The niche through time: Considering phenology and demographic stages in plant distribution models

Species distribution models (SDMs) are widely used to infer species-environment relationships, predict spatial distributions, and characterise species’ environmental niches. While the importance of space and spatial scales is widely acknowledged in SDM applications, temporal components of the niche are rarely addressed. We discuss how phenology and demographic stages affect model inference in plant SDMs. Ignoring conspicuousness and timing of phenological stages may bias niche estimates through increased observer bias, while ignoring stand age may bias niche estimates through temporal mismatches with environmental variables, especially during times of rapid global warming. We present different methods to consider phenology and demographic stages in plant SDMs, including the selection of causal, spatiotemporally explicit predictors, and the calibration of stage-specific SDMs. Based on a case study with citizen science data, we illustrate how spatiotemporal SDMs provide deeper insights on the coincidence of range and phenological shifts under climate change. The proliferation of digitally available biodiversity and citizen science data increasingly allows considering time explicitly in SDMs. This offers a more mechanistic understanding of plant distributions, and more robust predictions under global change, especially if the reporting of phenological stages and age is facilitated and promoted by relevant data portals.

Methods
We conducted a keyword-based search in the Web of Science to quantify how often temporal components related to phenology and demographic stages are explicitly considered in plant SDMs. A full list of keywords is provided in the Supporting Information Table S1. We used a nested set of keywords to identify all studies that mentioned SDMs (or common synonyms), were focused on plants, and were listing relevant keywords related to phenology or to demographic stages, respectively. The search was carried out on 5-Oct-2023 and was restricted to English-language journal articles in the period 1945-2022 (no studies using SDMs were published before that start year). Overall, we found more than 40,000 articles mentioning SDM and over 10,000 articles in our refined search for plant SDMs, with a strong increase in the number of articles over time. Among these, phenology (or related search terms) was mentioned in 970 articles and demographic stages (or related terms) in 1188 articles, each averaging c. 10% of the plant SDM articles. The search categories phenology and demographic stages, thereby, were largely mutually exclusive.

For each search category, phenology and demographic stages, we assessed a random subset of 300 articles to gain more insights about how these temporal components of the niche were considered in SDMs. To do so, we separated all articles published after 1990 in 11-year bins (1990-2000, 2001-2011, 2012-2022) and aimed at assessing 100 papers per bin and category (phenology vs. demographic stages). As fewer than 100 papers were published in the first time period (1990-2000), we drew slightly more articles from the latter two periods. This resulted in sample sizes of n=28, n=123 and n=149 of publications mentioning phenology in the three 11-year bins, respectively, and n=41, n=130 and n=129 mentioning demographic stages. We then scanned the abstracts for information that the articles indeed fitted plant SDMs and explicitly considered phenology or demographic stages. In cases where the abstract was inconclusive, we also scanned the methods. If the papers fulfilled these search criteria, we extracted additional information from abstract and methods regarding the taxa and region studied, and spatial and temporal scale.