Quantifying range‐wide variation in population trends from local abundance surveys and widespread opportunistic occurrence records

Abstract

Summary Species’ abundances vary in space and time. Describing these patterns is a cornerstone of macroecology. Moreover, trends in population size are an important criterion for the assessment of a species’ conservation status. Because abundance trends are not homogeneous in space, we need to quantify variation in abundance trends across the geographical range of a species. A basic difficulty exists in that data sets that cover large geographic areas rarely include population abundance data at high temporal resolution. Whilst both broad‐scale geographic distribution data and site‐specific population trend data are becoming more widely available, approaches are required which integrate these different types of data. We present a hierarchical model that integrates observations from multiple sources to estimate spatio‐temporal abundance trends. The model links annual population densities on a spatial grid to both long‐term count data and to opportunistic occurrence records from a citizen science programme. Specific observation models for both data types explicitly account for differences in data structure and quality. We test this novel method in a virtual study with simulated data and apply it to the estimation of abundance dynamics across the range of a butterfly species (Pyronia tithonus) in Great Britain between 1985 and 2004. The application to simulated and real data demonstrates how the hierarchical model structure accommodates various sources of uncertainty which occur at different stages of the link between observational data and the modelled abundance, thereby it accounts for these uncertainties in the inference of abundance variations. We show that by using hierarchical observation models that integrate different types of commonly available data sources, we can improve the estimates of variation in species abundances across space and time. This will improve our ability to detect regional trends and can also enhance the empirical basis for understanding range dynamics.