Austral Ecology - 2022 - Booth - Checking bioclimatic variables that combine temperature and precipitation data before

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<p>Checking bioclimatic variables that combine temperature</p><p>and precipitation data before their use in species</p><p>distribution models</p><p>TREVOR H. BOOTH*</p><p>CSIRO Land and Water, GPO Box 1700, Canberra, Australian Capital Territory, 2601, Australia</p><p>(Email: trevor.booth@csiro.au)</p><p>Abstract The 19 BIOCLIM variables available from the WorldClim databases have become the most widely</p><p>used set of climatic variables for creating species distribution models (SDMs) and ecological niche models</p><p>(ENMs). Nevertheless, in recent years, there has been an increasing trend to exclude four of these variables from</p><p>SDM/ENM analyses. The four values concerned are interactive as they combine both temperature and precipita-</p><p>tion assessments. Their exclusion was justified initially due to discontinuities in their interpolated surfaces</p><p>observed in a study using data from version 1 of WorldClim. The discontinuities were thought to be artefacts of</p><p>the measures used, and such sudden changes were unlikely to be biologically meaningful. A freely available bio-</p><p>diversity database and an open access geographic information system were used here to check the four interactive</p><p>variables for discontinuities in data from both versions 1 and 2 of WorldClim. Over most of the world, the four</p><p>interactive BIOCLIM variables from WorldClim were found to change smoothly. Major and minor discontinu-</p><p>ities, however, were noted for all four variables in specific parts of some of the six continents (excluding Antarc-</p><p>tica) covered by WorldClim databases. Discontinuities were related to sudden changes in the quarterly periods</p><p>used to calculate the variables. These discontinuities were often found in equatorial regions where there are only</p><p>small changes in temperature across the year. Bimodal precipitation distributions may also cause problems. It is</p><p>recommended that the quick and simple method described here should be used to facilitate visual inspection</p><p>and numerical checking for possible discontinuities of these variables before SDM/ENM analyses. If discontinu-</p><p>ities are found for an interactive variable in the study area, it is recommended that the variable should not be</p><p>used. Ways in which the four variables could be recalculated when new databases are created are suggested to</p><p>reduce discontinuity problems.</p><p>Key words: Atlas of Living Australia, BIOCLIM, climate, ecological niche models, habitat suitability models.</p><p>INTRODUCTION</p><p>The development of ecological niche models (ENMs)</p><p>and their derived species distribution models (SDMs)</p><p>is one of the most active areas of contemporary ecol-</p><p>ogy. Google ScholarTM searches for these terms indi-</p><p>cate that many hundreds of ENM/SDM studies are</p><p>now published every year. Guisan et al. (2017) have</p><p>presented a brief history of pioneering ENM/SDM</p><p>projects from 1924, which they refer to under the</p><p>combined term of ‘habitat suitability models’.</p><p>Though important advances were made in these early</p><p>studies they were often limited by the lack of reliable</p><p>environmental data across broad regions.</p><p>Modern ENM/SDM studies began with the release</p><p>of the BIOCLIM program in January 1984 (Nix</p><p>1986; Busby 1991). This provided an easy-to-use</p><p>integrated ENM/SDM package which afforded</p><p>access to reliable interpolated bioclimatic data across</p><p>the continent of Australia (Booth et al. 2014). The</p><p>user only needed to input example occurrence data</p><p>(i.e. latitude, longitude and elevation) for their taxon</p><p>of interest. The program developed a description of</p><p>its ecological niche and a map of its likely distribu-</p><p>tion in terms of core and marginal areas. The niche</p><p>was described in terms of the range or percentile</p><p>range of 12 bioclimatic factors, including variables</p><p>such as mean annual temperature and precipitation</p><p>of the driest quarter. This set of 12 variables was</p><p>extended to a set of 19 variables in 1996 for a</p><p>revised version of the BIOCLIM package. Both the</p><p>12 and 19 variable sets were designed to provide</p><p>variables that were both biologically meaningful and</p><p>would work in either northern or southern hemi-</p><p>spheres (Booth 2018). The development of improved</p><p>climatic interpolation routines was a crucial part of</p><p>the BIOCLIM work. In the period to 2005, these</p><p>methods were applied not only to Australia but also</p><p>to all or part of two other continents (Hutchinson</p><p>et al. 1996; Booth et al. 2014). The thin-plate spline</p><p>interpolation methods were made available in the</p><p>*Corresponding author.</p><p>Accepted for publication August 2022.</p><p>© 2022 Commonwealth Scientific and Industrial Research Organisation. Austral Ecology</p><p>published by John Wiley & Sons Australia, Ltd on behalf of Ecological Society of Australia.</p><p>doi:10.1111/aec.13234</p><p>This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use,</p><p>distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.