Predicting species distributions and community composition using satellite remote sensing predictors

Abstract

Biodiversity is rapidly changing due to changes in the climate and human related activities; thus, the accurate predictions of species composition and diversity are critical to developing conservation actions and management strategies. In this paper, using satellite remote sensing products as covariates, we constructed stacked species distribution models (S-SDMs) under a Bayesian framework to build next-generation biodiversity models. Model performance of these models was assessed using oak assemblages distributed across the continental United States obtained from the National Ecological Observatory Network (NEON). This study represents an attempt to evaluate the integrated predictions of biodiversity models—including assemblage diversity and composition—obtained by stacking next-generation SDMs. We found that applying constraints to assemblage predictions, such as using the probability ranking rule, does not improve biodiversity prediction models. Furthermore, we found that independent of the stacking procedure (bS-SDM versus pS-SDM versus cS-SDM), these kinds of next-generation biodiversity models do not accurately recover the observed species composition at the plot level or ecological-community scales (NEON plots are 400 m2). However, these models do return reasonable predictions at macroecological scales, i.e., moderately to highly correct assignments of species identities at the scale of NEON sites (mean area ~ 27 km2). Our results provide insights for advancing the accuracy of prediction of assemblage diversity and composition at different spatial scales globally. An important task for future studies is to evaluate the reliability of combining S-SDMs with direct detection of species using image spectroscopy to build a new generation of biodiversity models that accurately predict and monitor ecological assemblages through time and space.

Estimating the Potential Impacts of Climate Change on the Spatial Distribution of Garuga forrestii, an Endemic Species in China

“…This study shows that ENMs are reliable tools to investigate and understand the factors influencing the potential distribution of species across levels [57,58]. The precise predictions of species diversity and composition are necessary for developing strategic management policies and conservation actions to avert biodiversity loss and related crises [59]. Over the last decade, many predictions have been made using this system for many organisms to assess the impact of climate change on biodiversity [60,61].…”

Leaf habit affects the distribution of drought sensitivity but not water transport efficiency in the tropics

“…To characterise the atmospheric water demand, we used gridded potential evapotranspiration data at ~1 km (0.01°) resolution from the Global Aridity and Potential Evapotranspiration (PET) database ( http://www.cgiar‐csi.org ) (Zomer et al, 2008 ). We acknowledge different resolutions in the climatic products used, however, evidence suggests that analyses using resolutions with a grid cell size between 5 and 1 km produce almost identical results (Pinto‐Ledezma & Cavender‐Bares, 2021 ). We calculated the aridity index (AI) as the ratio between MAP and PET.…”

Low species turnover of upland Amazonian birds in the absence of physical barriers

“…Identifying exactly which factors are at play is a major challenge in ecology and includes evaluation of abiotic characteristics, environmental integrity, interactions among species and the dispersal propensity of organisms. Recognizing distributional patterns and attributing underlying causes is useful because it allows us to predict not only the effects of future environmental change but also which species should occur in areas that have never been inventoried (Ferrier & Guisan, 2006; Guisan & Rahbek, 2011; Pinto‐Ledezma & Cavender‐Bares, 2021). Thus, biodiversity inventories are the building blocks of a great deal of ecological and conservation science.…”