MaxEnt’s parameter configuration and small samples: are we paying attention to recommendations? A systematic review

Environmental niche modeling (ENM) is commonly used to develop probabilistic maps of species distribution. Among available ENM techniques, MaxEnt has become one of the most popular tools for modeling species distribution, with hundreds of peer-reviewed articles published each year. MaxEnt’s popularity is mainly due to the use of a graphical interface and automatic parameter configuration capabilities. However, recent studies have shown that using the default automatic configuration may not be always appropriate because it can produce non-optimal models; particularly when dealing with a small number of species presence points. Thus, the recommendation is to evaluate the best potential combination of parameters (feature classes and regularization multiplier) to select the most appropriate model. In this work we reviewed 244 articles published between 2013 and 2015 to assess whether researchers are following recommendations to avoid using the default parameter configuration when dealing with small sample sizes, or if they are using MaxEnt as a “black box tool.” Our results show that in only 16% of analyzed articles authors evaluated best feature classes, in 6.9% evaluated best regularization multipliers, and in a meager 3.7% evaluated simultaneously both parameters before producing the definitive distribution model. We analyzed 20 articles to quantify the potential differences in resulting outputs when using software default parameters instead of the alternative best model. Results from our analysis reveal important differences between the use of default parameters and the best model approach, especially in the total area identified as suitable for the assessed species and the specific areas that are identified as suitable by both modelling approaches. These results are worrying, because publications are potentially reporting over-complex or over-simplistic models that can undermine the applicability of their results. Of particular importance are studies used to inform policy making. Therefore, researchers, practitioners, reviewers and editors need to be very judicious when dealing with MaxEnt, particularly when the modelling process is based on small sample sizes.

[1]  Robert P. Anderson,et al.  Species-specific tuning increases robustness to sampling bias in models of species distributions: An implementation with Maxent , 2011 .

[2]  A. Stensgaard,et al.  Modelling spatial distribution of snails transmitting parasitic worms with importance to human and animal health and analysis of distributional changes in relation to climate. , 2014, Geospatial health.

[3]  Sabrina Mazzoni,et al.  Opportunities for improved distribution modelling practice via a strict maximum likelihood interpretation of MaxEnt , 2015 .

[4]  M. Alamgir,et al.  Modelling spatial distribution of critically endangered Asian elephant and Hoolock gibbon in Bangladesh forest ecosystems under a changing climate , 2015 .

[5]  D. Rödder,et al.  Habitat characterization and potential distribution of Tylototriton vietnamensis in northern Vietnam , 2013 .

[6]  CasacubertaFrancisco,et al.  Maximum Entropy Modeling , 2005 .

[7]  Matthew J. Smith,et al.  Protected areas network is not adequate to protect a critically endangered East Africa Chelonian: Modelling distribution of pancake tortoise, Malacochersus tornieri under current and future climates , 2013, bioRxiv.

[8]  Steven J. Phillips,et al.  The art of modelling range‐shifting species , 2010 .

[9]  Leon C. Hinz,et al.  Using Maxent to model the historic distributions of stonefly species in Illinois streams: The effects of regularization and threshold selections , 2013 .

[10]  R. Crafton.  Modeling invasion risk for coastal marine species utilizing environmental and transport vector data , 2014, Hydrobiologia.

[11]  Michael C. Orr,et al.  Taxonomic utility of niche models in validating species concepts: A case study in Anthophora (Heliophila) (Hymenoptera: Apidae). , 2014, Zootaxa.

[12]  Narkis S. Morales,et al.  A spatial multicriteria decision analysis for selecting priority sites for plant species restoration: a case study from the Chilean biodiversity hotspot , 2016 .

[13]  Nag Chetan,et al.  Delineating Ecological Boundaries of Hanuman Langur Species Complex in Peninsular India Using MaxEnt Modeling Approach , 2014, PLoS ONE.

[14]  Stephan B. Munch,et al.  Using measurement error models to account for georeferencing error in species distribution models , 2016 .

[15]  A. Ellison,et al.  MaxEnt versus MaxLike: empirical comparisons with ant species distributions , 2013 .

[16]  Miroslav Dudík,et al.  Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation , 2008 .

[17]  Jessica J. Meeuwig,et al.  Drifting baited stereo‐videography: a novel sampling tool for surveying pelagic wildlife in offshore marine reserves , 2015 .

[18]  R. Murphy,et al.  The speciation continuum: ecological and chromosomal divergence in the Simulium arcticum complex (Diptera: Simuliidae) , 2015 .

[19]  J. Andrew Royle,et al.  Presence‐only modelling using MAXENT: when can we trust the inferences? , 2013 .

[20]  Diversidad y distribución de Marcgraviaceae en México , 2013 .

[21]  Robert P. Anderson,et al.  Estimating optimal complexity for ecological niche models: A jackknife approach for species with small sample sizes , 2013 .

