The DOI matches its Crossref record; 78 of the 79 references it cites were found in a registry or an index.
The art of modelling range-shifting species: The art of modelling range-shifting species
Abstract
1. Species are shifting their ranges at an unprecedented rate through human transportation and environmental change. Correlative species distribution models (SDMs) are frequently applied for predicting potential future distributions of range-shifting species, despite these models’ assumptions that species are at equilibrium with the environments used to train (fit) the models, and that the training data are representative of conditions to which the models are predicted. Here we explore modelling approaches that aim to minimize extrapolation errors and assess predictions against prior biological knowledge. Our aim was to promote methods appropriate to range-shifting species. 2. We use an invasive species, the cane toad in Australia, as an example, predicting potential distributions under both current and climate change scenarios. We use four SDM methods, and trial weighting schemes and choice of background samples appropriate for species in a state of spread. We also test two methods for including information from a mechanistic model. Throughout, we explore graphical techniques for understanding model behaviour and reliability, including the extent of extrapolation. 3. Predictions varied with modelling method and data treatment, particularly with regard to the use and treatment of absence data. Models that performed similarly under current climatic conditions deviated widely when transferred to a novel climatic scenario. 4. The results highlight problems with using SDMs for extrapolation, and demonstrate the need for methods and tools to understand models and predictions. We have made progress in this direction and have implemented exploratory techniques as new options in the free modelling software, MaxEnt. Our results also show that deliberately controlling the fit of models and integrating information from mechanistic models can enhance the reliability of correlative predictions of species in non-equilibrium and novel settings. 5. Implications. The biodiversity of many regions in the world is experiencing novel threats created by species invasions and climate change. Predictions of future species distributions are required for management, but there are acknowledged problems with many current methods, and relatively few advances in techniques for understanding or overcoming these. The methods presented in this manuscript and made accessible in MaxEnt provide a forward step.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The present habitat suitability and distribution of each species were estimated using the MaxEnt modelling technique, which employs maximum entropy methods and is particularly resilient when dealing with presence-only species records (Elith et al., 2010).”
From Projecting future climate change impacts on the distribution of the ‘Octopus vulgaris species complex’ (2022) · cites this paper as Elith et al., 2010
In the citing paper
“Without clear records on survey effort across the landscape, we cannot distinguish between areas that are environmentally unsuitable and those that are under-sampled (Elith, Kearney & Phillips 2010: Appendix S1).”
From A maxent predictive model for hunter-gatherer sites in the southern pampas, argentina (2021) · cites this paper as Elith, Kearney & Phillips 2010
In the citing paper
“The executable Maxent software (v.3.4.4) (Phillips, Dudík & Schapire 2021) has several modifiable parameters that influence the model performance, and ultimately the effectiveness of the predictive model (e.g., Barbet-Massin et al. 2012; Elith, Kearney & Phillips 2010; Fourcade et al. 2014; Merow, Smith & Silander 2013; Morales, Fernández & Baca-González 2017; Phillips et al. 2017).”
From A maxent predictive model for hunter-gatherer sites in the southern pampas, argentina (2021) · cites this paper as Elith, Kearney & Phillips 2010
In the citing paper
“The executable Maxent software (v.3.4.4) (Phillips, Dudík & Schapire 2021) has several modifiable parameters that influence the model performance, and ultimately the effectiveness of the predictive model (e.g., Barbet-Massin et al. 2012; Elith, Kearney & Phillips 2010; Fourcade et al. 2014; Merow, Smith & Silander 2013; Morales, Fernández & Baca-González 2017; Phillips et al. 2017).”
