problem-solving in conservation biology and wildlife management (gibbs/problem-solving in...

CHAPTER 25

Climate EnvelopeModeling: Inferring theRanges of Species toFacilitate BiologicalExploration,Conservation Planning,and Threat Analysis

Robert Hijmans, Andy Jarvis, and Luigi Guarino

You would be rather startled to find a polar bear paddling in the Amazon or a palmtree swaying in the balmy breezes of Siberia. That’s because most species have quitepredictable distributions around the globe. Even species with really wide distribu-tions such as ospreys or bracken ferns are not found in every ecosystem.

Species distributions are shaped by many factors: physical features of the environ-ment such as climate and soils, the distribution of other species, and past and currentgeographic barriers. However, climate is usually reckoned to have a fundamentaleffect, and is increasingly being used to probe and predict the distributions ofspecies. A technique called climate envelope modeling uses points where a speciesis known to occur to infer the climatic adaptation of the species, and then to identifythe degree to which the climate in other areas is suitable for the species. See Grahamet al. (2004) for more information.

This technique is of great interest to conservation because occurrence data frome.g. natural history collection databases are often sparse and do not fully describe thegeographic distribution of a species. Climate envelope modeling fills in the gapsin the collections by providing an indication of the likelihood that a species mightbe found in unexplored areas. Specifically, this can be useful for:

. biological exploration, targeting sites which it might be worthwhile to visit to finda given species

. conservation planning, providing information as to the species likely present in agiven area

. threat analysis, identifying the factors that might cause changes in the distributionof a species (for example, climate change).

Gibbs / Problem-Solving in Conservation Biology 9781405152877_4_025 Final Proof page 244 11.10.2007 2:03pm Compositor Name: PAnanthi

Problem-Solving in Conservation Biology and Wildlife Management: Exercises for Class, Field, and LaboratoryJames P. Gibbs, Malcolm L. Hunter, and Eleanor J. Sterling© 2008 James P. Gibbs, Malcolm L. Hunter, Jr., and Eleanor J. Sterling ISBN: 978-1-405-15287-7

For this exercise, 26 wild species of the genus Arachis (the relatives of Arachis hypogaea,the cultivated peanut), are used as an example. This exercise is partly based on Jarviset al. (2003), which is useful to consult as you work through exercise.

Objectives

. To learn the basics of climate envelope modeling

. To examine the climatic adaptation of a species

. To predict the entire geographic range of a species.

Procedures

If you have not done so already, download and install on your computer the free GISprogram called DIVA-GIS from www.diva-gis.org. It is assumed that you havecompleted the DIVA-GIS exercise (on wild potatoes) in chapter 23 of this book,which also means that you have probably read the first chapter of the DIVA-GISmanual. For the present exercise, it would be useful to read chapter 7. For thisexercise you need to download climate data from the DIVA-GIS website (http://diva-gis.org/climate.htm); select the data that have 10 minutes spatial resolution andfollow directions given on the web site.

Data Preparation

1 Make a new folder, and unzip the data for this exercise into it.2 Make a shapefile called ‘‘peanuts.shp’’ of the points in ‘‘arachis_accessions.dbf ’’ and

save it in the new folder. This file has 397 localities representing collecting sites.3 Add the shapefile to the map; also add the ‘‘pt_countries’’ shapefile (download from

the book’s website). See Figure 25.1.

Fig. 25.1 Wild peanut distribution data.

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4 If you do not have them already, now download the global climate files at 10minute resolution for current conditions. Check if you have climate data files byclicking on Climate>Point and then clicking on any part of the map (where thereis land). If you cannot get windows like the two shown below (and get the errormessage ‘‘no climate databases found’’), you should download the climate datafrom http://www.diva-gis.org OR you need to change the folder where DIVA-GISlooks for the climate data (under Tools>Options>Climate). See Figure 25.2.

Distribution Modeling

Frequency distributions

Now we are going to explore the climate data associated with the peanut accessionsthat we have imported.

1 Make the ‘‘peanuts’’ layer the active layer.2 Use Modeling>Bioclim and Domain.3 Go to the second tab (Frequency) and press Apply. See Figure 25.3.4 Note that there are 161 non-duplicate observations. Points that are at exactly the

same location are included only once. Points that are at a different location but inthe same cell of the climate grid are also included once, in this case (this is anoption that can be changed on the first tab; if you do so you will note that there are224 unique observations).

5 Explore this tab by changing the climate variables. This is helpful to get an idea aboutthe general characteristics of the distribution of these points in environmental space(as opposed to the distribution in geographical space which you can see on the map).It may also reveal environmental outliers, perhaps caused by incorrect coordinates.

6 If you click on a point in the graph different things can happen, depending on thestate of checkboxes above the graph. Check this out.

Environmental Envelope

1 Go to the Envelope tab. Here you can look at the distribution of the points in twodimensions of ‘‘environmental space.’’

