particularly vulnerable to climate
change, which is likely to be because they tend to have
smaller current ranges at higher elevations, and hence
narrower environmental tolerances.
The influence of modelling assumptions also showed
different spatial patterns. For example, the projected
range shifts of Euastacus spinifer (a parastacid spiny crayfish endemic to NSW) could vary greatly with the choice
of GCM and SDM algorithm but there can also be very
clear constraints placed by dispersal pathways, and at a
finer scale still, by dispersal rates (Fig. 4). Whether
catchment headwaters at higher elevations would
remain suitable for E. spinifer was highly dependent on
the projection GCM, whereas the suitability of coastal
lowlands for this species varied depending on the modelling algorithm. Southern, and to some extent northern,
range expansion was dependent on the dispersal mode
and the ability to travel large distances across catchment
boundaries. Finally, the greatest differences in occupancy due to the dispersal rate were not at expanding
range margins, but around the perimeter of the current
range. Though high rates of dispersal may be needed to
access distant sites, this pattern was common because
the dispersal rate was immaterial when on average those
sub-catchments were not projected to be environmentally suitable.
For NSW as a whole, climate change was projected to
shift freshwater diversity east towards the more mesic
coastal catchments, although gains were still possible at a
number of locations in the south of the Murray–
Darling (Fig. 5). The number of species that could persist
at high elevations was heavily dependent on the GCM
used, whereas the differences due to RCP and the modelling algorithm were more evenly distributed, and still
reflecting the overall bias in richness regionally. However, the greatest differences in the richness of a particular area were contingent on which ecological group was
used, especially along the coastal fringe. Given all these
differences, those due to dispersal are interesting as they
do not follow trends in richness. As with the maps of
E. spinifer, the differences due to the dispersal mode indicate where taxa with dispersal limitations are most likely
to be excluded from, and therefore this is most common
© 2016 John Wiley & Sons Ltd, Freshwater Biology, doi: 10.1111/fwb.12874
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A. Bush & A. J. Hoskins
(a)
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Fig. 4 Mean occupancy of 1350 projections of Euastacus spinifer in sub-catchments of coastal New South Wales under a) current, and b)
future climate conditions (2085 RCP 8.5). Catchment boundaries are marked in black. The following maps show the mean difference in projected future occupancy of E.spinifer due to choices of c) GCM (n=5), d) modelling algorithm (n=6), e) dispersal mode (n=3) and f) dispersal
rate (n=15).
© 2016 John Wiley & Sons Ltd, Freshwater Biology, doi: 10.1111/fwb.12874
Dispersal ability and projected impacts of climate change
(a)
(b)
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Fig. 5 Mean occupancy of 2700 projections for each of 527 freshwater species modelled in catchments of New South Wales (outlined in
black); under (a) current and (b) future climate conditions (2085), and (c) the difference between the (a) and (b). Maps (d–i) then show
the mean difference in occupancy of each sub-catchment due to choices of (d) global climate model (n = 5), (e) representative concentration pathway (n = 2), (f) modelling algorithm (n = 6), (g) biological group (n = 7), (h) dispersal mode (n = 3) and (i) dispersal rate
(n = 15).
along the parts of the coast with multiple small separated
catchments. Low mean differences in the projected ranges
of taxa due to limitations of dispersal rate were present
across the entire landscape, but do suggest that high rates
of dispersal would be necessary to access the remaining
suitable habitat across the different divisions of the Murray–Darling basin.
Discussion
This study showed that the dispersal capacity, in particular the dispersal mode, influences the outcome of species’ vulnerability assessments against a backdrop of
many known uncertainties with modelling methods and
climate projections (Buisson et al., 2010; Porfirio et al.,
© 2016 John Wiley & Sons Ltd, Freshwater Biology, doi: 10.1111/fwb.12874
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A. Bush & A. J. Hoskins
2014; Wright et al., 2015). Naturally higher rates of dispersal could reduce the vulnerability of some taxa, but
on average it only helped offset losses up to a rate of
approximately 2–3 km year 1; with some further expansion possible in ‘semi-terrestrial’ dispersal scenarios. The
inclusion of altitude in the GAMM, and moderate to
severe projected range losses of many upland taxa,
including crayfish (Mccormack, 2012), galaxiid fishes
(Raadik, 2013) and Odonata (Bush et al., 2014), suggest
that following suitable environmental conditions up altitudinal gradients cannot compensate for widespread
declines. Likewise, higher dispersal capacity allowed
taxa dispersing via ‘aquatic’ pathways to access more
distant suitable habitats along stream networks, but the
opportunity to offset losses elsewhere were limited by
catchment boundaries (Morrongiello et al., 2011).