</p><p>Austral Ecology (2022) 47, 1506–1514</p><p>https://orcid.org/0000-0001-8506-7287</p><p>https://orcid.org/0000-0001-8506-7287</p><p>http://creativecommons.org/licenses/by-nc/4.0/</p><p>http://crossmark.crossref.org/dialog/?doi=10.1111%2Faec.13234&domain=pdf&date_stamp=2022-08-26</p><p>ANUSPLIN package (for current version see</p><p>Hutchinson & Xu 2013).</p><p>Both the ANUSPLIN interpolation method and</p><p>the set of 19 variables, initially developed for BIO-</p><p>CLIM, were adopted by the WorldClim team for the</p><p>preparation of versions 1 and 2 of their six continent</p><p>(excluding Antarctica) climatic database (Hijmans</p><p>et al. 2005; Fick & Hijmans 2017). The WorldClim</p><p>team adopted the set of 19 BIOCLIM variables with</p><p>one minor modification. Standard deviation was used</p><p>for temperature seasonality rather than a coefficient</p><p>of variation, as the latter was considered to have lim-</p><p>ited biological realism for interpretation with temper-</p><p>atures in the range between �1 and 1°C. Bradie and</p><p>Leunig (2017) examined more than 2000 SDM stud-</p><p>ies for nearly 1900 species. They reported the 19</p><p>BIOCLIM variables available from the WorldClim</p><p>database (Hijmans et al. 2005; Fick & Hijmans 2017)</p><p>to be the most frequently used set of variables for</p><p>developing SDMs and ENMs.</p><p>Nevertheless, Escobar et al. (2014) noted some dis-</p><p>continuities in the Americas for interpolated surfaces</p><p>available from version 1 of the WorldClim database</p><p>(Hijmans et al. 2005). Four of these variables, which</p><p>can be described as ‘interactive’ as they combine</p><p>temperature and precipitation, were excluded from</p><p>the Escobar et al. (2014) analysis. These discontinu-</p><p>ities, which extended across hundreds of kilometres,</p><p>involved abrupt changes in variable values over rela-</p><p>tively short geographical distances of around 1 km.</p><p>The interactive variables flagged were associated with</p><p>mean temperature of wettest quarter (Bio8), mean</p><p>temperature of driest quarter (Bio9), precipitation of</p><p>warmest quarter (Bio18) and precipitation of coldest</p><p>quarter (Bio19), where ‘quarter’ is a period of three</p><p>consecutive months. These variables are notable in</p><p>the set of 19 as they are the only variables that com-</p><p>bine both temperature and precipitation measure-</p><p>ments. The Bio8 and Bio9 variables were included in</p><p>the original BIOCLIM set of 12 variables (see vari-</p><p>ables 5 and 6 in Booth 1985).</p><p>Escobar et al. (2014) has been cited in 114 publi-</p><p>cations (to the end of 2021), 84 of which described</p><p>eliminating the same four variables from their analy-</p><p>ses. The notable tendency for researchers to exclude</p><p>these interactive variables based on Escobar et al.</p><p>(2014) has increased in recent years with 20 articles</p><p>published in 2020 and 43 in 2021. A concerning</p><p>aspect of almost all these papers is that they excluded</p><p>the variables based on the Escobar et al. (2014) anal-</p><p>ysis alone, without reporting further assessments of</p><p>discontinuities in their areas of analysis or any major</p><p>biological justification. Only one study (Burke et al.</p><p>2019) noted the Escobar et al. (2014) paper but</p><p>wrote ‘we chose to include these variables because</p><p>we did not observe any odd spatial patterns’ for the</p><p>study area of Mexico and the south-western United</p><p>States.</p><p>Similarly, a good-practice ENM guide (Sim~oes</p><p>et al. 2020), which also cited Escobar et al. (2014),</p><p>recommended that ‘regional checking could be per-</p><p>formed before discarding’ these variables.</p><p>Bradie and Leunig (2017) examined the contribu-</p><p>tion of over 400 environmental variables used to cre-</p><p>ate 2040 analyses using a correlative SDM called</p><p>MaxEnt (Phillips et al. 2006). Whilst the BIOCLIM</p><p>algorithm is still used for teaching and comparative</p><p>purposes MaxEnt has replaced BIOCLIM as one of</p><p>the leading analysis methods. Guisan et al. (2017)</p><p>have provided details of how environmental variables</p><p>affect different methods and details of how the many</p><p>available ENM/SDM algorithms work. In summary,</p><p>BIOCLIM determines simple ranges of suitable con-</p><p>ditions, whilst MaxEnt provides response curves.</p><p>Bradie and Leunig (2017) identified that the 19 BIO-</p><p>CLIM variables appeared in more than 1000 MaxEnt</p><p>analyses and the four interactive variables of concern</p><p>here (i.e. Bio8, Bio9, Bio18 and Bio19) were all</p><p>found as the most important predictors in some stud-</p><p>ies but were poor predictors in others. The mixed</p><p>results in the importance of these interactive BIO-</p><p>CLIM variables indicate the value of these variables</p><p>is context-dependent. Thus, the inclusion of interac-</p><p>tive BIOCLIM variables in some studies could</p><p>improve the models.</p><p>There is an unstated hypothesis behind the thou-</p><p>sands of ENM/SDM studies that have used the com-</p><p>plete set of 19 BIOCLIM variables from WorldClim.</p><p>This is that the four interactive bioclimatic variables</p><p>change smoothly across regions without sudden dis-</p><p>continuities due to artefacts in the assessment period</p><p>being used. In contrast, the unstated hypothesis in</p><p>the increasing number of studies that cite Escobar</p><p>et al. (2014) and exclude all four variables is that the</p><p>interpolated surfaces for all four interactive variables</p><p>have artefacts that render them unsuitable for use</p><p>anywhere.