Download Instructions for the use and interpretation of CLIMEX

Survey
yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Document related concepts
no text concepts found
Transcript
PRATIQUE
No. 212459
Deliverable number: 3.3 Annex 2G
Date: 17/06/2011
Deliverable 3.3 Protocol for mapping endangered areas:
Annex 2G Instructions for the Use and Interpretation of CLIMEX
Richard Baker (Fera, UK), Sarah Brunel (EPPO), Alan MacLeod (Fera, UK) & Darren
Kriticos (CSIRO)
1.
Introduction to CLIMEX
This document provides a brief introduction to CLIMEX and is primarily intended for those
who have not used the software and wish to understand how it works so they can interpret
CLIMEX maps.
Full details of the software can be found on the Hearne website and in the CLIMEX User’s
Guide (Sutherst et al., 2004; 2007) with examples of its use by the developers in
additional references, e.g. Sutherst & Maywald (1985, 2005). There are many examples
of the use of CLIMEX in the literature and these are given in the User’s Guide. Nine
tutorials are included with the software.
CLIMEX provides tools for predicting and mapping the potential distribution of an
organism based on:
(a) climatic similarities between areas where the organism occurs and the areas under
investigation (Match Index),
(b) a combination of the climate in the area where the organism occurs and the
organism’s climatic responses, obtained either by practical experimentation and
research or through iterative use of CLIMEX (Ecoclimatic Index).
CLIMEX version 3.0 was released in January 2007 . These instructions are based on
CLIMEX version 2.0.
1.1
Climate Match Index
To compare and contrast climates in an organism’s current range and the area under
consideration, various techniques, some of considerable complexity, have been used
[see, for example, Baker (2002)]. The CLIMEX Match Climates model takes a simple
approach by employing an algorithm that summarises the similarities in monthly mean,
minimum and maximum temperatures, rainfall, rainfall pattern, relative humidity and soil
moisture at different locations to derive a Composite Match Index (CMI) scaled from 0 to
1. Comparisons are made between one “home” location where the pest is present and
any number of “away” locations. If necessary, users can focus on particular parameters
or periods in the year by choosing not to select irrelevant parameters or time periods to
derive the CMI. The index is calculated for weather stations or for world/regional
climatologies interpolated to a grid. The results can be displayed in a table or a map.
Match indices can also be displayed for each individual climate variable to investigate the
patterns of match for the components of the CMI.
1
PRATIQUE
No. 212459
Deliverable number: 3.3 Annex 2G
Date: 17/06/2011
1.2
Ecoclimatic Index
The “Compare Locations” module of CLIMEX calculates an Ecoclimatic Index (EI) that
combines an estimate of the potential for the species to thrive when conditions are
suitable for development (Growth Index) and whether it could likely survive periods of
extreme cold, heat, wetness or drought and combinations of these stresses, e.g. hot and
wet, cold and wet (stress indices). The growth index, which represents the suitability of
the location for growth and development, is calculated according to how close ambient
temperatures, soil moistures and day lengths are to estimates of a pest’s maxima, minima
and optima. In the unfavourable periods, the stress index is calculated according to the
degree to which the climate is too wet, dry, hot, cold, wet and hot, wet and cold etc. The
overall suitability of the location is represented by the Ecoclimatic Index, formed by the
product of these two indices. Parameters for the growth and stress functions are usually
inferred from a pest’s known distribution. Responses to temperature, moisture and other
factors are estimated by trial and error to try to mirror the known distribution of the pest,
assuming that, in the centre of its range, the growth index will be at its maximum (but
probably not 100) and the stress indices at minimum. At the edges of its range, the
combination of growth and stress indices usually result in low EIs that are just sufficient for
survival.
If available, population responses to weather variables obtained by
experimentation can be used to inform the selection of cardinal temperature and soil
moisture parameters, though estimates from direct experimentation are generally not
directly transferable into a climatic model. There are two reasons for this. Firstly, climate
averages reduce the extreme vales found in a weather sequence, and secondly, an
organisms’ short-term response to a variable may not be the same as a populations’
response over a longer period.
Nonetheless, Parameters estimated from direct
experimentation and observation should provide corroboration of parameters inferred from
geographic distribution data.