Dispersal rate had a relatively minor influence on climate change projections because it moderated the area
of suitable sub-catchments likely to be accessible
regionally, an issue that is only important if suitable
habitats are present in the first place and inherently
dependent on other factors that affect the model globally like GCM and algorithm (Buisson et al., 2010;
Wright et al., 2015). The GAMMs suggest range losses
could be significantly reduced by advancing range
shifts around 2–3 km year 1, well within the range
already observed for some freshwater taxa in response
to climate change (Hickling et al., 2006; Hassall, 2015).
However, to be sustained for 70 years (2015–2085)
would still require the taxa to shift the leading edge of
their distribution between 140 and 210 km. This rate of
dispersal also assumes shifts are maintained despite
differences in landscape permeability and artificial barriers (Watling & Braga, 2015), information that could
be easily incorporated into the dispersal-network
model, but represented further assumptions beyond the
scope of this study. As a result, there are potentially
many taxa with low mobility that are likely to face severe declines due to climate change. For example, in a
rare case where dispersal was directly recorded, using
a chemical isotope on a stonefly in the USA for 2 years,
no individuals were recorded more than 1 km from the
stream channel (Macneale, Peckarsky & Likens, 2005).
Unfortunately, this remains an isolated study and even
generalising dispersal-related traits for well-known
groups would be difficult (Angert et al., 2011). When
considering future research, it is this uncertainty at the
slow end of the dispersal spectrum that should be of
greatest concern (Driscoll et al., 2014).
Dispersal mode had a greater impact than dispersal
rate on future projections because it both increased the
area of accessible habitats and affected the distance a
species had to travel to reach the same location. Selection of background points is important to the performance of SDMs under current conditions (Elith &
Graham, 2009; Barbet-Massin et al., 2012), but ‘background-mode’ did not have a significant effect on projected range shifts relative to other major sources of
uncertainty. Nonetheless, species’ occurrences should
only be fitted against the range of environmental conditions accessible to them, which implies an assumption of
dispersal pathways (Vanderwal et al., 2009; Domisch
et al., 2013). However, in many cases, even the dispersal
mode can be difficult to assign with confidence as the
opportunities for movement may even vary between the
different life-stages of a species (Driscoll et al., 2014). For
example, less-mobile taxa like insects that lack aerial
adults, molluscs, crayfish and hydrochorous plants
could be transported long distances by waterbirds
(Green, Figuerola & S
anchez, 2002; Brochet et al., 2009;
Van Leeuwen et al., 2012), and potentially track suitable
climatic conditions through a series of long-distance
jumps (Kelly et al., 2014). However, the prevalence of
such events is unknown (Horv
ath, Vad & Ptacnik, 2015),
and most evidence in NSW suggests freshwater communities are spatially structured to some degree, particularly by catchment boundaries (Bush et al., 2012).
Given the rapid rate of ongoing climate change, we
must conservatively assume adaptive range shifts to offset range losses will be limited in freshwater taxa. Range
declines are therefore inevitable, but early action to support the natural adaptation of biodiversity to changing
conditions could improve the persistence of many species in the future. This study supports the need for
assessments that plan conservation actions to be more
representative and not depend on single groups or surrogates (Darwall et al., 2011). Furthermore, it is important more studies report the uncertainty surrounding
climate change projections as the use of only select scenarios can be misleading (Beaumont, Hughes & Pitman,
2008). Furthermore, other aspects of SDM fitting such as
the degree to which projections are allowed to extrapolate beyond their training environment (Elith et al.,
2011), or the method for thresholding projected habitat
suitability (Liu et al., 2013) could also be a significant
source of uncertainty. Consensus on the most appropriate GCMs, modelling algorithm to use, or thresholding
mechanism to apply to projected climate change impacts
is unlikely in the near future, and therefore studies in
other landscapes should also consider similar methods
to partition sources of uncertainty and communicate
their distribution.