From A maxent predictive model for hunter-gatherer sites in the southern pampas, argentina (2021) · cites this paper as Elith, Kearney & Phillips 2010
In the citing paper
“The executable Maxent software (v.3.4.4) (”
From A maxent predictive model for hunter-gatherer sites in the southern pampas, argentina (2021) · cites this paper as Elith, Kearney \u0026 Phillips 2010
Citations
Cites
R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,893
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
Hanley, J. A., & McNeil, B. J. (1982) · Radiology · doi:10.1148/radiology.143.1.7063747 · Cited by 21,856
McCullagh, P., & Nelder, J. A. (1989) · doi:10.1007/978-1-4899-3242-6 · Cited by 16,836
Maximum entropy modeling of species geographic distributions
Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006) · Ecological Modelling · doi:10.1016/j.ecolmodel.2005.03.026 · Cited by 15,396
Self-citationRegression Modeling Strategies
Harrell, F. E. (2001) · Springer series in statistics · doi:10.1007/978-1-4757-3462-1 · Cited by 9,270
Novel methods improve prediction of species’ distributions from occurrence data
Elith*, J., H. Graham*, C., P. Anderson, R., Dudík, M., Ferrier, S., Guisan, A., J. Hijmans, R., Huettmann, F., R. Leathwick, J., Lehmann, A., et al. (2006) · Ecography · doi:10.1111/j.2006.0906-7590.04596.x · Cited by 9,088
Self-citationEcological and Evolutionary Responses to Recent Climate Change
Parmesan, C. (2006) · Annual Review of Ecology, Evolution, and Systematics · doi:10.1146/annurev.ecolsys.37.091305.110100 · Cited by 8,652
Brown, R. A., Hastie, T. J., & Tibshirani, R. J. (1991) · Biometrics · doi:10.2307/2532174 · Cited by 8,263
Koyak, R. A., Hastie, T. J., & Tibshirani, R. J. (1991) · Journal of the American Statistical Association · doi:10.2307/2290538 · Cited by 7,717
Research works printed but not linked: 2
- van Beurden, 1981, Bioclimatic limits to the spread of Bufo marinus in Australia: a baseline, Proceedings of the Ecological Society of Australia, 11, 143
- Lockwood, 2007, Invasion Ecology
Cited by
Sorted by citations
Items 101–200 of 1,002
Delimiting the geographical background in species distribution modelling
Acevedo, P., Jiménez‐Valverde, A., Lobo, J. M., & Real, R. (2012) · Journal of Biogeography · doi:10.1111/j.1365-2699.2012.02713.x · Cited by 182
Zurell, D., Elith, J., & Schröder, B. (2012) · Diversity and Distributions · doi:10.1111/j.1472-4642.2012.00887.x · Cited by 181
Self-citationA closer look at novel climates: new methods and insights at continental to landscape scales
Mahony, C. R., Cannon, A. J., Wang, T., & Aitken, S. N. (2017) · Global Change Biology · doi:10.1111/gcb.13645 · Cited by 178
“…detect projections outside the numerical range of training data in individual variables (e.g., multivariate environmental similarity surfaces, Elith et al., 2010), though applications of Mahalanobis distance for multivariate extrapolation detection are gaining recognition (Mesgaran et al.,…”
BackgroundVelazco, S. J. E., Rose, M. B., de Andrade, A. F. A., Minoli, I., & Franklin, J. (2022) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13874 · Cited by 177
Galante, P. J., Alade, B., Muscarella, R., Jansa, S. A., Goodman, S. M., & Anderson, R. P. (2017) · Ecography · doi:10.1111/ecog.02909 · Cited by 176
Guevara, L., Gerstner, B., Kass, J., & Anderson, R. (2018) · Global Change Biology · doi:10.1111/gcb.13992 · Cited by 167
Climate projections for ecologists
Harris, R. M. B., Grose, M., Lee, G., Bindoff, N. L., Porfirio, L. L., & Fox‐Hughes, P. (2014) · Wiley Interdisciplinary Reviews Climate Change · doi:10.1002/wcc.291 · Cited by 177
Martin, C. S., Giannoulaki, M., Leo, F. D., Scardi, M., Salomidi, M., Knittweis, L., Pace, M. L., Garofalo, G., Gristina, M., Ballesteros, E., et al. (2014) · Scientific Reports · doi:10.1038/srep05073 · Cited by 177
Target‐group backgrounds prove effective at correcting sampling bias in Maxent models
Barber, R. A., Ball, S. G., Morris, R. K. A., & Gilbert, F. S. (2021) · Diversity and Distributions · doi:10.1111/ddi.13442 · Cited by 172
“To create a background, these data were first converted into a continuous surface (Elith et al., 2010).”
MethodStensgaard, A., Utzinger, J., Vounatsou, P., Hürlimann, E., Schur, N., Saarnak, C. F. L., Simoonga, C., Mubita, P., Kabatereine, N. B., Tchuenté, L.-A. T., et al. (2011) · Acta Tropica · doi:10.1016/j.actatropica.2011.11.010 · Cited by 171
Benthos distribution modelling and its relevance for marine ecosystem management
Reiss, H., Birchenough, S., Borja, Á., Buhl-Mortensen, L., Craeymeersch, J., Dannheim, J., Darr, A., Galparsoro, I., Gogina, M., Neumann, H., et al. (2015) · ICES Journal of Marine Science · doi:10.1093/icesjms/fsu107 · Cited by 163
Containment measures limit environmental effects on COVID-19 early outbreak dynamics
Ficetola, G. F., & Rubolini, D. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.144432 · Cited by 177
Which environmental variables should I use in my biodiversity model?