In addition to needing to have growth potential at a site, and non-lethal stresses, an
organism also needs to be able to satisfy any requirements for a thermal heat sum, as
well as any diapause requirements. Where these mechanisms are employed in CLIMEX
it is possible to have high EI values up to some parts of the range border (eg Kriticos and
Randal, 2001; Kriticos et al. 2003). Once CLIMEX has satisfactorily mirrored the species’
current distribution, indices can be calculated from meteorological data in the area under
consideration and mapped.
Hint
Most newcomers to the Compare Locations model in CLIMEX (particularly ecologists) are
daunted by the array of parameters with a distinctly physiological nature. Whilst these
parameters do have an ecophysiological basis, they do not need to be parameterised
from physiological experiments. The CLIMEX model-fitting procedures allow you to infer
appropriate values for the parameters. In addition, only a subset of the parameters are
used to model each organism. The ecophysiological nature of the parameters in CLIMEX
means that you can use the model to extract information from its geographical distribution
to gain a greater understanding of the nature of the organism and its response to climate.
Where some understanding of these relationships already exists, it can be used to draw
greater confidence in the model, or to help inform parameter selection.
2
PRATIQUE
No. 212459
Deliverable number: 3.3 Annex 2G
Date: 17/06/2011
2.
Limitations of CLIMEX and predicting potential distribution based on climate
There are clear limitations since an organism’s current and potential distribution may
depend on other parameters in addition to recorded climate such as:
 Hosts, food (for predators) or habitats (for plants).
 Abiotic factors other than climate, e.g. pollution, salinity and pH of water and soil.
 The degree to which the microclimate where the pest lives, e.g. soil, within trees, in
riparian, marshy habitats or underwater, is comparable to that measured in a
weather station or in a grid where climatic data have been interpolated over the
landscape.
 The degree to which the microclimate where the pest lives is modified by humans,
e.g. in protected cultivation and by irrigation.
 Physical features, e.g. the sea, mountain ranges etc, which limit the distribution of a
pest.
 Natural enemies.
 Competitors.
 Vectors and symbionts.
 Features of the environment managed by humans.
 How far a species has spread (for species that are still invading).
 The strength and efficacy of eradication/containment programmes.
 The extent to which the pest has been surveyed (date, intensity, consistency,
accuracy etc).
 Accuracy of recording (e.g. incorrect identification, taxonomic revision or an error:
there may be reports of an organism from a country but these may just be
interceptions since the organism has not established).
 Lags in range expansion or contraction following climate change.
There are other difficulties with using climatic data:
 Climatic data are usually averages of 30 years of monthly data and do not reflect
the inter-annual fluctuations that may have a major influence on an organism’s
development and survival.
 The weather station and world gridded 0.5º resolution data provided with CLIMEX
are for 1961-1990 and are thus somewhat out of date, and do not reflect how the
climate has changed since 1990.
 The density and distribution of meteorological stations will influence the
representation of climate in the areas where the pest is present and the areas
endangered by the pest.
Information on climatic responses for individual species must also be interpreted with care
because:
 The averaging process involved in creating climatologies offsets the extremes. For
example, a monthly mean minimum temperature of 2.3 °C represents
approximately one frost day per week.
 Laboratory experiments at constant temperatures may not provide an accurate
assessment of development rates in natural fluctuating temperatures.
 Minimum temperatures for development may be approximate because they have
been calculated by linear regression and, due to high mortality, may be based on
very few individuals.
3
PRATIQUE
No. 212459
Deliverable number: 3.3 Annex 2G
Date: 17/06/2011


Data in the literature derived solely from a species’ native range, may not be
relevant to invading populations under study because of differences between the
realised and fundamental niches (Hutchinson 1957; Davis et al 1998).
If parameters are inferred from a species distribution and this is limited by nonclimatic range barriers, for example, by the sea or another physical barrier, its
ability to survive climatic conditions beyond the barrier may be unknown.
It is important to appreciate the fact that constructing a CLIMEX model to predict the
potential distribution of a pest can be very time consuming. Further work can usually be
undertaken to incorporate new information concerning the pest or to explore the influence
of particular parameters on predicted distribution. Pragmatism demands that model
development be halted at an appropriate point. A key difficulty lies in communicating the
remaining uncertainty concerning the CLIMEX maps to the reader. Research work on this
topic is being planned.
Although they need to be used with caution, CLIMEX outputs can be helpful in projecting
the potential distribution of pests, and also for gaining insight into what factors are likely to
be affecting it at different times of the year in each location.
3.
Preliminary work
Preliminary work consists of collecting and organising information on the pest by
searching the literature and through contacts with experts.