© 2016 John Wiley & Sons Ltd, Freshwater Biology, doi: 10.1111/fwb.12874
Dispersal ability and projected impacts of climate change
The differences between projections due to uncertainty in the dispersal mode can help identify divisions
between similar environments that may not pose a barrier to strong aerial taxa, but could be restored to
improve connectivity for taxa with weaker flight or the
capacity for semi-terrestrial movement (Alexander et al.,
2011). One possible opportunity in NSW could be to
add connectivity of freshwater communities to the
objectives of the Great Eastern Ranges Initiative which
has a predominantly terrestrial focus (Mackey, Watson
& Worboys, 2010). Sub-catchments would not necessarily require formal protection, but could aid connectivity
if by restoring riparian vegetation which also improves
the quality of in-stream habitats, provides shade and
buffers streams from adjacent land use (Capon et al.,
2013). Based on the network of dispersal pathways, it is
possible to identify the most efficient reserve system
that protects species at each time interval (Phillips et al.,
2008). However, as extensive as the reserve system in
NSW is (Turak et al., 2011), the protection they offer
freshwater species is questionable because reserve status does not address threats such as upstream water
quality, modified flow regimes and invasive species
(Stein & Nevill, 2011; Turak et al., 2011). To account for
further assumptions in reserve design such as cost, as
well as the value of possible alternative management
actions was beyond the remit of this study. However, a
strategic approach based on the dispersal network
could be used in the future at smaller scales to identify
preferred conservation corridors and complement policies for human climate adaptation (Lukasiewicz et al.,
2013).
Thirdly, if range shifts are likely to be limited by
aquatic dispersal, or simply low movement rates, more
information will be needed to identify the risks of persistence in situ under increasing climatic extremes
(Leigh et al., 2014). Many fish for instance in NSW
have wide environmental tolerances (Sternberg & Kennard, 2013) and only require standing water to be
accessible in the landscape when conditions deteriorate
(Chessman, 2013). Conversely many crayfish are
restricted to high altitude refugia, isolated by unfavourable conditions at lower elevations (i.e. the ‘death
valley’ scenario; Hughes, 2007; Hughes, Schmidt &
Finn, 2009), and projected to lose all suitable habitat
within their current range. Furthermore, regional range
losses of more widespread species may not be trivial if
they disguise the loss of genetically distinct populations and cryptic species in Australia (Balint et al.,
2011; Faulks, Gilligan & Beheregaray, 2011; Hammer
et al., 2014). Managers will need to consider whether
11
translocation is an option, or if climatic changes could
be mitigated in natural and artificial refugia (Robson
et al., 2013).
Globally, freshwater ecosystems are suffering from
multiple stressors (Dudgeon et al., 2006), and whilst
short-term conservation management remains essential
to slowing the pace of biodiversity loss, preparation for
long-term sustainability under future conditions is
becoming an increasing priority (Ormerod, 2009). The
high spatial and temporal variability of freshwater systems in New South Wales presents a particularly challenging environment for biodiversity management, and
the uncertainties become magnified when considering
how ecosystems may respond under climate change
(Ormerod, 2009). This study may appear to emphasise
the uncertainties, but it is important to clearly recognise
the assumptions that underpin projections and look for
management options that are robust to uncertainty
(Game et al., 2011). It is often easier to protect rather
than restore important habitats and this should be considered as early as possible to aid the long-term adaptation of ecosystems.
Acknowledgments
This work was funded as part of the New South Wales
Adaptation Research Hub. We are very grateful to all
the institutions that provided records or environmental
data as well as Renee Catullo, Dean Gilligan, Matthew
Gordos, Hugh Jones, Rob McCormack, Dan Rosauer, Jeremy Vanderwal and Tom Weir.
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Supporting Information
Additional Supporting Information may be found in the
online version of this article:
Appendix S1. Sources and temporal distribution of species data.
Appendix S2. Selected variable for all freshwater SDMs.
Appendix S3. Model validation scores for all SDM algorithms and ensemble models.
Appendix S4. R-code for calculating projected range
shifts of taxa with different dispersal pathways.
Appendix S5. Summary of generalised additive mixedeffects models.
(Manuscript accepted 27 October 2016)
© 2016 John Wiley & Sons Ltd, Freshwater Biology, doi: 10.1111/fwb.12874