Ecological processes are often spatially and temporally structured, potentially leading to autocorrelation either in environmental variables or species distribution data. Because of that, spatially-biased in-situ samples or predictors might affect the outcomes of ecological models used to infer the geographic distribution of species and diversity. There is a vast heterogeneity of methods and approaches to assess and measure spatial bias; this paper aims at addressing the spatial component of data-driven biases in species distribution modelling, and to propose potential solutions to explicitly test and account for them. Our major goal is not to propose methods to remove spatial bias from the modelling procedure, which would be impossible without proper knowledge of all the processes generating it, but rather to propose alternatives to explore and handle it. In particular, we propose and describe three main strategies that may provide a fair account of spatial bias, namely: (i) how to represent spatial bias; (ii) how to simulate null models based on virtual species for testing biogeographical and species distribution hypotheses; and (iii) how to make use of spatial bias - in particular related to sampling effort - as a leverage instead of a hindrance in species distribution modelling. We link these strategies with good practice in accounting for spatial bias in species distribution modelling.

Biogeography BayCEER University of Bayreuth Universitaetsstraße 30 95440 Bayreuth Germany

BIOME Lab Department of Biological Geological and Environmental Sciences Alma Mater Studiorum University of Bologna via Irnerio 42 40126 Bologna Italy

Center for Biodiversity and Global Change Yale University New Haven CT USA

CICGE Universidade do Porto Porto Portugal

Czech University of Life Sciences Prague Faculty of Environmental Sciences Department of Spatial Sciences Kamýcka 129 Praha Suchdol 16500 Czech Republic

Department of Biogeography and Global Change Museo Nacional de Ciencias Naturales Madrid Spain

Department of Botany Institute of Ecology and Earth Science University of Tartu J Liivi 2 50409 Tartu Estonia

Department of Computer Science and Engineering Alma Mater Studiorum University of Bologna via Irnerio 42 40126 Bologna Italy

Department of Ecology and Evolution University of Lausanne 1015 Lausanne Switzerland

Department of Life Health and Environmental Sciences University of L'Aquila Piazzale Salvatore Tommasi 1 67100 L'Aquila Italy

Department of Plant Ecology Institute of Landscape and Plant Ecology University of Hohenheim Stuttgart Germany

Dept of Ecology and Evolutionary Biology Yale University New Haven CT USA

Evolutionary Ecology and Genetics Group Earth and Life Institute UCLouvain 1348 Louvain la Neuve Belgium

Faculty of Forestry and Wood Sciences Czech University of Life Sciences Prague Prague Suchdol Czech Republic

Federal University of Goiás Campus Central Anápolis Brazil

Georges Lemaître Center for Earth and Climate Research Earth and Life Institute UCLouvain Louvain la Neuve Belgium

Institute of Earth Surface Dynamics University of Lausanne 1015 Lausanne Switzerland

Institute of Geoecology and Geoinformation Adam Mickiewicz University Krygowskiego 10 61 680 Poznan Poland

Knowledge Infrastructures Campus Fryslan University of Groningen Leeuwarden The Netherlands

MARBEC Univ Montpellier CNRS Ifremer IRD Sète France

Rui Nabeiro Biodiversity Chair MED Institute University of Évora Évora Portugal

School of Geography University of Nottingham Nottingham UK

UMR CNRS 7058 Ecologie et Dynamique des Systèmes Anthropisés Université de Picardie Jules Verne 1 Rue des Louvels 80000 Amiens France

University of Leeds Leeds UK

University of Parma Parma Italy

University of Sassari Department of Chemistry Physics Mathematics and Natural Sciences Sassari Italy

Zobrazit více v PubMed

Draper, D. Assessment and propagation of model uncertainty.