</p><p>The main aims of this paper are to provide a pre-</p><p>liminary assessment of both these hypotheses and to</p><p>encourage researchers to examine the interpolated</p><p>surfaces for the four interactive variables before their</p><p>use. To do this, an easy-to-use approach is described</p><p>to explore data for the four variables from versions 1</p><p>and 2 of the WorldClim database (Hijmans et al.</p><p>2005; Fick & Hijmans 2017). The geographic extent</p><p>of major variable discontinuities can be easily identi-</p><p>fied and indicative measures of the magnitude of</p><p>these abrupt changes can be quantified. Discontinu-</p><p>ities are related to sudden changes in the quarterly</p><p>measurement periods. Possible actions are described</p><p>to reduce these errors using weekly data estimated</p><p>from the existing monthly data, fixed three monthly</p><p>periods, or more sophisticated water balance analy-</p><p>ses. Means to test the hypotheses associated with the</p><p>use of the four interactive variables more rigorously</p><p>© 2022 Commonwealth Scientific and Industrial Research Organisation. Austral Ecology</p><p>published by John Wiley & Sons Australia, Ltd on behalf of Ecological Society of Australia.</p><p>doi:10.1111/aec.13234</p><p>CHECKING BIOCLIMATIC VARIABLES IN SDMS 1507</p><p>14429993, 2022, 7, D</p><p>ow</p><p>nloaded from</p><p>https://onlinelibrary.w</p><p>iley.com</p><p>/doi/10.1111/aec.13234 by U</p><p>FE</p><p>S - U</p><p>niversidade Federal do E</p><p>spirito Santo, W</p><p>iley O</p><p>nline L</p><p>ibrary on [07/11/2023]. See the T</p><p>erm</p><p>s and C</p><p>onditions (https://onlinelibrary.w</p><p>iley.com</p><p>/term</p><p>s-and-conditions) on W</p><p>iley O</p><p>nline L</p><p>ibrary for rules of use; O</p><p>A</p><p>articles are governed by the applicable C</p><p>reative C</p><p>om</p><p>m</p><p>ons L</p><p>icense</p><p>are described. Results from this exploration can facil-</p><p>itate and encourage variable examination in future</p><p>ENM/SDM studies.</p><p>METHODS</p><p>An open access biodiversity database, the Atlas of Living</p><p>Australia (ALA, www.ala.org.au) (Belbin & Williams 2016)</p><p>was used to check the interactive BIOCLIM variable sur-</p><p>faces available from WorldClim databases. These surfaces</p><p>are available in the ALA at the highest resolution (i.e.</p><p>30 seconds or approximately 1 km2). I accessed the analysis</p><p>facilities of the ALA through its spatial portal (Belbin</p><p>2011), which is available through ‘spatial.ala.org.au’. New</p><p>users need to create an account, including their email</p><p>address and a password, after clicking on the ‘sign up’</p><p>option at the top right of the screen. When logged in, an</p><p>outline map of Australia appears.</p><p>I used the ‘add to map/layer’ command, which brings up</p><p>a window entitled ‘Add environmental and contextual lay-</p><p>ers to the map’. To access WorldClim data I entered</p><p>‘WorldClim’ in the filter box and the 38 matching layers</p><p>representing the 19 BIOCLIM variables for versions 1 and</p><p>2 were listed. For example, I selected the small box at the</p><p>left of ‘WorldClim 2.1: Precipitation – coldest quarter’</p><p>(Bio19) and clicked on the ‘next’ button to select this sur-</p><p>face, which was initially displayed for Australia. Repeated</p><p>clicking on the ‘-‘button at the left of the screen caused the</p><p>surface to be displayed for the whole world.</p><p>I examined global maps for all four variables. For exam-</p><p>ple, I assessed global maps for Bio19 by first loading ver-</p><p>sion 1.4 (Hijmans et al. 2005) and then 2.1 (Fick &</p><p>Hijmans 2017). Loading one surface immediately after the</p><p>other ensures that they overlay exactly. The default opacity</p><p>was 60%, but this was varied by moving the slider (at the</p><p>left of the screen) between 100% (when only the v2.1 map</p><p>was visible) and 0% (when only the v1.4 map was visible).</p><p>Moving between both images made both similarities and</p><p>differences apparent.</p><p>Locations in Brazil were chosen for a detailed examina-</p><p>tion as the Americas were a focus of the Escobar et al.</p><p>(2014) paper and a major discontinuity was clearly visible</p><p>even at the global scale. To examine data for South Amer-</p><p>ica (and later for other regions), I dragged the screen poin-</p><p>ter from left to right tracking westward across the world.</p><p>The latitude and longitude of the screen pointer was shown</p><p>in a small box at the bottom right of the screen. Tracking</p><p>eastward from Australia to South America caused incorrect</p><p>longitude values to be displayed, so only westward tracking</p><p>was used. The data for any selected location on the surface</p><p>below the pointer was checked by double-clicking. Data for</p><p>numerous locations were quickly checked in this way.</p><p>To illustrate how a discontinuity was created, monthly</p><p>temperature and precipitation data for 1970–2000 were</p><p>accessed from the WorldClim website (www.worldclim.</p><p>com/version2) at 30 seconds (~1 km2) resolution (Fick &</p><p>Hijmans 2017) Each download was a ‘zip’ file containing</p><p>12 GeoTiff (.tif) files, one for each month of the year (Jan-</p><p>uary is 1; December is 12). Monthly data were extracted</p><p>for two locations near Pedro Afonso in Brazil using the</p><p>free and open-source cross-platform desktop geographic</p><p>information system application QGIS (htpps://www.qgis.