Hindcast‐validated species distribution models reveal future vulnerabilities of mangroves and salt marsh species

Abstract

Rapid climate change is threatening biodiversity via habitat loss, range shifts, increases in invasive species, novel species interactions, and other unforeseen changes. Coastal and estuarine species are especially vulnerable to the impacts of climate change due to sea level rise and may be severely impacted in the next several decades. Species distribution modeling can project the potential future distributions of species under scenarios of climate change using bioclimatic data and georeferenced occurrence data. However, models projecting suitable habitat into the future are impossible to ground truth. One solution is to develop species distribution models for the present and project them to periods in the recent past where distributions are known to test model performance before making projections into the future. Here, we develop models using abiotic environmental variables to quantify the current suitable habitat available to eight Neotropical coastal species: four mangrove species and four salt marsh species. Using a novel model validation approach that leverages newly available monthly climatic data from 1960 to 2018, we project these niche models into two time periods in the recent past (i.e., within the past half century) when either mangrove or salt marsh dominance was documented via other data sources. Models were hindcast‐validated and then used to project the suitable habitat of all species at four time periods in the future under a model of climate change. For all future time periods, the projected suitable habitat of mangrove species decreased, and suitable habitat declined more severely in salt marsh species.

Time to Step Up Conservation: Climate Change Will Further Reduce the Suitable Habitats for the Vulnerable Species Marbled Polecat (Vormela peregusna)

“…Species distribution models (SDMs) use environmental variables associated with species distribution sites to predict the ecological needs and potential distribution of species and are widely used in invasion biology, conservation biology, global change biology, and risk of disease transmission [8]. Similar to other ecological models, ecological niche models have uncertainty in their predictions, which are closely related to species distribution sites, environmental variables, and model algorithms and parameters, among which the species distribution sites and model algorithms have the greatest effects [9]. There are more than 30 SDMs available [10], each with different advantages; however, it is difficult to choose the optimal assessment model for species that have rarely been studied [11].…”

Geographic And Taxonomic Occurrence R‐based Scrubbing (gatoRs): An R package and workflow for processing biodiversity data

“…Herbarium data downloaded from these and other aggregators have been used to investigate a diverse array of biological questions (e.g., Wollan et al, 2008 ; Willis et al, 2017 ; Allen et al, 2019 ; reviewed in Heberling et al, 2019 ). Applications in research are broad and include species delimitation (e.g., Zapata and Jiménez, 2012 ; Su et al, 2020 ; de Mestier et al, 2023 ; Wu et al, 2023 ), understanding organismal response to seasonal events (e.g., Pearson et al, 2020 ; Guralnick et al, 2022 ; Belitz et al, 2023 ; Park et al, 2023 ), exploring global patterns of biodiversity (e.g., Gaynor et al, 2020 ; Melton et al, 2022 ; Folk et al, 2023 ), and investigating the potential impact of climate change on species distributions (e.g., Rawal et al, 2015 ; Gaynor et al, 2018 ; Hodel et al, 2022 ; Naranjo et al, 2022 ; Wang et al, 2022 ).…”