Le Rest, K., Pinaud, D., Monestiez, P., Chadoeuf, J. & Bretagnolle, V. Spatial leave-one-out cross-validation for variable selection in the presence of spatial autocorrelation. DOI

Pereira, J., Saura, S. & Jordan, F. Single-node vs. multi-node centrality in landscape graph analysis: key habitat patches and their protection for 20 bird species in NE Spain. DOI

Van Horne, B. Density as a misleading indicator of habitat quality. DOI

Ricotta, C., Godefroid, S. & Rocchini, D. Patterns of native and exotic species richness in the urban flora of Brussels: rejecting the “rich get richer” model. DOI

Marcantonio, M., Rocchini, D., Geri, F., Bacaro, G. & Amici, V. Biodiversity, roads, & landscape fragmentation: Two Mediterranean cases. DOI

Newmark, W. D., Jenkins, C. N., Pimm, S. L., McNeally, P. B. & Halley, J. M. Targeted habitat restoration can reduce extinction rates in fragmented forests. PubMed DOI PMC

Guisan, A. & Thuiller, W. Predicting species distribution: offering more than simple habitat models. PubMed DOI

Guisan, A. et al. Predicting species distributions for conservation decisions. PubMed DOI PMC

Lecours, V., Gabor, L., Edinger, E. and Devillers, R. Fine-scale habitat characterization of The Gully, the Flemish Cap, and the Orphan Knoll, Northwest Atlantic, with a focus on cold-water corals. In

Santini, L., Benitez-Lopez, A., Maiorano, L., Cengic, M. & Huijbregts, M. A. Assessing the reliability of species distribution projections in climate change research. DOI

Segal, R. D., Massaro, M., Carlile, N. & Whitsed, R. Small-scale species distribution model identifies restricted breeding habitat for an endemic island bird. DOI

Dallas, T. A. & Hastings, A. Habitat suitability estimated by niche models is largely unrelated to species abundance. DOI

Lenoir, J. et al. Species better track climate warming in the oceans than on land. PubMed DOI

Bokma, F., Bokma, J. & Monkkonen, M. Random processes and geographic species richness patterns: Why so few species in the north? DOI

Schwartz, M. A. The importance of stupidity in scientific research. PubMed DOI

Guisan, A., Thuiller, W. & Zimmermann, N.E. Habitat Suitability and Distribution Models: With Applications in R. (Cambridge University Press, 2017).

Bittner, T., Jaeschke, A., Reineking, B. & Beierkuhnlein, C. Comparing modelling approaches at two levels of biological organisation - Climate change impacts on selected Natura 2000 habitats. DOI

Saupe, E. E. et al. Variation in niche and distribution model performance: The need for a priori assessment of key causal factors. DOI

Inman, R., Franklin, J., Esque, T. & Nussear, K. Comparing sample bias correction methods for species distribution modeling using virtual species. DOI

Thompson, J. N. Variation in interspecific interactions. DOI

Pereira, J., Battiston, F. & Jordan, F. Priority areas for protection of plant-pollinator interaction networks in the Atlantic Forest. DOI

Tobler, M. W. et al. Joint species distribution models with species correlations and imperfect detection. PubMed DOI

Gavish, Y. et al. Accounting for biotic interactions through alpha-diversity constraints in stacked species distribution models. DOI

Norberg, A. et al. A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels. DOI

Zurell, D. et al. Testing species assemblage predictions from stacked and joint species distribution models. DOI

Wisz, M. S. et al. The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling. PubMed DOI PMC

Mateo, R. G., Felicisimo, A. M., Pottier, J., Guisan, A. & Munoz, J. Do stacked species distribution models reflect altitudinal diversity patterns? PubMed DOI PMC

Peterson, A. T., Navarro-Siguenza, A. G. & Benitez-Diaz, H. The need for continued scientific collecting; a geographic analysis of Mexican bird specimens. DOI

Hirzel, A. & Guisan, A. Which is the optimal sampling strategy for habitat suitability modelling. DOI

Albert, C. H., Graham, C. H., Yoccoz, N. G., Zimmermann, N. E. & Thuiller, W. Applied sampling in ecology and evolution - integrating questions and designs. DOI