</p><p>org). The GeoTiff files were loaded into QGIS. A .csv file</p><p>was prepared using ExcelTM. This comprised of ‘latitude’</p><p>and ‘longitude’ as headers followed by the data for the two</p><p>locations. The file was read into QGIS and the monthly</p><p>data for the two locations were extracted from the surfaces.</p><p>RESULTS</p><p>Figure 1 shows precipitation of the coldest quarter</p><p>(Bio19) across most of the land areas of the world</p><p>using data from version 2.1 of WorldClim (Fick &</p><p>Hijmans 2017). Only output for version 2.1 is pre-</p><p>sented here as the output for version 1.4 was broadly</p><p>similar. The discontinuities were detected as sudden</p><p>changes in the climatic values, expressed as clear</p><p>contours represented by colours that are not adjacent</p><p>on the colour scale.</p><p>It is important to appreciate that the preparation of</p><p>the image shown in Figure 1 for the ALA involved</p><p>detailed quantitative assessment and provides useful</p><p>information on group differences. The colour scales</p><p>for the locations shown in these maps were originally</p><p>created for the ALA after sorting data for about 115</p><p>million points for each variable (i.e. total land area of</p><p>the Earth minus land area of Antarctica in km2).</p><p>They provide extensive information on statistically</p><p>significant differences between</p><p>right.</p><p>These would normally appear</p><p>in the ALA near the red dot</p><p>but would obscure much of the</p><p>discontinuity Source: World-</p><p>Clim v 2.1 (Fick & Hij-</p><p>mans 2017). (b) Mean</p><p>monthly temperature and pre-</p><p>cipitation data from World-</p><p>Clim for two locations near</p><p>Pedro Afonso, Brazil. Note the</p><p>very slight variation in mean</p><p>monthly temperatures through-</p><p>out the year. The coldest quar-</p><p>ter at 8.94oS, 48.18oW (data</p><p>shown in blue) is January–</p><p>March (a period of very high</p><p>precipitation). About two kilo-</p><p>metres south at 8.96oS,</p><p>48.18oW the coldest quarter</p><p>(data shown in red) is May–</p><p>July (a relatively very dry per-</p><p>iod). Source: WorldClim (Fick</p><p>& Hijmans 2017).</p><p>doi:10.1111/aec.13234 © 2022 Commonwealth Scientific and Industrial Research Organisation. Austral Ecology</p><p>published by John Wiley & Sons Australia, Ltd on behalf of Ecological Society of Australia.</p><p>1510 T. H. BOOTH</p><p>14429993, 2022, 7, D</p><p>ow</p><p>nloaded from</p><p>https://onlinelibrary.w</p><p>iley.com</p><p>/doi/10.1111/aec.13234 by U</p><p>FE</p><p>S - U</p><p>niversidade Federal do E</p><p>spirito Santo, W</p><p>iley O</p><p>nline L</p><p>ibrary on [07/11/2023]. See the T</p><p>erm</p><p>s and C</p><p>onditions (https://onlinelibrary.w</p><p>iley.com</p><p>/term</p><p>s-and-conditions) on W</p><p>iley O</p><p>nline L</p><p>ibrary for rules of use; O</p><p>A</p><p>articles are governed by the applicable C</p><p>reative C</p><p>om</p><p>m</p><p>ons L</p><p>icense</p><p>are not statistically significant. For example, consider</p><p>the very small temperature differences between the</p><p>coldest quarters for the two locations near Pedro</p><p>Alfonso in Brazil shown in Figure 2b. Interpolated</p><p>temperature surfaces are likely, especially in data</p><p>sparse regions such as this, to have a root-mean-</p><p>squared error of about 1°C (Fick & Hijmans 2017).</p><p>So, the discontinuity could be due to a difference</p><p>between the two quarterly assessment periods that is</p><p>not statically significant.</p><p>As Bradie and Leunig (2017) showed, Bio8, Bio9,</p><p>Bio18 and Bio19 are not the most effective of the 19</p><p>BIOCLIM variables, but for some specific studies</p><p>these interactive variables are the most important</p><p>predictors. It is appropriate to assess the interpolated</p><p>surfaces for the four variables before their use or</p><p>default exclusion in SDM/ENM models. The spatial</p><p>portal of the ALA (Belbin 2011) provides a readily</p><p>accessible means to review the WorldClim surfaces.</p><p>Similar systems are becoming widely available as the</p><p>Global Biodiversity Information Facility (GBIF) is</p><p>making ALA-based systems available for many</p><p>countries around the world (Global Biodiversity</p><p>Information Facility 2021).</p><p>The ALA and related systems provide a quick</p><p>visual and quantitative way of identifying likely prob-</p><p>lem areas of discontinuity. A detailed assessment of</p><p>discontinuities for all four interactive variables across</p><p>the whole world would be desirable. However, such a</p><p>comprehensive analysis of every data point and its</p><p>surrounding points would be a major undertaking. In</p><p>the meantime, numerous SDM/ENM studies would</p><p>be inappropriately including the four variables whilst</p><p>others would be inappropriately excluding them.</p><p>Until such time as a detailed analysis can be under-</p><p>taken the approach described here provides an effec-</p><p>tive way of identifying major discontinuities and</p><p>hence variables that need to be excluded.</p><p>Some of these discontinuities could be avoided or</p><p>at least reduced if BIOCLIM variables Bio8, Bio9,</p><p>Bio18 and Bio19 were calculated using consecutive</p><p>periods of 13 weeks rather than quarterly (i.e. con-</p><p>secutive three monthly) periods. Another simple</p><p>alternative would be to use fixed summer or winter</p><p>Fig. 3. (a) Example of dis-</p><p>continuity for precipitation of</p><p>the coldest quarter (Bio19) in</p><p>Africa. The major discontinuity</p><p>is apparent in sub-Saharan</p><p>regions with an abrupt colour</p><p>change from orange to blue.