Species distributions models may predict accurately future distributions but poorly how distributions change: A critical perspective on model validation

Abstract

Aim: Species distribution models (SDMs) are widely used to make predictions on how species distributions may change as a response to climatic change. To assess the reliability of those predictions, they need to be critically validated with respect to what they are used for. While ecologists are typically interested in how and where distributions will change, we argue that SDMs have seldom been evaluated in terms of their capacity to predict such change. Instead, typical retrospective validation methods estimate model's ability to predict to only one static time in future. Here, we apply two validation methods, one that predicts and evaluates a static pattern, while the other measures change and compare their estimates of predictive performance. Location: Fennoscandia. Methods:We applied a joint SDM to model the distributions of 120 bird species in four model validation settings. We trained models with a dataset from 1975 to 1999 and predicted species' future occurrence and abundance in two ways: for one static time period (2013-2016, 'static validation') and for a change between two time periods (difference between 1996-1999 and 2013-2016, 'change validation'). We then measured predictive performance using correlation between predicted and observed values. We also related predictive performance to species traits.

Potential Global Distribution of Paracoccus marginatus, under Climate Change Conditions, Using MaxEnt

“…Species with larger territories may be more vulnerable to landscape changes. Moreover, only localized studies of a type of species may lead to inaccurate predictions [64].…”

Predicting Habitat Suitability and Adaptation Strategies of an Endangered Endemic Species, Camellia luteoflora Li ex Chang (Ericales: Theaceae) under Future Climate Change

“…However, the enhancement of MaxEnt performance within a single time period may not ensure transferability between time periods [34]. To validate the temporal transferability and predictive accuracy of the MaxEnt model, based on the distribution data for the periods 1970 to 2000 ("historical") (Table S2) and 2021 to 2040 ("current"), MaxEnt predicted the current time period (the "future" time period associated with the model fitting data) using the historical data by comparing the predictions with the current data to assess the consistency of the predictions with the current known distribution of C. luteoflora [35].…”

Wind Farms and Power Lines Reduced the Territory Status and Probability of Fledgling Production in the Eurasian Goshawk Accipiter gentilis

“…Global surface temperature has increased very fast in the last 50 years, and strong reductions in CO 2 and other greenhouse gas emissions in the coming decades are needed to reduce global warming [2]. With global warming of 1.5-2 • C, the majority of terrestrial species ranges are projected to shrink dramatically [3], but the precision of many distribution models varies [4,5]. Many species in different taxa have already experienced population declines caused by global warming [6][7][8], including many bird species [9,10].…”