Leitao, P. J., Moreira, F. & Osborne, P. E. Effects of geographical data sampling bias on habitat models of species distributions: a case study with steppe birds in southern Portugal. DOI

Tessarolo, G., Rangel, T. F., Araujo, M. B. & Hortal, J. Uncertainty associated with survey design in species distribution models. DOI

Vollering, J., Halvorsen, R., Auestad, I. & Rydgren, K. Bunching up the background betters bias in species distribution models. DOI

Tessarolo, G., Lobo, J. M., Rangel, T. F. & Hortal, J. High uncertainty in the effects of data characteristics on the performance of species distribution models. DOI

Graham, C. H. et al. The influence of spatial errors in species occurrence data used in distribution models. DOI

Moudry, V. & Simova, P. Influence of positional accuracy, sample size and scale on modelling species distributions: a review. DOI

Hefley, T. J., Brost, B. M. & Hooten, M. B. Bias correction of bounded location errors in presence-only data. DOI

Guisan, A. & Rahbek, C. SESAM - a new framework integrating macroecological and species distribution models for predicting spatio-temporal patterns of species assemblages. DOI

Jaeschke, A. et al. Biotic interactions in the face of climate change: a comparison of three modelling approaches. PubMed DOI PMC

Dawson, M. N. et al. An horizon scan of biogeography. PubMed DOI PMC

Bruelheide, H. et al. sPlot - A new tool for global vegetation analyses. DOI

Sabatini, F. M. et al. sPlotOpen—An environmentally balanced, open-access, global dataset of vegetation plots. DOI

Zizka, A. et al. CoordinateCleaner: Standardized cleaning of occurrence records from biological collection databases. DOI

Anderson, R. P. et al. Optimizing biodiversity informatics to improve information flow, data quality, and utility for science and society. DOI

Grattarola, F., Bowler, D. & Keil, P. Integrating presence-only and presence-absence data to model changes in species geographic ranges: An example of yaguarundí in Latin America. Preprint available at EcoEvorxiv: 10.32942/osf.io/67c4u (2022).

Ficetola, G. F. et al. An evaluation of the robustness of global amphibian range maps. DOI

Williams, K. J., Belbin, L., Austin, M. P., Stein, J. L. & Ferrier, S. Which environmental variables should I use in my biodiversity model? DOI

Lomolino, M.V. Conservation biogeography. In

Kuper, W., Sommer, J. H., Lovett, J. C. & Barthlott, W. Deficiency in African plant distribution data—missing pieces of the puzzle. DOI

Duputie, A., Zimmermann, N. E. & Chuine, I. Where are the wild things? Why we need better data on species distribution. DOI

Sousa-Baena, M. S., Garcia, L. C. & Peterson, A. T. Completeness of digital accessible knowledge of the plants of Brazil and priorities for survey and inventory. DOI

Meyer, C., Kreft, H., Guralnick, R. & Jetz, W. Global priorities for an effective information basis of biodiversity distributions. PubMed DOI PMC

Wuest, R. O. et al. Macroecology in the age of Big Data - Where to go from here? DOI

Dennis, R. L. H., Sparks, T. H. & Hardy, P. B. Bias in butterfly distribution maps: the effects of sampling effort. DOI

Hortal, J., Jimenez-Valverde, J., Gomez, J. F., Lobo, J. M. & Baselga, A. Historical bias in biodiversity inventories affects the observed environmental niche of the species. DOI

Kéry, M. Towards the modelling of true species distributions. DOI

Gaiji, S. et al. Content assessment of the primary biodiversity data published through GBIF network: status, challenges and potentials.

Hortal, J. et al. Seven shortfalls that beset large-scale knowledge of biodiversity. DOI

Anderson, R.P. et al. Final report of the task group on GBIF data fitness for use in distribution modelling. Global Biodiversity Information Facility. 1-27(2016).