Williams, K. J., Belbin, L., Austin, M. P., Stein, J. L., & Ferrier, S. (2012) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2012.698015 · Cited by 168
Ongoing invasions of the African clawed frog, Xenopus laevis: a global review
Measey, J. G., Rödder, D., Green, S. L., Kobayashi, R., Lillo, F., LOBOS, G. A., Rebelo, R., & Thirion, J. (2012) · Biological Invasions · doi:10.1007/s10530-012-0227-8 · Cited by 168
Detecting range shifts among Australian fishes in response to climate change
Booth, D. J., Bond, N. R., & Macreadie, P. I. (2011) · Marine and Freshwater Research · doi:10.1071/mf10270 · Cited by 169
Will climate change increase the risk of plant invasions into mountains?
Petitpierre, B., McDougall, K. L., Seipel, T. F., Broennimann, O., Guisan, A., & Kueffer, C. (2015) · Ecological Applications · doi:10.1890/14-1871 · Cited by 188
Velásquez‐Tibatá, J., Salaman, P. G. W., & Graham, C. H. (2012) · Regional Environmental Change · doi:10.1007/s10113-012-0329-y · Cited by 164
Garcia, R. A., Burgess, N. D., Cabeza, M., Rahbek, C., & Araújo, M. B. (2011) · Global Change Biology · doi:10.1111/j.1365-2486.2011.02605.x · Cited by 163
Parravicini, V., Azzurro, E., Kulbicki, M., & Belmaker, J. (2015) · Ecology Letters · doi:10.1111/ele.12401 · Cited by 166
Climate change‐induced shifts in fire for Mediterranean ecosystems
Batllori, E., Parisien, M., Krawchuk, M. A., & Moritz, M. A. (2013) · Global Ecology and Biogeography · doi:10.1111/geb.12065 · Cited by 159
A Short Guide to the Climatic Variables of the Last Glacial Maximum for Biogeographers
Varela, S., Lima‐Ribeiro, M. S., & Terribile, L. C. (2015) · PLoS ONE · doi:10.1371/journal.pone.0129037 · Cited by 160
Using species distribution models to inform IUCN Red List assessments
Syfert, M. M., Joppa, L., Smith, M. J., Coomes, D. A., Bachman, S. P., & Brummitt, N. A. (2014) · Biological Conservation · doi:10.1016/j.biocon.2014.06.012 · Cited by 158
Gebrewahid, Y., Abrehe, S., Meresa, E., Eyasu, G., Abay, K., Gebreab, G., Kidanemariam, K., Adissu, G., Gebrekidan, A., & Darcha, G. (2020) · Ecological Processes · doi:10.1186/s13717-019-0210-8 · Cited by 158
“8 (Elith et al. 2010) was used as a cutoff limit to minimize the effect of multicollinearity and model overfitting (Wang et al.”
MethodInfluentialEskildsen, A., le Roux, P. C., Heikkinen, R. K., Høye, T. T., Kissling, W. D., Pöyry, J., Wisz, M. S., & Luoto, M. (2013) · Global Ecology and Biogeography · doi:10.1111/geb.12078 · Cited by 155
Camera, C., Zomeni, Z., Noller, J. S., Zissimos, A. M., Christoforou, I. C., & Bruggeman, A. (2016) · Geoderma · doi:10.1016/j.geoderma.2016.09.019 · Cited by 157
Fischer, D., Thomas, S. M., Niemitz, F., Reineking, B., & Beierkuhnlein, C. (2011) · Global and Planetary Change · doi:10.1016/j.gloplacha.2011.05.008 · Cited by 157
Bosso, L., Febbraro, M. D., Cristinzio, G., Zoina, A., & Russo, D. (2016) · Biological Invasions · doi:10.1007/s10530-016-1118-1 · Cited by 156
“2015), and demonstrate a very high predictive capacity (Elith et al. 2010).”