</p><p>This contrasts with areas north</p><p>of the Sahara where gradual</p><p>precipitation changes are indi-</p><p>cated by smooth colour</p><p>changes from orange to yellow</p><p>to green to blue. A red dot</p><p>indicates the example location</p><p>of Jos. Two small kidney-</p><p>shaped areas of less extensive</p><p>discontinuity are also visible at</p><p>the bottom of the map in the</p><p>Democratic Republic of the</p><p>Congo. Source: WorldClim v</p><p>2.1 (Fick & Hijmans 2017).</p><p>(b) Temperature and precipita-</p><p>tion climatic averages for Jos,</p><p>Nigeria. Note the similar two</p><p>coldest quarter periods (July-</p><p>Sept and November–January),</p><p>which correspond with rela-</p><p>tively wet and dry periods.</p><p>Source: Climate-Data.org</p><p>© 2022 Commonwealth Scientific and Industrial Research Organisation. Austral Ecology</p><p>published by John Wiley & Sons Australia, Ltd on behalf of Ecological Society of Australia.</p><p>doi:10.1111/aec.13234</p><p>CHECKING BIOCLIMATIC VARIABLES IN SDMS 1511</p><p>14429993, 2022, 7, D</p><p>ow</p><p>nloaded from</p><p>https://onlinelibrary.w</p><p>iley.com</p><p>/doi/10.1111/aec.13234 by U</p><p>FE</p><p>S - U</p><p>niversidade Federal do E</p><p>spirito Santo, W</p><p>iley O</p><p>nline L</p><p>ibrary on [07/11/2023]. See the T</p><p>erm</p><p>s and C</p><p>onditions (https://onlinelibrary.w</p><p>iley.com</p><p>/term</p><p>s-and-conditions) on W</p><p>iley O</p><p>nline L</p><p>ibrary for rules of use; O</p><p>A</p><p>articles are governed by the applicable C</p><p>reative C</p><p>om</p><p>m</p><p>ons L</p><p>icense</p><p>http://climate-data.org</p><p>Fig. 4. (a) Example of dis-</p><p>continuity for temperature of</p><p>the wettest quarter (Bio8).</p><p>Note a major discontinuity in</p><p>the south-eastern United States</p><p>and a minor discontinuity in</p><p>the south-western United</p><p>States roughly between Los</p><p>Angles and Phoenix. A red dot</p><p>indicates the example location</p><p>of Dothan (Alabama). Source:</p><p>WorldClim v 2.1 (Fick & Hij-</p><p>mans 2017). (b) Temperature</p><p>and precipitation climatic aver-</p><p>ages for Dothan, Alabama.</p><p>Note there are two relatively</p><p>wet periods (June–August and</p><p>December–April). Source:</p><p>NOAA (2021).</p><p>Table 1. Example locations near discontinuities for the four interactive bioclimatic variables and the six continents for which</p><p>the WorldClim website provides data</p><p>Bio8 Bio9 Bio18 Bio19</p><p>Australia 24.88°S, 114.87°E 33.15°S, 144.40°E - -</p><p>18/30°C 11/23°C - -</p><p>Asia 28.58°N, 66.89°E 23.57°N, 81.85°E 21.65°N, 70.40°E 12.95°N, 75.70°E</p><p>11/29°C 18/29°C 100/430 mm 60/2470 mm</p><p>Europe 48.51°N, 5.08°E 44.79°N, 8.19°E - -</p><p>4/18°C 4/22°C - -</p><p>N. Am. 44.42°N, 70.96°W 32.59°N, 96.50°W - -</p><p>1/18°C 10/28°C - -</p><p>S. Am. 30.50°S, 52.73°W - 12.44°S, 45.38°W 8.94°S, 48.18°W</p><p>13/22°C - 155/360 mm 50/780 mm</p><p>Africa 23.45°N, 27.33°E 27.15°N, 20.35°E 10.44°N, 0.18°W 9.90°N, 8.83°E</p><p>15/30°C 15/29°C 85/190 mm 3/760 mm</p><p>This table is intended to provide samples to assist researchers locate example discontinuities. It is not a comprehensive list.</p><p>Dashes indicate combinations which lack obvious discontinuities such as those shown in Figures 1-4. Paired temperature and</p><p>precipitation values are indicative of discontinuity changes in the locations indicated. Note the Bio18 contrasts are all rela-</p><p>tively minor, whilst the Bio19 contrasts are very large.</p><p>doi:10.1111/aec.13234 © 2022 Commonwealth Scientific and Industrial Research Organisation. Austral Ecology</p><p>published by John Wiley & Sons Australia, Ltd on behalf of Ecological Society of Australia.</p><p>1512 T. H. BOOTH</p><p>14429993, 2022, 7, D</p><p>ow</p><p>nloaded from</p><p>https://onlinelibrary.w</p><p>iley.com</p><p>/doi/10.1111/aec.13234 by U</p><p>FE</p><p>S - U</p><p>niversidade Federal do E</p><p>spirito Santo, W</p><p>iley O</p><p>nline L</p><p>ibrary on [07/11/2023]. See the T</p><p>erm</p><p>s and C</p><p>onditions (https://onlinelibrary.w</p><p>iley.com</p><p>/term</p><p>s-and-conditions) on W</p><p>iley O</p><p>nline L</p><p>ibrary for rules of use; O</p><p>A</p><p>articles are governed by the applicable C</p><p>reative C</p><p>om</p><p>m</p><p>ons L</p><p>icense</p><p>three monthly periods, though these would often pro-</p><p>duce discontinuities when these periods changed at</p><p>the equator. A more sophisticated approach would be</p><p>to calculate periodic variables based on weekly water</p><p>balance. This was the method used by the BIOCLIM</p><p>team in 1999 when they added variables beyond the</p><p>set of 19 (Xu & Hutchinson 2011; Booth et al.</p><p>2014). More recently, several other groups have</p><p>explored the use of various evapotranspiration mea-</p><p>sures and water balance methods (Kriticos et al.</p><p>2012; Golicher 2013; Title & Bemmels 2018; Noce</p><p>et al. 2020). Assembling the global databases neces-</p><p>sary to comprehensively test these alternative mea-</p><p>sures is beyond the scope of the present paper. It</p><p>must also be acknowledged</p><p>that different seasons are</p><p>often not strongly expressed in equatorial regions, so</p><p>replacing the four interactive variables with more</p><p>effective measures is always likely to be difficult in</p><p>these areas.