Girardello, M. et al. Gaps in butterfly inventory data: a global analysis. DOI

Moudrý, V. & Devillers, R. Quality and usability challenges of global marine biodiversity databases: An example for marine mammal data. DOI

Hughes, A. C. et al. Sampling biases shape our view of the natural world. DOI

Raja, N. B. et al. Colonial history and global economics distort our understanding of deep-time biodiversity. PubMed DOI

Higgins, S. I. et al. A physiological analogy of the niche for projecting the potential distribution of plants. DOI

Owens, H. L. et al. Constraints on interpretation of ecological niche models by limited environmental ranges on calibration areas. DOI

Yates, K. L. et al. Outstanding challenges in the transferability of ecological models. PubMed DOI

Qiao, H. et al. An evaluation of transferability of ecological niche models. DOI

Stohlgren, T. J., Jarnevich, C. S., Esaias, W. E. & Morisette, J. T. Bounding species distribution models. DOI

Mesgaran, M. B., Cousens, R. D. & Webber, B. L. Here be dragons: a tool for quantifying novelty due to covariate range and correlation change when projecting species distribution models. DOI

Meyer, H. & Pebesma, E. Predicting into unknown space? Estimating the area of applicability of spatial prediction models. DOI

Shcheglovitova, M. & Anderson, R. P. Estimating optimal complexity for ecological niche models: A jackknife approach for species with small sampl6e sizes. DOI

De Oliveira, G., Rangel, T. F., Lima-Ribeiro, M. S., Terribile, L. C. & Diniz-Filho, J. A. F. Evaluating, partitioning, and mapping the spatial autocorrelation component in ecological niche modeling: a new approach based on environmentally equidistant records. DOI

Ploton, P. et al. Spatial validation reveals poor predictive performance of large-scale ecological mapping models. PubMed DOI PMC

Tessarolo, G., Ladle, R. J., Lobo, J. M., Rangel, T. F. & Hortal, J. Using maps of biogeographical ignorance to reveal the uncertainty in distributional data hidden in species distribution models. DOI

Thibaud, E., Petitpierre, B., Broennimann, O., Davison, A. C. & Guisan, A. Measuring the relative effect of factors affecting species distribution model predictions. DOI

Chevalier, M. et al. Low spatial autocorrelation in mountain biodiversity data and model residuals. DOI

Meyer, H. & Pebesma, E. Machine learning-based global maps of ecological variables and the challenge of assessing them. PubMed DOI PMC

Bruelheide, H. et al. Global trait-environment relationships of plant communities. PubMed DOI

Heberling, J. M., Miller, J. T., Noesgaard, D., Weingart, S. B. & Schigel, D. Data integration enables global biodiversity synthesis. PubMed DOI PMC

Maldonado, C. et al. Species diversity and distribution in the era of Big Data. PubMed DOI PMC

Troudet, J. et al. Taxonomic bias in biodiversity data and societal preferences. PubMed DOI PMC

Nunez, M. A. & Amano, T. Monolingual searches can limit and bias results in global literature reviews. PubMed DOI

Nunez, M. A., Chiuffo, M. C., Pauchard, A. & Zenni, R. D. Making ecology really global. PubMed DOI

Adamo, M. et al. Plant scientists’ research attention is skewed towards colourful, conspicuous and broadly distributed flowers. PubMed DOI

Sanchez-Fernandez, D. et al. Don’t forget subterranean ecosystems in climate change agendas. DOI

Bini, L. M., Diniz-Filho, J. A. F., Rangel, T. F. L., Bastos, R. P. & Pinto, M. P. Challenging Wallacean and Linnean shortfalls: knowledge gradients and conservation planning in a biodiversity hotspot. DOI

Oliver, R. Y., Meyer, C., Ranipeta, A., Winner, K. & Jetz, W. Global and national trends, gaps, and opportunities in documenting and monitoring species distributions. PubMed DOI PMC