BackgroundMethodInfluentialBond, N. R., Thomson, J., Reich, P., & Stein, J. L. (2011) · Marine and Freshwater Research · doi:10.1071/mf10286 · Cited by 154
Species-Distribution Modeling: Advantages and Limitations of Its Application. 2. MaxEnt
Lissovsky, A. A., & Dudov, S. V. (2021) · Biology Bulletin Reviews · doi:10.1134/s2079086421030087 · Cited by 152
VisTrails SAHM: visualization and workflow management for species habitat modeling
Morisette, J. T., Jarnevich, C. S., Holcombe, T. R., Talbert, C. B., Ignizio, D. A., Talbert, M. K., Silva, C., Koop, D., Swanson, A., & Young, N. E. (2013) · Ecography · doi:10.1111/j.1600-0587.2012.07815.x · Cited by 151
A translucent box: interpretable machine learning in ecology
Lucas, T. C. D. (2020) · Ecological Monographs · doi:10.1002/ecm.1422 · Cited by 161
Historical distribution of Sundaland’s Dipterocarp rainforests at Quaternary glacial maxima
Raes, N., Cannon, C. H., Hijmans, R. J., Piessens, T., Saw, L. G., van Welzen, P. C., & Slik, F. J. W. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1403053111 · Cited by 149
Beaumont, L. J., Graham, E., Duursma, D. E., Wilson, P. D., Cabrelli, A. L., Baumgartner, J. B., Hallgren, W., Esperón‐Rodríguez, M., Nipperess, D. A., Warren, D. L., et al. (2016) · Ecological Modelling · doi:10.1016/j.ecolmodel.2016.10.004 · Cited by 148
Summers, D., Bryan, B. A., Crossman, N. D., & Meyer, W. S. (2012) · Global Change Biology · doi:10.1111/j.1365-2486.2012.02700.x · Cited by 148
Aryal, A., Shrestha, U. B., Ji, W., Ale, S. B., Shrestha, S., Ingty, T., Maraseni, T., Cockfield, G., & Raubenheimer, D. (2016) · Ecology and Evolution · doi:10.1002/ece3.2196 · Cited by 153
“The density map was then rescaled to 1–20 value classes to prevent extreme down-weighting of highly sampled cells (Elith et al. 2010; Peers et al. 2014).”
Liu, B., Gao, X., Ma, J., Jiao, Z., Xiao, J., Hayat, M. A., & Wang, H. (2019) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2019.01.301 · Cited by 146
Low, B. W., Zeng, Y., Tan, H. H., & Yeo, D. C. J. (2020) · Diversity and Distributions · doi:10.1111/ddi.13211 · Cited by 147
“…of previous studies showing that Maxent model performance frequently peaks at RM values higher than the default of 1, which is often insufficient to reduce model overparameterization and overfitting to acceptable levels (Elith et al., 2010; Radosavljevic & Anderson, 2014; Warren & Seifert, 2011).”
BackgroundMethodInfluentialTULLOCH, A. I. T., Sutcliffe, P., Naujokaitis‐Lewis, I., Tingley, R., Brotons, L., Ferraz, K. M. P. M. D. B., Possingham, H. P., Guisan, A., & Rhodes, J. R. (2016) · Biological Conservation · doi:10.1016/j.biocon.2016.04.023 · Cited by 154
Multiscale, presence‐only habitat suitability models: fine‐resolution maps for eight bat species
Bellamy, C. C., Scott, C. A., & Altringham, J. D. (2013) · Journal of Applied Ecology · doi:10.1111/1365-2664.12117 · Cited by 144
Ihlow, F., Dambach, J., Engler, J. O., Flecks, M., Hartmann, T., Nekum, S., Rajaei, H., & Rödder, D. (2011) · Global Change Biology · doi:10.1111/j.1365-2486.2011.02623.x · Cited by 144
Hotspots of plant invasion predicted by propagule pressure and ecosystem characteristics
Catford, J. A., Vesk, P. A., White, M. D., & Wintle, B. A. (2011) · Diversity and Distributions · doi:10.1111/j.1472-4642.2011.00794.x · Cited by 145
Kumar, S., Graham, J., West, A. M., & Evangelista, P. (2014) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2014.02.007 · Cited by 142
Drawbacks to palaeodistribution modelling: the case of South American seasonally dry forests
Collevatti, R. G., Terribile, L. C., de Oliveira, G. H., Lima‐Ribeiro, M. S., Nabout, J. C., Rangel, T. F., & Diniz‐Filho, J. A. F. (2012) · Journal of Biogeography · doi:10.1111/jbi.12005 · Cited by 142
Climate refugia of snow leopards in High Asia
Li, J., McCarthy, T. M., Wang, H., Weckworth, B., Schaller, G. B., Mishra, C., Lü, Z., & Beissinger, S. R. (2016) · Biological Conservation · doi:10.1016/j.biocon.2016.09.026 · Cited by 147
“We usedmost default settings for the MaxEntmodel (3.3.3k) except we selected the hinge feature (Elith et al., 2010) and generated 30,000 background points across the study region.”