</p><p>There are several global databases available that</p><p>provide the set of 19 BIOCLIM variables. World-</p><p>Clim is the most widely used with more than 28 000</p><p>citations in Google ScholarTM. CHELSA with over</p><p>4000 citations is the next most used system (Karger</p><p>et al. 2017). The issues raised here apply to all biocli-</p><p>matic databases that use the four interactive BIO-</p><p>CLIM variables.</p><p>The value of including or excluding the four interac-</p><p>tive variables could be tested by examining previous</p><p>studies. At least one thousand studies have included</p><p>all 19 BIOCLIM variables (Bradie & Leunig 2017)</p><p>and many provide some indication of the relative effec-</p><p>tiveness of these variables as predictors. These studies</p><p>could be examined to determine if the four interactive</p><p>variables are more effective when used in regions</p><p>where discontinuities are not a problem.</p><p>The main aim of this paper was to discourage</p><p>researchers from automatically excluding the four</p><p>interactive BIOCLIM variables from SDM/ENM</p><p>analyses and to encourage them to examine whether</p><p>discontinuities are a concern in the region they are</p><p>examining. The four BIOCLIM variables can some-</p><p>times provide useful information for SDM/ENM</p><p>analyses (Bradie & Leunig 2017), so should not be</p><p>excluded unless necessary.</p><p>A general conclusion from looking at the four sur-</p><p>faces across the whole world is that major discontinu-</p><p>ities are relatively rare in most regions but as shown in</p><p>Table 1 can be considerable in some regions. The dis-</p><p>continuities identified here are artefacts of the mea-</p><p>sures used and such sudden changes are unlikely to be</p><p>biologically meaningful. This suggests that interactive</p><p>BIOCLIM variables should be excluded if there are</p><p>major discontinuities within the area of study.</p><p>It is recommended that researchers use tools</p><p>including the ALA to identify the location of biocli-</p><p>matic artefacts, such as the discontinuities illustrated</p><p>here, to ensure the selection of robust climatic data-</p><p>sets before the use of BIOCLIM variables in SDMs</p><p>or ENMs.</p><p>ACKNOWLEDGEMENTS</p><p>The author thanks the WorldClim team for the devel-</p><p>opment of their interpolated climatic surfaces and the</p><p>derived BIOCLIM variables. The author thanks the</p><p>ALA and QGIS teams for their preparation of power-</p><p>ful open access systems. The author also thanks the</p><p>OpenStreetMap for the use of background mapping</p><p>data of country boundaries. These data are available</p><p>under the Open Data Licence (see www.opendata</p><p>commons.org/licenses/odbl). The author thanks</p><p>Climate-Data.org for permission to use climate data</p><p>for Jos. Luis Escobar provided very helpful comments</p><p>on earlier versions of this manuscript but was not</p><p>available to comment on the later revisions. The</p><p>author also thanks Stephen Roxburgh and Tahir Ali</p><p>for their review comments on various drafts of this</p><p>manuscript and the anonymous reviewers.</p><p>AUTHOR CONTRIBUTIONS</p><p>Trevor Henry Booth: Conceptualization (lead);</p><p>data curation (lead); formal analysis (lead); funding</p><p>acquisition (lead); investigation (lead); methodology</p><p>(lead); project administration (lead); resources (lead);</p><p>software (lead); supervision (lead); validation (lead);</p><p>visualization (lead); writing – original draft (lead);</p><p>writing – review and editing (lead).</p><p>FUNDING STATEMENT</p><p>The author received no funding for this work.</p><p>CONFLICT OF INTEREST</p><p>The author declares that he has no conflicting</p><p>interest.</p><p>PERMISSION TO REPRODUCE MATERIAL</p><p>FROM OTHER SOURCES</p><p>All figures and the table are original.</p><p>DATA AVAILABILITY STATEMENT</p><p>Data used are broadly and openly accessible. Full</p><p>details are provided in the references section. All data</p><p>© 2022 Commonwealth Scientific and Industrial Research Organisation. Austral Ecology</p><p>published by John Wiley & Sons Australia, Ltd on behalf of Ecological Society of Australia.</p><p>doi:10.1111/aec.13234</p><p>CHECKING BIOCLIMATIC VARIABLES IN SDMS 1513</p><p>14429993, 2022, 7, D</p><p>ow</p><p>nloaded from</p><p>https://onlinelibrary.w</p><p>iley.com</p><p>/doi/10.1111/aec.13234 by U</p><p>FE</p><p>S - U</p><p>niversidade Federal do E</p><p>spirito Santo, W</p><p>iley O</p><p>nline L</p><p>ibrary on [07/11/2023]. See the T</p><p>erm</p><p>s and C</p><p>onditions (https://onlinelibrary.w</p><p>iley.com</p><p>/term</p><p>s-and-conditions) on W</p><p>iley O</p><p>nline L</p><p>ibrary for rules of use; O</p><p>A</p><p>articles are governed by the applicable C</p><p>reative C</p><p>om</p><p>m</p><p>ons L</p><p>icense</p><p>http://www.opendatacommons.org/licenses/odbl</p><p>http://www.opendatacommons.org/licenses/odbl</p><p>http://climate-data.org</p><p>for the four interactive BIOCLIM variables examined</p><p>here, as well as the monthly data used in Figure 2b,</p><p>are available from the open access WorldClim web-</p><p>site – https://www.worldclim.org/data/worldclim14.html;</p><p>https://www.worldclim.org/data/worldclim21.html.</p><p>REFERENCES</p><p>Belbin L. (2011) The Atlas of Living Australia’s spatial portal.</p><p>In: Proceedings of the Environmental Information Management</p><p>Conference 2011 (eds M. B. Jones & C. Gries) pp. 39–43.