Sastre, P. & Lobo, J. M. Taxonomist survey biases and the unveiling of biodiversity patterns. DOI

Boakes, E. H. et al. Distorted views of biodiversity: spatial and temporal bias in species occurrence data. PubMed DOI PMC

Yang, W., Ma, K. & Kreft, H. Geographical sampling bias in a large distributional database and its effects on species richness-environment models. DOI

Kadmon, R., Farber, O. & Danin, A. Effect of roadside bias on the accuracy of predictive maps produced by bioclimatic models. DOI

Oliveira, U. et al. The strong influence of collection bias on biodiversity knowledge shortfalls of Brazilian terrestrial biodiversity. DOI

Geldmann, J. et al. What determines spatial bias in citizen science? Exploring four recording schemes with different proficiency requirements. DOI

Ronquillo, C. et al. Assessing spatial and temporal biases and gaps in the publicly available distributional information of iberian mosses. PubMed DOI PMC

Petersen, T. K., Speed, J. D. M., Grotan, V. & Austrheim, G. Species data for understanding biodiversity dynamics: The what, where and when of species occurrence data collection. DOI

Pärtel, M., Sabatini, F. M., Morueta-Holme, N., Kreft, H. & Dengler, J. Macroecology of vegetation - Lessons learnt from the Virtual Special Issue. DOI

Rodrigues, A. S. L. et al. A global assessment of amphibian taxonomic effort and expertise. DOI

Meyer, C., Weigelt, P. & Kreft, H. Multidimensional biases, gaps and uncertainties in global plant occurrence information. PubMed DOI

Costa, G. C., Nogueira, C., Machado, R. B. & Colli, G. R. Sampling bias and the use of ecological niche modeling in conservation planning: a field evaluation in a biodiversity hotspot. DOI

Rocchini, D. et al. Accounting for uncertainty when mapping species distributions: The need for maps of ignorance. DOI

Phillips, S. J. et al. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. PubMed DOI

Beck, J., Boller, M., Erhardt, A. & Schwanghart, W. Spatial bias in the GBIF database and its effect on modeling species’ geographic distributions. DOI

Barbosa, A. M., Pautasso, M. & Figueiredo, D. Species-people correlations and the need to account for survey effort in biodiversity analyses. DOI

Chevalier, M., Broennimann, O., Cornuault, J. & Guisan, A. Data integration methods to account for spatial niche truncation effects in regional projections of species distribution. PubMed DOI

Acevedo, P., Jimenez-Valverde, A., Lobo, J. M. & Real, R. Delimiting the geographical background in species distribution modelling. DOI

Jimenez-Valverde, A., Acevedo, P., Barbosa, A. M., Lobo, J. M. & Real, R. Discrimination capacity in species distribution models depends on the representativeness of the environmental domain. DOI

Sillero, N. & Barbosa, A. M. Common mistakes in ecological niche models. DOI

Sobral-Souza, T. et al. Knowledge gaps hamper understanding the relationship between fragmentation and biodiversity loss: the case of Atlantic Forest fruit-feeding butterflies. PubMed DOI PMC

McCune, J. L., Rosner-Katz, H., Bennett, J. R., Schuster, R. & Kharouba, H. M. Do traits of plant species predict the efficacy of species distribution models for finding new occurrences? PubMed DOI PMC

Guo, C. et al. Uncertainty in ensemble modelling of large-scale species distribution: effects from species characteristics and model techniques. DOI

Jimenez-Valverde, A., Lobo, J. M. & Hortal, J. Not as good as they seem: the importance of concepts in species distribution modelling. DOI

Jeliazkov, A. et al. Sampling and modelling rare species: conceptual guidelines for the neglected majority. PubMed DOI

Anderson, R. P. & Raza, A. The effect of the extent of the study region on GIS models of species geographic distributions and estimates of niche evolution: preliminary tests with montane rodents (genus Nephelomys) in Venezuela. DOI

Hattab, T. et al. A unified framework to model the potential and realized distributions of invasive species within the invaded range. DOI

Lembrechts, J. J., Lenoir, J., Scheffers, B. & De Frenne, P. Designing countrywide and regional microclimate networks. DOI

Fourcade, Y., Engler, J. O., Rodder, D. & Secondi, J. Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias. PubMed DOI PMC

Nunez-Penichet, C. et al. Selection of sampling sites for biodiversity inventory: Effects of environmental and geographical considerations. DOI

Whittaker, R. H. A criticism of the plant association and climatic climax concepts.