BackgroundMethodInfluentialInvasion trajectory of alien trees: the role of introduction pathway and planting history
Donaldson, J. E., Hui, C., Richardson, D. M., Robertson, M. P., Webber, B. L., & Wilson, J. R. (2013) · Global Change Biology · doi:10.1111/gcb.12486 · Cited by 148
Jayasinghe, S. L., & Kumar, L. (2019) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2019.03.025 · Cited by 143
Managing uncertainty in climate-driven ecological models to inform adaptation to climate change
Littell, J. S., McKenzie, D., Kerns, B. K., Cushman, S., & Shaw, C. G. (2011) · Ecosphere · doi:10.1890/es11-00114.1 · Cited by 138
Modelling the effects of global climate change on Chikungunya transmission in the 21st century
Tjaden, N. B., Suk, J. E., Fischer, D., Thomas, S. M., Beierkuhnlein, C., & Semenza, J. C. (2017) · Scientific Reports · doi:10.1038/s41598-017-03566-3 · Cited by 146
Challenges in Identifying Sites Climatically Matched to the Native Ranges of Animal Invaders
Rodda, G. H., Jarnevich, C. S., & Reed, R. N. (2011) · PLoS ONE · doi:10.1371/journal.pone.0014670 · Cited by 137
Ancillotto, L., Santini, L., Ranc, N., Maiorano, L., & Russo, D. (2016) · Die Naturwissenschaften · doi:10.1007/s00114-016-1334-7 · Cited by 137
“In addition, species distribution models based on presence-only data assume an even sampling effort in the study area (Elith et al. 2010), i.e. that no geographic bias occurs in the presence dataset.”
BackgroundModeling the climatic suitability of leishmaniasis vector species in Europe
Koch, L. K., Kochmann, J., Klimpel, S., & Cunze, S. (2017) · Scientific Reports · doi:10.1038/s41598-017-13822-1 · Cited by 143
Syfert, M. M., Smith, M. J., & Coomes, D. A. (2013) · PLoS ONE · doi:10.1371/annotation/35be5dff-7709-4029-8cfa-f1357e5001f5 · Cited by 137
Lee, D. N., Papeş, M., & Bussche, R. A. V. D. (2012) · PLoS ONE · doi:10.1371/journal.pone.0042466 · Cited by 136
The shaping of genetic variation in edge‐of‐range populations under past and future climate change
Razgour, O., Javier Juste, B., Ibáñez, C., Kiefer, A., Rebelo, H., Puechmaille, S. J., Arlettaz, R., Burke, T. A., Dawson, D. A., Beaumont, M., et al. (2013) · Ecology Letters · doi:10.1111/ele.12158 · Cited by 135
Jueterbock, A. O., Smolina, I., Coyer, J. A., & Hoarau, G. G. (2016) · Ecology and Evolution · doi:10.1002/ece3.2001 · Cited by 136
“Habitat suitability for F. distichus under present-day and future environmental conditions was estimated using correlative Ecological Niche Models (ENMs) compiled with the program MAXENT v3.”
BackgroundInfluentialFonseca, M. G., Alves, L. M., Aguiar, A. P., Arai, E., Anderson, L. O., Rosan, T. M., Shimabukuro, Y. E., & Aragão, L. E. O. C. (2019) · Global Change Biology · doi:10.1111/gcb.14709 · Cited by 140
Animal invaders threaten protected areas worldwide
Liu, X., Blackburn, T. M., Song, T., Wang, X., Huang, C., & Li, Y. (2020) · Nature Communications · doi:10.1038/s41467-020-16719-2 · Cited by 132
Do Ecological Niche Models Accurately Identify Climatic Determinants of Species Ranges?
Searcy, C. A., & Shaffer, H. B. (2016) · The American Naturalist · doi:10.1086/685387 · Cited by 127
“…variable, based on visual examinations of the most dissimilar variable plots and determination of which variable is assigned the largest geographic space (i.e., the variable that is expected to change the most between now and 2070 over the geographic space being considered; Elith et al. 2010).”
BackgroundInfluentialTransferring biodiversity models for conservation: Opportunities and challenges
Sequeira, A. M. M., Bouchet, P. J., Yates, K. L., Mengersen, K. L., & Caley, M. J. (2018) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12998 · Cited by 131
“Integrating information from mechanistic models can improve trust in the resulting predictions in this context (Elith et al., 2010).”
