Reliability of presence-only data for assessing plant community responses to climate warming

Abstract

Climate warming has triggered shifts in plant distributions, resulting in changes within communities, characterized by an increase in warm-demanding species and a decrease in cold-adapted species – referred to as thermophilization. Researchers conventionally rely on co-occurrence data from vegetation assemblages to examine these community dynamics. Despite the increasing availability of presence-only data in recent decades, their potential has largely remained unexplored due to concerns about their reliability. Our study aimed to determine whether climate-induced changes in community dynamics, as inferred from presence-only data from the Global Biodiversity Information Facility (GBIF), corresponded with those derived from co-occurrence plot data. To assess the differences between these datasets, we computed a community temperature index (CTI) using a transfer function, weighted-averaging partial least squares regression (WA-PLS). We calibrated the transfect function model based on the species–temperature relationship using data before recent climate warming. Then we assessed the differences in CTI and examined the temporal trend in thermophilization. In a preliminary analysis, we assessed the performance of this calibration using three datasets: 1) Norwegian co-occurrence data, 2) presence-only data from a broader European region organized into pseudo-plots (potentially capturing a larger part of the species niches), and 3) a combined dataset merging 1) and 2). The transfer function including the combined dataset performed best. Subsequently, we compared the CTI for the co-occurrence plots paired up spatially and temporally with presence-only pseudo-plots. The results demonstrated that presence-only data can effectively evaluate species assemblage responses to climate warming, with consistent CTI and thermophilization values to what was found for the co-occurrence data. Employing presence-only data for evaluating community responses opens up better spatial and temporal resolution and much more detailed analyses of such responses. Our results therefore outline how a large amount of presence-only data can be used to enhance our understanding of community dynamics in a warmer world.