</p><p>University of California, Santa Barbara. Available from</p><p>URL: https://www.ala.org.au/wp-content/uploads/2011/10/</p><p>EIM_ALA.pdf. Accessed 21 August 2022.</p><p>Belbin L. & Williams K. J. (2016) Towards a national bio-</p><p>environmental data facility: Experiences from the Atlas of</p><p>Living Australia. Int. J. Geogr. Inf. Sci. 30, 108–25.</p><p>Booth T. H. (1985) A new method to assist species selection.</p><p>Commonw. Forest Rev. 64, 241–50. https://www.jstor.org/</p><p>stable/42608049</p><p>Booth T. H. (2018) Why understanding the pioneering and</p><p>continuing contributions of BIOCLIM to species</p><p>distribution modelling is important. Austral Ecol. 43, 852–</p><p>60.</p><p>Booth T. H., Nix H. A., Busby J. R. & Hutchinson M. F.</p><p>(2014) BIOCLIM: The first species distribution modelling</p><p>package, its early applications and relevance to most</p><p>current Maxent studies. Divers. Distrib. 20, 1–9.</p><p>Bradie J. & Leunig B. (2017) A quantitative synthesis of the</p><p>importance of variables used in MaxEnt species</p><p>distribution models. J. Biogeogr. 44, 1344–61.</p><p>Burke R. A., Gangull A. & Stoner K. E. (2019) Species</p><p>distribution modelling supports “nectar corridor”</p><p>hypothesis for migratory nectarivorous bats and</p><p>conservation of tropical dry forest. Divers. Distrib. 25,</p><p>1399–141.</p><p>Busby J. R. (1991) BIOCLIM – A bioclimate analysis and</p><p>prediction system. Plant Prot. Q. 6, 8–9.</p><p>Climate-Data, Climate data for cities worldwide. Data for Jos</p><p>are from. https://en.climate-data.org/Africa/Nigeria/plateau/</p><p>jos-46664/. Accessed 21 August 2022.</p><p>Escobar L. E., Lira-Noriega A., Medina-Vogel G. & Peterson</p><p>A. T. (2014) Potential for spread of the white-nose fungus</p><p>(Pseudogymnoascus destructans) in the Americas: Use of</p><p>Maxent and NicheA to assure strict model transference.</p><p>Geospat. Health 9, 221–9.</p><p>Fick S. E. & Hijmans R. J. 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Model. 190, 231–59.</p><p>Sim~oes M., Romero-Alvarez D., Nu~nez-Penichet C., Jim�enez</p><p>L. & Cobos M. E. (2020) General theory and good</p><p>practices in ecological niche modeling: A basic guide.</p><p>Biodiversity Informatics 15, 67–8.</p><p>Title P. O. & Bemmels J. B. (2018) ENVIREM: An expanded</p><p>set of bioclimatic and topographic variables increases</p><p>flexibility and improves performance of ecological niche</p><p>modeling. Ecography 41, 291–307.</p><p>Xu T. & Hutchinson M. F. (2011) ANUCLIM Version 6.1 User</p><p>Guide. The Australian National University, Fenner School</p><p>of Environment and Society, Canberra. Available from</p><p>URL: https://fennerschool.anu.edu.au/files/anuclim61.pdf.</p><p>Accessed 21 August 2022.</p><p>doi:10.1111/aec.13234 © 2022 Commonwealth Scientific and Industrial Research Organisation. Austral Ecology</p><p>published by John Wiley & Sons Australia, Ltd on behalf of Ecological Society of Australia.</p><p>1514 T. H. BOOTH</p><p>14429993, 2022, 7, D</p><p>ow</p><p>nloaded from</p><p>https://onlinelibrary.w</p><p>iley.com</p><p>/doi/10.1111/aec.13234 by U</p><p>FE</p><p>S - U</p><p>niversidade Federal do E</p><p>spirito Santo, W</p><p>iley O</p><p>nline L</p><p>ibrary on [07/11/2023]. See the T</p><p>erm</p><p>s and C</p><p>onditions (https://onlinelibrary.w</p><p>iley.com</p><p>/term</p><p>s-and-conditions) on W</p><p>iley O</p><p>nline L</p><p>ibrary for rules of use; O</p><p>A</p><p>articles are governed by the applicable C</p><p>reative C</p><p>om</p><p>m</p><p>ons L</p><p>icense</p><p>https://www.worldclim.org/data/worldclim14.html</p><p>https://www.worldclim.org/data/worldclim21.html</p><p>https://www.ala.org.au/wp-content/uploads/2011/10/EIM_ALA.pdf</p><p>https://www.ala.org.au/wp-content/uploads/2011/10/EIM_ALA.pdf</p><p>https://www.jstor.org/stable/42608049</p><p>https://www.jstor.org/stable/42608049</p><p>https://en.climate-data.org/Africa/Nigeria/plateau/jos-46664/</p><p>https://en.climate-data.org/Africa/Nigeria/plateau/jos-46664/</p><p>https://living-atlases.gbif.org</p><p>https://living-atlases.gbif.org</p><p>https://rpubs.com/dgolicher/2964</p><p>https://rpubs.com/dgolicher/2964</p><p>http://fennerschool.anu.edu.au/files/anusplin44.pdf</p><p>http://fennerschool.anu.edu.au/files/anusplin44.pdf</p><p>http://www.ncgia.ucsb.edu/SANTA_FE_CD-ROM/sf_papers/hutchinson_michael_africa/africa.html</p><p>http://www.ncgia.ucsb.edu/SANTA_FE_CD-ROM/sf_papers/hutchinson_michael_africa/africa.html</p><p>https://www.ncei.noaa.gov/access/us-climate-normals/#dataset=normals-monthly&timeframe=30&location=AL&station=USW000013839</p><p>https://www.ncei.noaa.gov/access/us-climate-normals/#dataset=normals-monthly&timeframe=30&location=AL&station=USW000013839</p><p>https://www.ncei.noaa.gov/access/us-climate-normals/#dataset=normals-monthly&timeframe=30&location=AL&station=USW000013839</p><p>https://fennerschool.anu.edu.au/files/anuclim61.pdf</p><p>Abstract</p><p>INTRODUCTION</p><p>METHODS</p><p>RESULTS</p><p>aec13234-fig-0001</p><p>DISCUSSION</p><p>aec13234-fig-0002</p><p>aec13234-fig-0003</p><p>aec13234-fig-0004</p><p>ACKNOWLEDGEMENTS</p><p>AUTHOR CONTRIBUTIONS</p><p>FUNDING STATEMENT</p><p>CONFLICT OF INTEREST</p><p>PERMISSION TO REPRODUCE MATERIAL FROM OTHER SOURCES</p><p>DATA AVAILABILITY STATEMENT</p><p>REFERENCES</p>on Integrating GIS and Environmental
Modeling. NCGIA, Santa Barbara, California. Available from
URL: http://www.ncgia.ucsb.edu/SANTA_FE_CD-ROM/sf_
papers/hutchinson_michael_africa/africa.html. Accessed 21
August 2022.