[22]  J. Elith,et al.  Species Distribution Models: Ecological Explanation and Prediction Across Space and Time , 2009 .

[23]  Antoine Guisan,et al.  Predictive habitat distribution models in ecology , 2000 .

[24]  Matthew J. Smith,et al.  The Effects of Sampling Bias and Model Complexity on the Predictive Performance of MaxEnt Species Distribution Models , 2013, PloS one.

[25]  Robert P. Anderson,et al.  Maximum entropy modeling of species geographic distributions , 2006 .

[26]  R. Machado,et al.  Effect of chronological addition of records to species distribution maps: The case of Tonatia saurophila maresi (Chiroptera, Phyllostomidae) in South America , 2015 .

[27]  Incorporating bioclimatic and biogeographic data in the construction of species distribution models in order to prioritize searches for new populations of threatened flora , 2015 .

[28]  Alycia L. Stigall,et al.  Relating environmental change and species stability in Late Ordovician seas , 2014 .

[29]  Hans Visser,et al.  The Map Comparison Kit , 2006, Environ. Model. Softw..

[30]  F. Geri,et al.  Habitat Suitability and Landscape Structure: A Maximum Entropy Approach in a Mediterranean Area , 2015 .

[31]  Jane Elith,et al.  Species Distribution Modeling , 2019, Ecology.

[32]  Patricia Illoldi-Rangel,et al.  Ecological niche modeling under climate change to select shrubs for ecological restoration in Central Mexico , 2015 .

[33]  Current and potential geographical distribution of Platymeris biguttatus (Linnaeus, 1767) with description of nymphs , 2015, Zoological Studies.

[34]  Dan L Warren,et al.  Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. , 2011, Ecological applications : a publication of the Ecological Society of America.

[35]  L. E. Acosta,et al.  A predictive modeling approach to test distributional uniformity of Uruguayan harvestmen (Arachnida: Opiliones) , 2014, Zoological Studies.

[36]  B. Stuart,et al.  Predicting Environmental Suitability for a Rare and Threatened Species (Lao Newt, Laotriton laoensis) Using Validated Species Distribution Models , 2013, PloS one.

[37]  M. Biondi,et al.  Maximum entropy modeling of geographic distributions of the flea beetle species endemic in Italy (Coleoptera: Chrysomelidae: Galerucinae: Alticini) , 2015 .

[38]  Boris Schröder,et al.  The importance of correcting for sampling bias in MaxEnt species distribution models , 2013 .

[39]  M. Pie,et al.  Assessing the exposure of lion tamarins (Leontopithecus spp.) to future climate change , 2014, American journal of primatology.

[40]  Predicting priority areas for conservation from historical climate modelling: stingless bees from Atlantic Forest hotspot as a case study , 2015, Journal of Insect Conservation.

[41]  M. F. Siqueira,et al.  Assessing the conservation status of species with limited available data and disjunct distribution , 2014 .

[42]  A. Townsend Peterson,et al.  Novel methods improve prediction of species' distributions from occurrence data , 2006 .

[43]  K. Weathers,et al.  Cyanobacteria as biological drivers of lake nitrogen and phosphorus cycling , 2015 .

[44]  D. Moher,et al.  Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. , 2010, International journal of surgery.

[45]  J. Rabinovich,et al.  Global Climate Change Effects on Venezuela's Vulnerability to Chagas Disease is Linked to the Geographic Distribution of Five Triatomine Species , 2015, Journal of medical entomology.

[46]  Robert P. Anderson,et al.  Making better Maxent models of species distributions: complexity, overfitting and evaluation , 2014 .

[47]  L. Mboera,et al.  Predicting distribution of Aedes aegypti and Culex pipiens complex, potential vectors of Rift Valley fever virus in relation to disease epidemics in East Africa , 2013, Infection ecology & epidemiology.

[48]  P. Beja,et al.  Combining distribution modelling and non-invasive genetics to improve range shift forecasting , 2015 .

[49]  Chunjing Wang,et al.  A model-based method to evaluate the ability of nature reserves to protect endangered tree species in the context of climate change , 2014 .

[50]  Mauro Fois,et al.  A practical method to speed up the discovery of unknown populations using Species Distribution Models , 2015 .

[51]  More on the Mesopotamian-Yungas disjunction in subtropical and temperate Argentina: Bioclimatic distribution models of the harvestman Discocyrtus dilatatus (Opiliones: Gonyleptidae) , 2015 .

[52]  Junhua Hu,et al.  Unveiling the Conservation Biogeography of a Data-Deficient Endangered Bird Species under Climate Change , 2014, PloS one.

[53]  E. Bassi,et al.  Modelling distribution and potential overlap between Boreal Owl Aegolius funereus and Black Woodpecker Dryocopus martius: implications for management and monitoring plans , 2013, Bird Conservation International.