Increasing temperature threatens an already endangered coastal dune plant

Abstract

Climate change has the potential to reduce the abundance and distribution of species and threaten global biodiversity, but it is typically not listed as a threat in classifying species conservation status. This likely occurs because demonstrating climate change as a threat requires data-intensive demographic information. Moreover, the threat from climate change is often studied in specific biomes, such as polar or arid ones. Other biomes, such as coastal ones, have received little attention, despite being currently exposed to substantial climate change effects. We forecast the effect of climate change on the demography and population size of a federally endangered coastal dune plant (Lupinus tidestromii). We use data from a 14-yr demographic study across seven extant populations of this endangered plant. Using model selection, we found that survival and fertility measures responded negatively to temperature anomalies. We then produced forecasts based on stochastic individual-based population models that account for uncertainty in demographic outcomes. Despite large uncertainties, we predict that all populations will decline if temperatures increase by 1°C. Considering the total number of individuals across all seven populations, the most likely outcome is a population decline of 90%. Moreover, we predict extinction is certain for one of our seven populations. These results demonstrate that climate change will profoundly decrease the current and future population growth rates of this plant, and its chance of persistence. Thus, our study provides the first evidence that climate change is an extinction threat for a plant species classified as endangered under the USA Endangered Species Act.

The inclusion of immediate and lagged climate responses amplifies the effect of climate autocorrelation on long‐term growth rate of populations

“…Furthermore, our results are relevant to conservation research aimed at understanding and accurately forecasting the dynamics of populations known to be threatened by climate change (e.g. Compagnoni et al, 2021;Lindell et al, 2022). In the case of species particularly sensitive to climatic variation, explicit modelling of TVR could substantially change forecasts by correctly accounting for the indirect effects of climatic autocorrelation.…”

“…Climate drivers are usually thought to cause responses in vital rates that are similar in direction (e.g. Compagnoni et al, 2021; Hindle et al, 2019). For example, drought typically harms multiple vital rates rather than harm some and benefit others.…”

Spatial replication can best advance our understanding of population responses to climate

“…on biophysical conditions, Elmendorf et al 2012). Instead, short-term climatic effects are directly relevant to inform the management and conservation of populations during the upcoming century of rapid climate change (Compagnoni et al 2021a, b, Hunter et al 2010. Here, we therefore advocate to replicate sampling across populations that occur in similar climates, and to use this spatial data as replicates of the same temporal process.…”

Rpadrino: An R package to access and use PADRINO, an open access database of Integral Projection Models

“…Erickson et al, 2017;Jongejans et al, 2011), evolutionary stable strategies (e.g. Childs et al, 2004), the effect of climate drivers on population persistence (Compagnoni, Pardini, & Knight, 2021;Salguero-Gómez et al, 2012), and linking evolutionary feedbacks to population dynamics (Coulson et al, 2011). In general, this data format limits the variety of potential analyses, because individual matrix elements may be composed of multiple vital rates and this information is lost by storing only the resulting values (i.e.…”