Fig. 25.2 Visualizing the climate data.

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2 Display different ‘‘environmental envelopes,’’ using the Percentile button. Consultthe textbox below entitled Climate Envelope Modeling for further background. Forexample use percentiles 0, 0.05, 0.1, and 0.25. The points inside the multidimen-sional (i.e. all climatic variables) envelope at the percentile cut off specified arecolored green; the other ones are colored red. Thus, red points within the envelopemust be outside of it in another dimension (with regards to another variable on thehorizontal or vertical axis).

. The percentiles are calculated for each variable individually, and then combined.The percentage of the points that are inside a multidimensional envelope ata certain percentile will be different for each data set.

. The points that are inside the (two-dimensional) envelope on the graph arenow colored yellow on the map. We have thus established a link betweenthe distributions in ecological and geographic space (Figure 25.4).

Modeled Distributions

1 Go to the Predict tab.2 First, enter the area for which you want to make a prediction. Use these values:

MinX ¼ �71; MaxX ¼ �39; MinY ¼ �33; MaxY ¼ �4.3 Select the Bioclim output, choose an output filename, and press Apply. The result

should be as in Figure 25.5. Note that this result suggests that there is a large areain south central Brazil where the climate appears to be suitable for peanuts butwhere no peanuts have been collected (or even occur?). See Figure 25.5.

Fig. 25.3 A cumulative frequency distribution for the mean temperature data.

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Climate Envelope Modeling

In climate envelope modeling (also referred to as species distribution modelingor ecological niche modeling) a set of points where a species was observed isused to infer its environmental requirements. For example, when a species onlyoccurs in cold and dry areas then the reasonable assumption is made that thespecies can only survive under such conditions. These inferred environmentalrequirements can then be used to predict the likelihood of the species beingpresent (or able to survive) at any place or time period for which environmentaldata is available. See Graham et al. (2004) for more information.

Many different statistical methods have been applied to this problem, includingdistance metrics, percentile distributions, logistic regression, principal compon-ents analysis, and machine learning approaches such as neural networks. Many ofthe early methods use ‘‘presence-only’’ data, but more recently developed methodstend to use ‘‘presence/absence’’ data. Sometimes the absence data used is simply arandom sample from throughout the study area. Elith et al. (2006) compared manymethods and showed that machine learning and advanced regression methodsgenerally outperform other methods.

InthisexerciseweusetheBIOCLIMmodel,because it is implementedinDIVA-GISand easy to understand and run. DIVA-GIS also implements the DOMAIN model(see the manual about more information about these models). Both are presence-onlymodels that may not perform very well. Maxent is an easy-to-run model that isthought to be much superior (see Further Resources).

Fig. 25.4 Establishing a link between the distributions in ecological and geographicspace.

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4 Now make distribution models with different output types (use Bioclim True/Falseand Bioclim Most Limiting Factor Note that they are all representations of the sameresult, and that the result is dependent on the percentile cut-off you choose (explorethis cut-off in the graph of the Envelope tab). The Most Limiting Factor outputsthe variable with the lowest score in each grid cell for which there is a prediction.

Single Species Models

1 So far, we have treated the data as representatives of a single taxon, the genusArachis. Now let’s go to the species level. Go to the Input tab, select Many Classesand then select the Taxon field (see Figure 25.6). Go over the list of taxa to assurethat there are no errors. A typical problem is that the same species occurs morethan once because of spelling mistakes.

2 Now go to the Frequency tab and select one or two classes (or species, in our case).In the example below we compare the annual rainfall for two species. A. kuhlmanniiis clearly distributed in drier areas than A. stenosperma. Note that there is one hugeoutlier in the rainfall distribution of S. kuhlmanii. This could be a problem.

Click on that outlier on the graph, to find out what record it represents, what itsassociated climate data are, and where it is located on the map (use the checkboxesabove the graph). For example, first select the species, using Layer> Select Recordsso that the records for that species are colored yellow on the map. Then click on theoutlier on the ‘frequency’ graph, and see to which point on the map it corresponds. Itis clearly a geographical outlier as well. No members of the species (or any otherArachis species for that matter) have been observed in this area. In this case it wouldbe prudent to check the coordinates against the locality description. However, thatinformation is not provided here so there is not much we can do to assess whether thisrecord is valid or not. Very much a problem! It is up to your ‘‘expert opinion’’ whetherto leave this record in or take it out. Let’s leave it in for this exercise (Figure 25.7).

Fig. 25.5 Predicting where the climate is to be suitable for peanuts but where no peanutshave been collected.

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3 Go to the Predict tab and make modeled predictions (using Bioclim) for the samearea as before (set the coordinates again as before, or make the previously modeleddistribution the active layer and press the Read from Layer button to get thecoordinates). In this case, however, use the Batch option. This will create a

Fig. 25.6 Selecting a particular taxon of wild peanut for modeling.

Fig. 25.7 Identifying outliers in climate space.