Austin, M. P., Cunningham, R. B. & Fleming, P. M. New approaches to direct gradient analysis using environmental scalars and statistical curve-fitting procedures. DOI

Aiello-Lammens, M. E., Boria, R. A., Radosavljevic, A., Vilela, B. & Anderson, R. P. spthin: an R package for spatial thinning of species occurrence records for use in ecological niche models. DOI

Fourcade, Y. Fine-tuning niche models matters in invasion ecology.A lesson from the land planarian DOI

Varela, S., Anderson, R. P., Garcia-Valdes, R. & Fernandez-Gonzalez, F. Environmental filters reduce the effects of sampling bias and improve predictions of ecological niche models.

Anderson, R. P. & Gonzalez Jr, I. Species-specific tuning increases robustness to sampling bias in models of species distributions: An implementation with Maxent. DOI

Gabor, L., Moudry, V., Bartak, V. & Lecours, V. How do species and data characteristics affect species distribution models and when to use environmental filtering? DOI

Dormann, C. F. et al. Model averaging in ecology: a review of Bayesian, information-theoretic, and tactical approaches for predictive inference. DOI

Hao, T., Elith, J., Guillera-Arroita, G. & Lahoz-Monfort, J. J. A review of evidence about use and performance of species distribution modelling ensembles like BIOMOD. DOI

Hao, T., Elith, J., Lahoz-Monfort, J. J. & Guillera-Arroita, G. Testing whether ensemble modelling is advantageous for maximising predictive performance of species distribution models. DOI

Amano, T., Lamming, J. D. L. & Sutherland, W.-J. Spatial gaps in global biodiversity information and the role of citizen science. DOI

Wolf, S. et al. Citizen science plant observations encode global trait patterns. PubMed DOI

Theobald, E. J. et al. Global change and local solutions: Tapping the unrealized potential of citizen science for biodiversity research. DOI

Lobo, J. M. et al. KnowBR: An application to map the geographical variation of survey effort and identify well-surveyed areas from biodiversity databases. DOI

Mokany, K., Harwood, T. D., Overton, J. M., Barker, G. M. & Ferrier, S. Combining alpha- and beta-diversity models to fill gaps in our knowledge of biodiversity. PubMed DOI

Chevalier, M., Zarzo-Arias, A., Guélat, J., Mateo, R. G. & Guisan, A. Accounting for niche truncation to improve spatial and temporal predictions of species distributions. DOI

Boggs, S. W. An atlas of ignorance: A needed stimulus to honest thinking and hard work.

Ladle, R. J. & Hortal, J. Mapping species distributions: living with uncertainty. DOI

Konig, C. et al. Biodiversity data integration-the significance of data resolution and domain. PubMed DOI PMC

Ellis-Soto, D., Merow, C., Amatulli, G., Parra, J. L. & Jetz, W. Continental-scale 1 km hummingbird diversity derived from fusing point records with lateral and elevational expert information. DOI

Palmer, M. W. How should one count species?

