Sampling and modelling rare species: Conceptual guidelines for the neglected majority - Alienor Jeliazkov; Yoni Gavish; Charles J. Marsh; Jonas Geschke; Neil Brummitt; Duccio Rocchini; Peter Haase; William E. Kunin; Klaus Henle (Global Change Biology, 2022)
Biodiversity conservation faces a methodological conundrum: Biodiversity measurement often relies on species, most of which are rare at various scales, especially prone to extinction under global change, but also the most challenging to sample and model. Predicting the distribution change of rare species using conventional species distribution models is challenging because rare species are hardly captured by most survey systems. When enough data are available, predictions are usually spatially biased towards locations where the species is most likely to occur, violating the assumptions of many modelling frameworks. Workflows to predict and eventually map rare species distributions imply important trade‐offs between data quantity, quality, representativeness and model complexity that need to be considered prior to survey and analysis. Our opinion is that study designs need to carefully integrate the different steps, from species sampling to modelling, in accordance with the different types of rarity and available data in order to improve our capacity for sound assessment and prediction of rare species distribution. In this article, we summarize and comment on how different categories of species rarity lead to different types of occurrence and distribution data depending on choices made during the survey process, namely the spatial distribution of samples (where to sample) and the sampling protocol in each selected location (how to sample). We then clarify which species distribution models are suitable depending on the different types of distribution data (how to model). Among others, for most rarity forms, we highlight the insights from systematic species‐targeted sampling coupled with hierarchical models that allow correcting for overdispersion and spatial and sampling sources of bias. Our article provides scientists and practitioners with a much‐needed guide through the ever‐increasing diversity of methodological developments to improve the prediction of rare species distribution depending on rarity type and available data.

Accounting for temporal change in multiple biodiversity patterns improves the inference of metacommunity processes - Laura Melissa Guzman; Patrick L. Thompson; Duarte S. Viana; Bram Vanschoenwinkel; Zsófia Horváth; Robert Ptacnik; Alienor Jeliazkov; Stéphanie Gascón; Pieter Lemmens; Maria Anton‐Pardo; Silke Langenheder; Luc De Meester; et al. (Ecology, 2022)
In metacommunity ecology, a major focus has been on combining observational and analytical approaches to identify the role of critical assembly processes, such as dispersal limitation and environmental filtering, but this work has largely ignored temporal community dynamics. Here, we develop a “virtual ecologist” approach to evaluate assembly processes by simulating metacommunities varying in three main processes: density‐independent responses to abiotic conditions, density‐dependent biotic interactions, and dispersal. We then calculate a number of commonly used summary statistics of community structure in space and time and use random forests to evaluate their utility for inferring the strength of these three processes. We find that (i) both spatial and temporal data are necessary to disentangle metacommunity processes based on the summary statistics we test, and including statistics that are measured through time increases the explanatory power of random forests by up to 59% compared to cases where only spatial variation is considered; (ii) the three studied processes can be distinguished with different descriptors; and (iii) each summary statistic is differently sensitive to temporal and spatial sampling effort. Including repeated observations of metacommunities over time was essential for inferring the metacommunity processes, particularly dispersal. Some of the most useful statistics include the coefficient of variation of species abundances through time and metrics that incorporate variation in the relative abundances (evenness) of species. We conclude that a combination of methods and summary statistics is probably necessary to understand the processes that underlie metacommunity assembly through space and time, but we recognize that these results will be modified when other processes or summary statistics are used.