Species distribution modelling supports the study of past, present and future biogeographies

Abstract

Species distribution modelling (SDM), also called environmental or ecological niche modelling, has developed over the last 30 years as a widely used tool used in core areas of biogeography including historical biogeography, studies of diversity patterns, studies of species ranges, ecoregional classification, conservation assessment and projecting future global change impacts. In the 50th anniversary year of Journal of Biogeography, I reflect on developments in species distribution modelling, illustrate how embedded the methodology has become in all areas of biogeography and speculate on future directions in the field. Challenges to species distribution modelling raised in this journal in 2006 have been addressed to a significant degree. Those challenges are clarification of the niche concept; improved sample design for species occurrence data; model parameterization; predictor selection; assessing model performance and transferability; and integrating correlative and process models of species distributions. SDM is used, often in conjunction with other evidence, to understand past species range dynamics, identify patterns and drivers of biological diversity, identify drivers of species range limits, define and delineate ecoregions, estimate the distributions of biodiversity elements in relation to protected status and to prioritize conservation action, and to forecast species range shifts in response to climate change and other global change scenarios. Areas of progress in SDM that may become more widely accessible and useful tools in biogeography include genetically informed models and community distribution models.

Top ten hazards to avoid when modeling species distributions: a didactic guide of assumptions, problems, and recommendations

“…1) (Franklin 2010, Peterson et al 2011, Guisan et al 2017). Substantial literature exists regarding relationships between species ecological niches and geographic distributions, as well as correlative and mechanistic approaches to estimating them (Soberón 2007, Enriquez‐Urzelai et al 2019, Kearney and Porter 2020, Franklin 2023). Studies using these techniques generally aim to estimate environmental suitability for a species, mapping it onto geography to characterize spatial patterns (Fig.…”

“…To varying degrees, these issues have been addressed elsewhere, although often independently (Araújo and Peterson 2012, Jiménez‐Valverde et al 2013, Cooper and Soberón 2018, Yates et al 2018, Cobos et al 2019a, Qiao et al 2019, Warren et al 2020). Fortunately, some synthetic books (Franklin 2010, Peterson et al 2011, Guisan et al 2017), shorter reviews (Elith et al 2011, Anderson 2012, Merow et al 2014, Jarnevich et al 2015, Beery et al 2021, Sillero et al 2021, Franklin 2023), and proposed standards for modeling and reporting exist (Araújo et al 2019, Feng et al 2019a, Sofaer et al 2019, Zurell et al 2020b, Fitzpatrick et al 2021). Nevertheless, the book‐level syntheses are long and include mathematical and statistical formalizations, making it difficult for some readers to understand key information and deterring others from attempting.…”

“…Recommendations. Completely eliminating the false signal resulting from sampling bias remains challenging; however, several approaches may reduce its effects considerably (Franklin 2023). One theoretically sound approach corrects for sampling bias by contrasting the focal species' occurrences against a sample of the environment characterized by the same bias, via occurrence records of other species detected with the same techniques (i.e.…”

“…Several analytical approaches now exist to provide a clearer understanding and documentation of extrapolation. Together, they allow for flagging areas with high uncertainty due to extrapolation, which can help researchers identify regions where drawing any inference from the suitability values may be unadvisable (Owens et al 2013, Franklin 2023). 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).…”

Impacts of physiological characteristics and human activities on the species distribution models of orchids taking the Hengduan Mountains as a case

“…However, there is no exact method to quantify or assess the impact of human activities on orchid distribution in current research. This shortcoming may result in biases between the predicted potential habitat generated by the lack of anthropogenic dispersal constraints in predicting species distributions and the potential geographic distributions (Franklin, 2023). In orchid distribution pattern studies in Central America, model results show that most orchid hotspots occur in the most densely populated provinces (Crain & Fernandez, 2020).…”