Cazzolla Gatti, R. et al. The number of tree species on Earth. PubMed DOI PMC

Soberón, J. M., Llorente, J. B. & Oñate, L. The use of specimen-label databases for conservation purposes: an example using Mexican Papilionid and Pierid butterflies. DOI

Bystriakova, N., Peregrym, M., Erkens, R. H. J., Bezsmertna, O. & Schneider, H. Sampling bias in geographic and environmental space and its effect on the predictive power of species distribution models. DOI

Zurell, D. et al. The virtual ecologist approach: simulating data and observers. DOI

Miller, J. A. Virtual species distribution models: Using simulated data to evaluate aspects of model performance. DOI

Moudry, V. Modelling species distributions with simulated virtual species. DOI

Fernandes, R. F., Scherrer, D. & Guisan, A. How much should one sample to accurately predict the distribution of species assemblages? A virtual community approach. DOI

Meynard, C. N., Leroy, B. & Kaplan, D. M. Testing methods in species distribution modelling using virtual species: what have we learnt and what are we missing? DOI

Dullinger, S. et al. Extinction debt of high-mountain plants under twenty-first-century climate change. DOI

Pulliam, H. R. Sources, sinks, and population regulation. DOI

Zurell, D. et al. Benchmarking novel approaches for modelling species range dynamics. PubMed DOI PMC

Brun, P. et al. Model complexity affects species distribution projections under climate change. DOI

Schweiger, A. H., Irl, S. D. H., Steinbauer, M. J., Dengler, J. & Beierkuhnlein, C. Optimizing sampling approaches along ecological gradients. DOI

Meynard, C. N. & Kaplan, D. M. Using virtual species to study species distributions and model performance. DOI

Hirzel, A. H., Helfer, V. & Metral, F. Assessing habitat-suitability models with a virtual species. DOI

Leroy, B., Meynard, C. N., Bellard, C. & Courchamp, F. virtualspecies, an R package to generate virtual species distributions. DOI

Qiao, H. et al. NicheA: creating virtual species and ecological niches in multivariate environmental scenarios. DOI

De Paor, D. G. & Whitmeyer, S. J. Geological and geophysical modeling on virtual globes using KML, COLLADA, and Javascript. DOI

Wallace, A. R. On the law which has regulated the introduction of new species. DOI

Pillar, V. D., Sabatini, F. M., Jandt, U., Camiz, S. & Bruelheide, H. Revealing the functional traits linked to hidden environmental factors in community assembly.

Cazzolla Gatti, R. A century of biodiversity: Some open questions and some answers. DOI

Qiao, H., Peterson, A. T., Ji, L. & Hu, J. Using data from related species to overcome spatial sampling bias and associated limitations in ecological niche modelling. DOI

Winsberg, E. Sanctioning models: The epistemology of simulation. DOI

Winsberg, E. Simulated experiments: Methodology for a virtual world. DOI

Peck, S. L. Simulation as experiment: a philosophical reassessment for biological modeling. PubMed DOI

Rangel, T. F. L., Diniz-Filho, J. A. F. & Colwell, R. K. Species richness and evolutionary niche dynamics: a spatial pattern-oriented simulation experiment. PubMed DOI

Nakazawa, Y. Niche breadth, environmental landscape, and physical barriers: their importance as determinants of species distributions. DOI

Nakazawa, Y. & Peterson, A. T. Effects of climate history and environmental grain on species’ distributions in Africa and South America. DOI

Darroch, S. A. & Saupe, E. E. Reconstructing geographic range-size dynamics from fossil data. DOI

Kadmon, R., Farber, O. & Danin, A. A systematic analysis of factors affecting the performance of climatic envelope models. DOI

Foody, G. M. GIS: stressing the geographical. DOI

Osborne, P. E., Foody, G. M. & Suarez-Seoane, S. Non-stationarity and local approaches to modelling the distributions of wildlife. DOI

Foody, G. M. GIS: biodiversity applications. DOI

Hortal, J., Lobo, J. M. & Jimenez-Valverde, A. Limitations of biodiversity databases: case study on seed-plant diversity in Tenerife (Canary Islands). PubMed DOI

Ficetola, G. F. et al. Sampling bias inverts ecogeographical relationships in island reptiles. DOI