Information on the pest’s life cycle, the need for specific hosts or vectors, its responses to
climate, e.g. temperature thresholds and degree-days for development, and other abiotic
factors may be available in the literature. When undertaking literature searches and
browsing the Internet it is important to remember that information in books and datasheets
in compendia, e.g. from CABI, can often only be obtained by consulting libraries. Museum
and herbarium data may list the locations where a species is known to be present. Such
data, in addition to those from fauna and floral databases, are especially useful when
trying to determine the species known distribution.
These data can be mapped with computer mapping software known as geographical
information systems (GIS). Well-known examples are Arcview and ArcGIS from ESRI or
Mapinfo from the Mapinfo Corporation. World and regional distribution maps can be
generated with different colours to show, e.g. native and non-native ranges.
Once maps of a pest’s current distribution have been created, they may immediately
provide clues and hypotheses concerning the extent to which species are limited by cold,
hot, wet or dry stresses or insufficient warmth to complete their life cycle that can then be
explored with CLIMEX.
Although the information is often incomplete, it is particularly important to try to determine
the locations where the pest:
 is native,
 has invaded,
 has never been present,
4
PRATIQUE
No. 212459
Deliverable number: 3.3 Annex 2G
Date: 17/06/2011

may be present but no surveys have been undertaken or the species may have
been overlooked due to detection/identification difficulties,
 was once present but has died out due to unfavourable climatic factors,
 is only present through repeated invasions, i.e. its status is transient or casual,
 is only present due to human modification of the environment through factors such
as irrigation,
 is most abundant and climatic conditions are most suitable,
 is rare and climatic conditions only just allow the species to survive. Populations
may be unhealthy, small in size or have low fecundity, longevity and abundance.
Determining whether species distributions are related to particular environmental variables
is often problematical. It is often the case that in the process of fitting the model
parameters it becomes apparent that one or more locations cannot be fitted without
distorting the model. These outliers deserve considerable attention to determine whether
they should be included in the model, or whether they should be treated as special cases.
Where available for a known location, information on the growth phenology of a species
can also be extremely important for inferring temperature and soil moisture parameters.
4.
Use of the CLIMEX functions
The world gridded data at 0.5º (or 30’) latitude/longitude resolution should always be used.
Although they take longer to run, they give a more complete picture than the weather
station dataset. If desired, even finer-scale datasets can be used, but due to the long time
it takes to run the finer scale datasets, they should be reserved for model fine-tuning.
4.1
Match Index
The Match Index can provide a useful general guide to a species’ potential distribution
when there is only scant information available, but care must be taken since the CLIMEX
Match Index algorithm and the selection of climatic factors may not be relevant to the
species studied.
The Match Index is particularly useful when locations where the species is known to be
very numerous, i.e. where climatic conditions are assumed to be at their most suitable
(though remember the caveats listed in section 2 above), have been obtained. Knowledge
of the species’ life history should also be taken into account. When matching climates to
a location near a species’ range boundary, you should remember that the climate
matching will identify sites of both lower suitability and higher suitability as having a lower
degree of match. The index is identifying sites that have a climate that is marginal for the
species.
Examples
For pests of irrigated crops and aquatic plants, rainfall and soil moisture variables can
generally be ignored when predicting potential distribution, so temperature parameters
may be the only climatic factors that can be explored.
For a summer growing annual plant of temperate climates whose seeds lie dormant in the
soil during winter and can survive very harsh winters, Match Index comparisons could
5
PRATIQUE
No. 212459
Deliverable number: 3.3 Annex 2G
Date: 17/06/2011
exclude winter conditions (though vernalisation requirements may need to be taken into
account).
4.2
Compare Locations
Ideally, locations within the current distribution should be divided into three sets, with one
part employed to determine the key CLIMEX parameters, the second part used to verify
the model fit, and the third used to validate the model fit, before the potential distribution in
the PRA area is projected. Model verification is the process of checking that the model
behaves as expected, whereas model validation is the test of how well the model behaves
when compared to independent data. If the organism’s distribution is limited to only one
or two continents, then it may be necessary to omit the verification or validation steps, and
note this fact in the model documentation.
It is a fairly common observation that species are able to expand their climatic ranges
when released from the effects of their natural enemies (Keane & Crawley 2002). This
phenomena has lead to the recommendation that where available, include consideration
of exotic ranges when fitting models (Kriticos & Randall 2001, Kriticos et al., 2005).