BackgroundImpacts of climate change on distributions and diversity of ungulates on the Tibetan Plateau
Luo, Z., Jiang, Z., & Tang, S. (2014) · Ecological Applications · doi:10.1890/13-1499.1 · Cited by 133
Thorson, J. T., Pinsky, M. L., & Ward, E. J. (2016) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12567 · Cited by 132
Soley‐Guardia, M., Alvarado‐Serrano, D. F., & Anderson, R. P. (2024) · Ecography · doi:10.1111/ecog.06852 · Cited by 134
“For example, MaxEnt can calculate the degree of environmental novelty across space (‘MESS’ and ‘MoD’ analyses) as well as how much the prediction depends on the particular extrapolation method (‘clamping analysis’; Elith et al. 2010).”
MethodHof, C., Voskamp, A., Biber, M. F., Böhning-Gaese, K., Engelhardt, E. K., Niamir, A., Willis, S. G., & Hickler, T. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1807745115 · Cited by 131
A comparison of M axlike and M axent for modelling species distributions
Merow, C., & Silander, J. A. (2013) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12152 · Cited by 130
Plant invasions are context‐dependent: multiscale effects of climate, human activity and habitat
González‐Moreno, P., Diez, J. M., Ibáñez, I., Font, X., & Vilà, M. (2014) · Diversity and Distributions · doi:10.1111/ddi.12206 · Cited by 130
Maxent-directed field surveys identify new populations of narrowly endemic habitat specialists
Rhoden, C. M., Peterman, W. E., & Taylor, C. A. (2017) · PeerJ · doi:10.7717/peerj.3632 · Cited by 133
Potential distribution of pine wilt disease under future climate change scenarios
Hirata, A., Nakamura, K., Nakao, K., Kominami, Y., Tanaka, N., Ohashi, H., Takano, K. T., Takeuchi, W., & Matsui, T. (2017) · PLoS ONE · doi:10.1371/journal.pone.0182837 · Cited by 137
Climate-driven range shifts of the king penguin in a fragmented ecosystem
Cristofari, R., Liu, X., Bonadonna, F., Cherel, Y., Pistorius, P. A., Maho, Y. L., Raybaud, V., Stenseth, N. C., Bohec, C. L., & Trucchi, E. (2018) · Nature Climate Change · doi:10.1038/s41558-018-0084-2 · Cited by 132
Ogden, N. H., Wilson, J. R., Richardson, D. M., Hui, C., Davies, S. J., Kumschick, S., Roux, J. J. L., Measey, J. G., Saul, W., & Pulliam, J. R. C. (2019) · Royal Society Open Science · doi:10.1098/rsos.181577 · Cited by 142
“The same is true of EIDs when they are introduced into an immunologically naive population [15] .”
BackgroundHigashida, R. T., Hieshima, G. B., Halbach, V. V., Bentson, J. R., & Goto, K. (1986) · PubMed · Cited by 128
Briscoe, N. J., Kearney, M., Taylor, C. A., & Wintle, B. A. (2016) · Global Change Biology · doi:10.1111/gcb.13280 · Cited by 129
“…sets had similar predictive power as measured by AUC (a widely used metric) under the current climate, yet © 2016 John Wiley & Sons Ltd, Global Change Biology, 22, 2425–2439 produced very different forecasts of species responses to climate change (Elith et al., 2010; Synes & Osborne, 2011).”
BackgroundMethodInfluentialSelf-citationApplications and implications of ecological energetics
Tomlinson, S., Arnall, S. G., Munn, A. J., Bradshaw, D., Maloney, S. K., Dixon, K. W., & Didham, R. K. (2014) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2014.03.003 · Cited by 131
Barbet‐Massin, M., & Jetz, W. (2014) · Diversity and Distributions · doi:10.1111/ddi.12229 · Cited by 126
Morán‐Ordóñez, A., Lahoz‐Monfort, J. J., Elith, J., & Wintle, B. A. (2016) · Global Ecology and Biogeography · doi:10.1111/geb.12545 · Cited by 125
Self-citationMathewson, P. D., Moyer‐Horner, L., Beever, E. A., Briscoe, N. J., Kearney, M., Yahn, J. M., & Porter, W. P. (2016) · Global Change Biology · doi:10.1111/gcb.13454 · Cited by 126
“One possibility is the integration of the two approaches using mechanistic model outputs as inputs to a correlative model (Elith et al., 2010).”