Rocchini, D. et al. Anticipating species distributions: handling sampling effort bias under a Bayesian framework. PubMed DOI

Miller, D. L., Burt, M. L., Rexstad, E. A. & Thomas, L. Spatial models for distance sampling data: recent developments and future directions. DOI

Barry, S. C. & Welsh, A. H. Generalized additive modelling and zero inflated count data. DOI

Schmera, D. & Eros, T. The role of sampling effort, taxonomical resolution and abundance weight in multivariate comparison of stream dwelling caddisfly assemblages collected from riffle and pool habitats. DOI

Lark, R. M. Spatially nested sampling schemes for spatial variance components: scope for their optimization. DOI

Lark, R. M. Exploring scale-dependent correlation of soil properties by nested sampling. DOI

Rousset, F. & Ferdy, J.-B. Testing environmental and genetic effects in the presence of spatial autocorrelation. DOI

Rousset, F., Ferdy, J.-B. and Courtiol, A. spaMM: Mixed-Effect Models, with or without Spatial Random Effects. R package version 3.11.14. (2021). https://CRAN.R-project.org/package=spaMM.

Lozier, J. D., Aniello, P. & Hickerson, M. J. Predicting the distribution of Sasquatch in western North America: anything goes with ecological niche modelling. DOI

Tessarolo, G., Ladle, R., Rangel, T. & Hortal, J. Temporal degradation of data limits biodiversity research. PubMed DOI PMC

Foody, G. M. Impacts of imperfect reference data on the apparent accuracy of species presence-absence models and their predictions. DOI

Beale, C. M. & Lennon, J. J. Incorporating uncertainty in predictive species distribution modelling. PubMed DOI PMC

Sanchez-Fernandez, D., Lobo, J. M., Abellan, P., Ribera, I. & Millan, A. Bias in freshwater biodiversity sampling: the case of Iberian water beetles. DOI

Gomez-Rodriguez, C., Bustamante, J., Díaz-Paniagua, C. & Guisan, A. Integrating detection probabilities in species distribution models of amphibians breeding in Mediterranean temporary ponds. DOI

Ficetola, G. F., Bonardi, A., Sindaco, R. & Padoa-Schioppa, E. Estimating patterns of reptile biodiversity in remote regions. DOI

Barbosa, A. M., Fontaneto, D., Marini, L. & Pautasso, M. Is the human population a large-scale indicator of the species richness of ground beetles? DOI

Fontaneto, D., Barbosa, A. M., Segers, H. & Pautasso, M. The ‘rotiferologist’ effect and other global correlates of species richness in monogonont rotifers. DOI

Real, R., Barbosa, A. M. & Bull, J. W. Species Distributions, quantum theory, and the enhancement of biodiversity measures. PubMed

Wasof, S. et al. Disjunct populations of European vascular plant species keep the same climatic niches. DOI

McCarthy, M. A. & Masters, P. Profiting from prior information in Bayesian analyses of ecological data. DOI

Elith, J., Burgman, M. A. & Regan, H. M. Mapping epistemic uncertainties and vague concepts in predictions of species distribution. DOI

Radosavljevic, A. & Anderson, R. P. Making better Maxent models of species distributions: complexity, overfitting and evaluation. DOI

Rosner-Katz, H., McCune, J. L. & Bennett, J. R. Using stacked SDMs with accuracy and rarity weighting to optimize surveys for rare plant species. DOI

Zurell, D., Elith, J. & Schroder, D. Predicting to new environments: tools for visualizing model behaviour and impacts on mapped distributions. DOI

Brooks, T. M. et al. Coverage provided by the global protected-area system: is it enough? DOI

Dorling, D. Area Cartograms: Their Use and Creation. Concepts and Techniques in Modern Geography (CATMOG) 59 (Univ. of East Anglia, Norwich, U.K.) (1996).

Gastner, M. T. & Newman, M. E. J. Diffusion-based method for producing density-equalizing maps. PubMed DOI PMC