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prediction for each species. Save the output file in a new folder. Note that theoutput file is a stack. A stack is a collection of one or more gridfiles with the sameorigin, extent, and resolution.

4 Open some of the gridfiles that you have just made and compare the modeledrange map with the observed points. There are a number of ways to look at thepoints for one species at a time. One option is to select the records for the speciesin question. The default color for the selected points is yellow, which, in this case,is also a color on the grid. You can change the color of the selected records underTools>Options. You can also save the selected records to a new shapefile. Try it.

However, the easiest way to show a single species is by using Layer>Filter. Makethe peanuts shapefile the active layer, then select the field Taxon and the valueA. stenosperma. A. stenosperma has a disjunct distribution (a number of populationsalong the coast and another group more inland). There is one record right in themiddle of the two distributions. This record again warrants checking. If it is correct,it would make an interesting specimen for study. A disjunct distribution can bedifficult to model. It may very well be that the groups are in fact different ecotypeswith a quite distinct climatic adaptation. In this case, the predicted range has all theinland localities on its margins, and shows a large area of apparent suitability fromwhere there are no records of this or any other Arachis species. Also note some smallisolated areas far away from the main area of the observed records and predictedrange. These areas are probably irrelevant because they are very small, and far awayfrom any locality where the species has been observed (Figure 25.8).

Modeled versus observed diversity

1 Add all the gridfiles (Stack>Calculate> Sum). Use the Sum as Present/Absentand> 0 option. Only very few areas are predicted to have more than one species(Figure 25.9). Are all areas included that were on the prediction for the genus as awhole? With other model settings, the results can change quite a bit. For example,

Fig. 25.8 Observed and predicted distribution of A. stenosperma.

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repeat the modeling and summation using only bioclimatic variables 1, 5, 6, 12,15. (Uncheck the others under Select Variables on the Predict tab.)

2 Aggregate (Grid>Aggregate) the stack to a 1 degree resolution (6x) using theMAX option and sum the result again. Aggregation combines smaller cells intolarger cells. It will frequently be done using the Average option so that the largercell has the mean value of the original smaller cells. Here we use Max because if isspecies is present in only one small sub-cell of the large cell we can say it is presentin the larger cell (Figures 25.10 and 25.11).

3 Make a map with observed species richness at the same resolution as the previousmap. Use Grid>Point to Grid>Richness (use Define Grid, and ‘‘Use param-eters from another grid’’ to assure having exactly the same grids).

Compare modeled and observed richness before and after aggregation. First useAnalysis>Regression. Then use Grid>Overlay (Figure 25.12).

Fig. 25.9 Predicted diversity pattern.

Fig. 25.10 Changing cell size of a grid by aggregation.

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Expected Products

. Description of the climatic adaptation in ‘‘ecological space’’ for the genus as awhole and for two contrasting species

. Map of the predicted distribution for one species of the genus

. Map of the observed and predicted species richness of the genus

. Map identifying new areas where further exploration of a species might besuccessful

. Responses in a form indicated by your instructor to the Discussion questionsbelow.

Fig. 25.11 Aggregated grid data.

Fig. 25.12 Overlaying two grids.

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Discussion

1 What other factors apart from climate might be important in defining a speciesrange?

2 How many collection points do you think are needed for climate envelopemodeling to be accurate?

3 Can you identify the potential sources of error that might cause the results to beless accurate?

4 How would you analyze the climatic envelope of species with disjunct distributions,or with known ecotypes?

Making It Happen

You can refine this exercise by using different climate envelope models andcombining the results to increase the confidence in your predicted species range.Some of these are available in DIVA-GIS (DOMAIN, for example), whilst othersrequire installation of other programs (MaxEnt or GARP, for example) althoughDIVA-GIS can be used to prepare the input files for your analysis. You may alsowant to experiment with methods for validating your results. This can be done bysplit-sampling of the input collection point data, and using statistical analyses suchas Kappa or AUC plots. This is available in DIVA-GIS in Modeling>Evaluation.You could also combine the predicted range from the climate envelope model withother datasets such as land cover to limit the range depending on the habitatpreference of the species under study. Finally, you can bring all the analyses togetherto identify conservation priorities through overlaying a protected areas dataset andexamining the current effectiveness of the reserve network in conserving the speciesfound in the genus. Species that do not fall in protected areas are then prioritiesfor conservation and the predicted ranges can be used to identify the regions wherea reserve would potentially conserve the greatest biological richness.

Another interesting application would be to look at climate change, but that isdiscussed in exercise 24 of this book.

Further Resources

For those interested in exploring these issues further, the following resources arerecommended:

. Maxent species distribution model: www.cs.princeton.edu/~schapire/maxent/

. More data on climate: www.worldclim.org

. More data on land cover: www.diva-gis.org/Data.htm or https://zulu.ssc.nasa.gov/mrsid/

. More data on protected areas: www.unep-wcmc.org/wdpa/.

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