CLIMEX Compare Locations models are best fitted to location data by firstly fitting stress
parameters, aligning the edge of the Core Distribution to the known range of the species
being modelled. When the stresses have been fitted, the growth indices can be adjusted
to indicate the distribution pattern of climatic suitability within the range defined by the
stresses.
Example
The native range of Eichhornia crassipes is in South America, and it has invaded NorthAmerica, Africa, Australia and New Zealand. CLIMEX Compare locations parameters can
therefore be developed by trial and error for the American continent, tested in Africa,
Australia and New Zealand, and then applied to Europe.
CLIMEX templates were fitted to a climate classification. The following templates are
provided: Comfort, Desert, Mediterranean, Semi-arid, Temperate, Tropical savannah and
Wet tropical. These CLIMEX climate templates are a good place to start when estimating
species parameters, but their parameter values should never be relied upon as a
justification for selection of a parameter.
Based on hypotheses that may have been generated while undertaking the preliminary
work (see point 3), temperature indices, moisture indices, cold, heat, dry and wet stresses
can be modified individually or in combination to emulate the known distribution of the
species. The factors that continue to limit distribution can be explored by right-clicking the
locations on the CLIMEX map. Light index, diapause index and day-degree accumulation
are particularly designed for invertebrate pests. Multiple stress indices (Hot-Dry, Hot-Wet,
Cold-Dry, Cold-Wet stresses) are generally only used when the required distribution
cannot be obtained by other methods. A cold-wet stress could be used to preclude a
species from a Mediterranean climate zone, whereas a hot-wet stress would have the
same effect for a tropical zone.
6
PRATIQUE
No. 212459
Deliverable number: 3.3 Annex 2G
Date: 17/06/2011
Further sensitivity analyses can be undertaken to explore the importance of particular
variables. That is, systematically changing each of the variables and assessing the effect
that the change has on the projected range and spatial pattern of abundance of the
species.
Example
For Eichhornia crassipes, the wet tropical template was first taken as a basis. The cold
stress, heat stress and development temperatures were then manipulated so that the
CLIMEX distribution corresponds with the distribution in the Americas, and then tested on
the distribution in Africa, Australia and New Zealand, and finally produce climatic
prediction maps for the world and Europe.
Another important function of CLIMEX is the climate change scenario. This module allows
the user to run a model for a species under simple climate change scenarios (Kriticos et
al., 2003). This gives the user the opportunity to simulate the effects of past or future
climates or modified habitat. In addition to using the climate change module in CLIMEX to
generate climate change scenarios, it is also possible to draw upon output from Global
Climate Models (GCM’s).
A range of future climate scenarios used by the
Intergovernmental Panel on Climate Change (IPCC) Third Assessment Report have been
imported into CLIMEX (Stephens et al 2007). These scenarios have the advantage that
they are internally consistent i.e., that at each place and time the projected temperature
conditions could be associated with the other variables (eg rainfall, relative humidity)
without violating any laws of physics, and that the scenario could plausibly occur. It
should be borne in mind however that as well as uncertainty in our ability to model climate
changes, there remain great uncertainties around emission scenarios and the sensitivity
of climate to greenhouse gases (Kriticos et al 2006).
CLIMEX can produce maps for many variables, apart from the Ecoclimatic Index, which
provides an overall summary of the climate suitability for a species persistence. For
example. annual totals for each of the stresses, can be mapped as a means of
understanding what factors are likely to limit a species’ ability to persist at each location.
The annual Growth Index indicates the opportunity for a species population to grow there.
It is quite possible for a species to be able to grow for a substantial period of the year at a
location, but be unable to survive an unfavourable season there. If these locations are
close by a suitable location, then there may be opportunity for seasonal migration and
occupation of the site during the favourable part of the year.
The CLIMEX results can be exported to a *.csv file, imported into a GIS, and if desired,
the Ecoclimatic Index values can be separated into classes of relative habitat suitability. A
value of zero corresponds to unsuitable habitat. Increasing values of EI correspond to
increasing habitat suitability. However, without some analysis of the relative habitat
suitability, any classification into suitability classes such as that of Kriticos et al (2003) or
Sutherst and Maywald (2005) is arbitrary. The intention in these publications of providing
a suitability classification was to de-emphasise the implied precision associated with the
percentage scale of the Ecoclimatic Index.
5.