BackgroundMethodResultInfluentialSelf-citationPotential impact of climate change on the distribution of six invasive alien plants in Nepal
Shrestha, U. B., Sharma, K. P., Devkota, A., Siwakoti, M., & Shrestha, B. B. (2018) · Ecological Indicators · doi:10.1016/j.ecolind.2018.07.009 · Cited by 129
“The multiple presence points were removed to retain only one presence point pergrid of 30-arc-sec (∼1×1 km) to correspond the spatial resolution of the environmental variables (Elith et al., 2010).”
BackgroundWinters, G., Beer, S., Willette, D. A., Viana, I. G., Chiquillo, K. L., Beca‐Carretero, P., Villamayor, B. M. R., Azcárate-García, T., Shem‐Tov, R., Mwabvu, B., et al. (2020) · Frontiers in Marine Science · doi:10.3389/fmars.2020.00300 · Cited by 126
Gómez, C., Tenorio, E. A., Montoya, P., & Cadena, C. D. (2016) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2015.2458 · Cited by 123
Biomechanics meets the ecological niche: the importance of temporal data resolution
Kearney, M., Matzelle, A., & Helmuth, B. S. T. (2012) · Journal of Experimental Biology · doi:10.1242/jeb.059634 · Cited by 125
Self-citationMartínez, B. D. ‐., Arenas, F., Trilla, A., Viejo, R. M., & Carreño, F. (2014) · Global Change Biology · doi:10.1111/gcb.12655 · Cited by 126
Wielstra, B., Crnobrnja‐Isailović, J., Litvinchuk, S. N., Reijnen, B. T., Skidmore, A. K., Sotiropoulos, K., Toxopeus, A. G., Tzankov, N., Vukov, T., & Arntzen, J. W. (2013) · Frontiers in Zoology · doi:10.1186/1742-9994-10-13 · Cited by 122
Climate change amplifies plant invasion hotspots in Nepal
Shrestha, U. B., & Shrestha, B. B. (2019) · Diversity and Distributions · doi:10.1111/ddi.12963 · Cited by 136
Pili, A. N., Tingley, R., Sy, E. Y., Diesmos, M. L. L., & Diesmos, A. C. (2020) · Scientific Reports · doi:10.1038/s41598-020-64568-2 · Cited by 122
Zhan, P., Wang, F., Xia, P., Zhao, G., Wei, M., Wei, F., & Han, R. (2021) · Industrial Crops and Products · doi:10.1016/j.indcrop.2021.114416 · Cited by 120
Zwiener, V. P., Padial, A. A., Marques, M. C. M., Faleiro, F. V., Loyola, R. A., & Peterson, A. T. (2017) · Diversity and Distributions · doi:10.1111/ddi.12588 · Cited by 126
Coxen, C. L., Frey, J. K., Carleton, S. A., & Collins, D. P. (2017) · Global Ecology and Conservation · doi:10.1016/j.gecco.2017.08.001 · Cited by 127
Predicting the Impact of Climate Change on Threatened Species in UK Waters
Jones, M. C., Dye, S. R., Fernandes, J. A., Frölicher, T. L., Pinnegar, J. K., Warren, R., & Cheung, W. W. L. (2013) · PLoS ONE · doi:10.1371/journal.pone.0054216 · Cited by 124
Shearer, F. M., Huang, Z., Weiss, D. J., Wiebe, A., Gibson, H. S., Battle, K. E., Pigott, D. M., Brady, O. J., Putaporntip, C., Jongwutiwes, S., et al. (2016) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0004915 · Cited by 115
“The MESS calculation produces negative values for novel environments, locations where at least one covariate has a value that is outside the range of reference values (hereafter extrapolation), and positive values for locations within this range (hereafter interpolation).”
MethodInfluentialA global map of suitability for coastal Vibrio cholerae under current and future climate conditions
Escobar, L. E., Ryan, S. J., Ibarra, A. M. S., Finkelstein, J. L., King, C. A., Qiao, H., & Polhemus, M. E. (2015) · Acta Tropica · doi:10.1016/j.actatropica.2015.05.028 · Cited by 117
Shabani, F., Ahmadi, M., Kumar, L., Solhjouy‐Fard, S., Tehrany, M. S., Shabani, F., Kalantar, B., & Esmaeili, A. (2020) · Ecological Indicators · doi:10.1016/j.ecolind.2020.106436 · Cited by 122
“, 2011); therefore, for a realistic result, the dataset should be as accurately representative of the known distribution of the species of the selected areas as possible (Elith et al., 2010).”