Karger D. N., Conrad O., B€ohner J. et al. (2017)
Climatologies at high resolution for the earth land surface
areas. Scientific Data. 4, 170122.
Kriticos D. J., Webber B. L., Leriche A. et al. (2012)
CliMond: Global high-resolution historical and future
scenario climate surfaces for bioclimatic modelling.
Methods Ecol. Evol. 3, 53–64.
Nix H. A. (1986) A biogeographic analysis of Australian elapid
snakes. In: Atlas of Elapid Snakes of Australia. Australian
Flora and Fauna Series 7 (ed R. Longmore) pp. 4–15.
Bureau of Flora and Fauna, Canberra.
NOAA (2021) National Oceanic & Atmospheric Administration,
Data for Dothan. Available from URL: https://www.ncei.
noaa.gov/access/us-climate-normals/#dataset=normals-mon
thly&timeframe=30&location=AL&station=USW000013839.
Accessed 21 August 2022.
Noce S., Caporaso L. & Santini M. (2020) A new global
dataset of bioclimatic indicators. Scientific Data 7, 398.
Phillips S. J., Anderson R. P. & Schapire R. E. (2006)
Maximum entropy modelling of species geographic
distributions. Ecol. Model. 190, 231–59.
Sim~oes M., Romero-Alvarez D., Nu~nez-Penichet C., Jim�enez
L. & Cobos M. E. (2020) General theory and good
practices in ecological niche modeling: A basic guide.
Biodiversity Informatics 15, 67–8.
Title P. O. & Bemmels J. B. (2018) ENVIREM: An expanded
set of bioclimatic and topographic variables increases
flexibility and improves performance of ecological niche
modeling. Ecography 41, 291–307.
Xu T. & Hutchinson M. F. (2011) ANUCLIM Version 6.1 User
Guide. The Australian National University, Fenner School
of Environment and Society, Canberra. Available from
URL: https://fennerschool.anu.edu.au/files/anuclim61.pdf.
Accessed 21 August 2022.
doi:10.1111/aec.13234 © 2022 Commonwealth Scientific and Industrial Research Organisation. Austral Ecology
published by John Wiley & Sons Australia, Ltd on behalf of Ecological Society of Australia.
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https://www.worldclim.org/data/worldclim14.html
https://www.worldclim.org/data/worldclim21.html
https://www.ala.org.au/wp-content/uploads/2011/10/EIM_ALA.pdf
https://www.ala.org.au/wp-content/uploads/2011/10/EIM_ALA.pdf
https://www.jstor.org/stable/42608049
https://www.jstor.org/stable/42608049
https://en.climate-data.org/Africa/Nigeria/plateau/jos-46664/
https://en.climate-data.org/Africa/Nigeria/plateau/jos-46664/
https://living-atlases.gbif.org
https://living-atlases.gbif.org
https://rpubs.com/dgolicher/2964
https://rpubs.com/dgolicher/2964
http://fennerschool.anu.edu.au/files/anusplin44.pdf
http://fennerschool.anu.edu.au/files/anusplin44.pdf
http://www.ncgia.ucsb.edu/SANTA_FE_CD-ROM/sf_papers/hutchinson_michael_africa/africa.html
http://www.ncgia.ucsb.edu/SANTA_FE_CD-ROM/sf_papers/hutchinson_michael_africa/africa.html
https://www.ncei.noaa.gov/access/us-climate-normals/#dataset=normals-monthly&timeframe=30&location=AL&station=USW000013839
https://www.ncei.noaa.gov/access/us-climate-normals/#dataset=normals-monthly&timeframe=30&location=AL&station=USW000013839
https://www.ncei.noaa.gov/access/us-climate-normals/#dataset=normals-monthly&timeframe=30&location=AL&station=USW000013839
https://fennerschool.anu.edu.au/files/anuclim61.pdf
	 Abstract
	 INTRODUCTION
	 METHODS
	 RESULTS
	aec13234-fig-0001
	 DISCUSSION
	aec13234-fig-0002
	aec13234-fig-0003
	aec13234-fig-0004
	 ACKNOWLEDGEMENTS
	 AUTHOR CONTRIBUTIONS
	 FUNDING STATEMENT
	 CONFLICT OF INTEREST
	 PERMISSION TO REPRODUCE MATERIAL FROM OTHER SOURCES
	 DATA AVAILABILITY STATEMENT
	 REFERENCES