Conclusions
7
PRATIQUE
No. 212459
Deliverable number: 3.3 Annex 2G
Date: 17/06/2011
Maps produced by the Match Index and Compare Locations models in CLIMEX may be
helpful in identifying areas at risk of pest invasion. Whilst CLIMEX maps appear to
convey a straightforward message concerning the areas suitable for an organism’s
establishment, in reality, the creation and interpretation of such maps is never simple. In
many cases, all that will be provided would be one map, with little explanation of how it
was created. This gives the misleading perception that CLIMEX provides not only a
simple procedure to follow, but also that its outputs have little uncertainty. The uncertainty
associated with the selection of each parameter , and the resulting uncertainty in the
model projections should always be taken into account. To map areas at risk of invasion,
CLIMEX maps will need to be combined with maps of the other key factors that influence
successful establishment, principally host range and habitats.
For further examples, please refer to the existing literature in scientific reviews, as well as
to climatic predictions available in Pest Risk Analyses (i.e. refer to the European and
Mediterranean Plant Protection Organization website).
8
PRATIQUE
No. 212459
Deliverable number: 3.3 Annex 2G
Date: 17/06/2011
References
Baker RHA (2002) Predicting the limits to the potential distribution of alien crop pests. In:
Invasive Arthropods in Agriculture. Problems and Solutions, Hallman, G.J. & Schwalbe,
C.P. (Eds). pp. 207-241. Science Publishers Inc. Enfield USA.
Davis, AJ, Jenkinson, LS, Lawton JH, Shorrocks B, Wood, S (1998) Making mistakes
when predicting shifts in species range in response to global warming. Nature. 391:783786.
Hearne website
Information on CLIMEX Version 2 and its use, including a working demonstration and
pricing,
can
be
obtained
from:
www.hearne.com.au/products/climex
and
www.hearne.co.uk/products/climex/.
Hutchinson, GE (1957) Concluding remarks. Cold Spring Symposium on Quantitative
Biology; Yale University, New Haven, Connecticutt, USA. 415-427. v. 22).
Keane RM., Crawley MJ (2002) Exotic plant invasions and the enemy release hypothesis.
Trends in Ecology and Evolution. 17(4), 164-170.
Kriticos DJ, Randall RP (2001) A comparison of systems to analyse potential weed
distributions. Groves, R. H.; Panetta, F. D., and Virtue, J. G., Eds. Weed Risk
Assessment. Melbourne, Australia: CSIRO Publishing. pp. 61-79.
Kriticos DJ, Sutherst RW, Brown JR, Adkins SA, Maywald GF (2003) Climate change and
the potential distribution of an invasive alien plant: Acacia nilotica ssp. indica in Australia.
Journal of Applied Ecology 40(1), 111-124.
Kriticos DJ, Yonow T, McFadyen RE (2005) A revised estimate of the potential distribution
of Chromolaena odorata (Siam weed) in relation to climate. Weed Research 45(4), 246254.
Kriticos, DJ; Alexander, NS, Kolomeitz, SM (2006) Predicting the potential geographic
distribution of weeds in 2080. Proceedings of the Fifteenth Australian Weeds Conference ;
Adelaide, Australia. Melbourne, Australia: Weed Science Society of Victoria. Pp. 27-34.
Stephens, AEA; Kriticos, DJ, Leriche, A (2007) The current and future potential
geographic distribution of the Oriental fruit fly, Bactrocera dorsalis, (Diptera: Tephritidae).
Bulletin of Entomological Research. 97(4):369-378.
Sutherst RW, Maywald GF (1985) A computerised system for matching climates in
ecology. Agriculture Ecosystems and Environment 13, 281-99.
Sutherst RW, Maywald GF (2005) A climate model of the red imported fire ant, Solenopsis
invicta Buren (Hymenoptera: Formicidae): implications for invasion of new regions,
particularly Oceania. Environmental Entomology 34, 317-335.
9
PRATIQUE
No. 212459
Deliverable number: 3.3 Annex 2G
Date: 17/06/2011
Sutherst, GW, Maywald GF, Bottomley W, Bourne A (2004) CLIMEX v2. User’s Guide.
Hearne Scientific Software Pty Ltd, Melbourne, Australia
Sutherst, RW; Maywald, GF, Kriticos, DJ (2007) CLIMEX Version 3: User's Guide.
www.Hearne.com.au: Hearne Scientific Software Pty Ltd; Melbourne, Australia. 131pp.
10