BackgroundBuckley, L. B., Waaser, S. A., MacLean, H. J., & Fox, R. (2011) · Ecology · doi:10.1890/11-0066.1 · Cited by 115
Iverson, L. R., Prasad, A., Matthews, S. N., & Peterson, D. L. (2011) · Ecosystems · doi:10.1007/s10021-011-9456-4 · Cited by 115
Fischer, D., Thomas, S. M., Neteler, M., Tjaden, N. B., & Beierkuhnlein, C. (2014) · Eurosurveillance · doi:10.2807/1560-7917.es2014.19.6.20696 · Cited by 119
The interplay of climate and land use change affects the distribution of EU bumblebees
Marshall, L., Biesmeijer, J. C., Rasmont, P., Vereecken, N. J., Dvořák, L., Fitzpatrick, Ú., Francis, F., Neumayer, J., Ødegaard, F., Paukkunen, J., et al. (2017) · Global Change Biology · doi:10.1111/gcb.13867 · Cited by 115
“An important role of bio-diversity conservation research is to understand and estimate potential changes in biodiversity alongside changing abiotic and biotic conditions (Elith, Kearney, & Phillips, 2010; Porfirio et al., 2014).”
Fischer, D., Moeller, P., Thomas, S. M., Naucke, T. J., & Beierkuhnlein, C. (2011) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0001407 · Cited by 127
Verbruggen, H., Tyberghein, L., Belton, G. S., Mineur, F., Jueterbock, A., Hoarau, G. G., Gurgel, C. F. D., & Clerck, O. D. (2013) · PLoS ONE · doi:10.1371/journal.pone.0068337 · Cited by 114
Macroecological conclusions based on IUCN expert maps: A call for caution
Herkt, K. M. B., Skidmore, A. K., & Fahr, J. (2017) · Global Ecology and Biogeography · doi:10.1111/geb.12601 · Cited by 114
A climate-associated multispecies cryptic cline in the northwest Atlantic
Stanley, R. R. E., DiBacco, C., Lowen, B., Beiko, R. G., Jeffery, N. W., Wyngaarden, M. V., Bentzen, P., Brickman, D., Benestan, L., Bernatchez, L., et al. (2018) · Science Advances · doi:10.1126/sciadv.aaq0929 · Cited by 112
“We conducted further tests on the applicability accuracy of each model, including clamping and multivariate environmental similarity surfaces analysis (46), according to methods outlined in Lowen and DiBacco (47).”
MethodHow complex should models be? Comparing correlative and mechanistic range dynamics models
Fordham, D. A., Bertelsmeier, C., Brook, B. W., Early, R., Neto, D., Brown, S. C., Ollier, S., & Araújo, M. B. (2017) · Global Change Biology · doi:10.1111/gcb.13935 · Cited by 113
Risks of Biological Invasion on the Belt and Road
Liu, X., Blackburn, T. M., Song, T., Li, X., Huang, C., & Li, Y. (2019) · Current Biology · doi:10.1016/j.cub.2018.12.036 · Cited by 113
Metadata
- Authors
- Elith, J., Kearney, M., & Phillips, S.
- Affiliations
- The University of Melbourne (AU)
- AT&T (United States) (US)
- Corresponding author
- Elith, Jane
- Venue
- Methods in Ecology and Evolution
- Publisher
- Wiley
- Published
- 2010-05-24
- Volume
- 1
- Issue
- 4
- Pages
- 330-342
- ISSN
- 2041-210X (ISSN-L)
- OpenAlex
- W2100533358
- Semantic Scholar
- 57fbdc5ff136d10620ea5204db024583249f9550
- Cited by
- 2,813
- References
- 79
- Citations by year
- 2026: 190 · 2025: 230 · 2024: 252 · 2023: 251 · 2022: 240 · 2021: 219 · 2020: 199 · 2019: 184 · 2018: 163 · 2017: 179 · 2016: 152 · 2015: 128 · 2014: 159 · 2013: 107 · 2012: 100
- FWCI
- 50.92
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Methods in Ecology and Evolution (publishedVersion)
- Topics
- Physical Sciences › Environmental Science › Ecological Modeling › Species Distribution and Climate Change
- Physical Sciences › Environmental Science › Nature and Landscape Conservation › Ecology and Vegetation Dynamics Studies
- Physical Sciences › Environmental Science › Ecology › Animal Ecology and Behavior Studies
- Keywords
- species distribution modeling, extrapolation error, Rhinella marina, invasive species, climate change scenarios, absence data, MaxEnt, model reliability, species spread
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
