Efficacy of the global protected area network is threatened by disappearing climates and potential transboundary range shifts

Abstract

Protected areas are essential to conserving biodiversity, yet changing climatic conditions challenge their efficacy. For example, novel and disappearing climates within the protected area network indicate that extant species may not have suitable climate in protected areas in the future. Further, potential transboundary range shifts, those that involve movement from one country to another, are also challenging because physical (e.g. fencing) and non-physical barriers (e.g. contrasting conservation policies) may impede climate-induced movements. Through the lens of climate analogs, we examined disappearing and novel climates within the global terrestrial protected area network and the potential for transboundary range shifts among protected areas under global warming 2 °C above preindustrial levels. We found that globally, climates in 24% of protected lands will no longer be protected within a 500 km radius of their focal location (indicating disappearing climates within the protected area network), while 36% of protected lands will gain climates not previously protected (indicating novel climates within the protected area network). Further, we found that potential transboundary range shifts are widespread but variable; for example, 23% of protected climates in Europe and >50% of protected climates in Africa under climate change are located in a different country than the focal protected areas. As the global conservation community actively deliberates conservation frameworks (e.g. 30% by 2030), our study offers insights to reduce the prevalence of novel and disappearing climates within the global protected area network via strategic conservation actions and underscores the importance of setting and accommodating targets and strategies that transcend national boundaries.

Protected areas not likely to serve as steppingstones for species undergoing climate‐induced range shifts

“…We followed the methods of Abatzoglou et al (2020) and Parks et al (2022) to characterize climate and identify backward and forward climate analogs. The specific climate variables we used were average minimum temperature of the coldest month ( T min ), average maximum temperature of the warmest month ( T max ), annual actual evapotranspiration (AET), and annual climate water deficit (CWD).…”

“…We identified backward and forward analogs by estimating the climatic dissimilarity between each protected focal pixel (resolution = ~4 km to match gridded climate data) and all protected pixels within a 500‐km radius using a standardized Mahalanobis distance (Mahony et al, 2017). We chose the 500‐km search radius as it encompasses an upper range of dispersal for some terrestrial animals and plants (Chen et al, 2011) when assuming 2°C warming by mid‐21st century; this search radius has also been used in previous studies (Bellard et al, 2014; Parks et al, 2022; Williams et al, 2007). The Mahalanobis distance metric synthesized the four climate variables (i.e., T min , T max , AET, and CWD; Figure 2a) by measuring distance in multivariate space away from a centroid using principal component analysis of standardized anomalies.…”

“…We standardized Mahalanobis distance to account for data dimensionality by calculating a multivariate z ‐score (σ d ) based on a Chi distribution (Mahony et al, 2017). σ d represents the climate similarity between each focal pixel and its candidate backward and forward analogs (i.e., all other protected terrestrial pixels within 500 km), and we considered any protected pixels with σ d ≤ 0.5 as climate analogs (Figure 2b) (following Parks et al, 2022). We were unable to calculate Mahalanobis distance when there was no ICV for any one of the four variables, and as a consequence, these areas are omitted from all analyses; this affects, for example, a relatively small tropical area in South America (CWD = 0 each year) and areas perennially covered by snow (CWD = 0 each year; e.g., most of Greenland).…”

“…For example, consider a montane species whose future optimal climate is located on an adjacent mountain range, yet there is a hot desert between locations that acts as a barrier to movement (Figure 1c). Lastly, the lack of protected lands with suitable future climate conditions may hinder successful species movement (i.e., novel/disappearing climates) (Figure 1d) (Hoffmann et al, 2019; Parks et al, 2022). These constraints on species range shifts—dispersal limitations, human modified landscapes, exposure to dissimilar climates, and novel/disappearing climates—have been well established, yet they have not yet been integrated or simultaneously evaluated at a local, regional, or global level.…”

Improving ecological connectivity assessment : a step forward to consider connectivity concerns in landscape planning

“…Some studies use predefined protected areas as sources and destinations of movements due to their importance for biodiversity conservation and the availability of mechanisms to manage them (Minor and Lookingbill 2010;Saura et al 2017Saura et al , 2018de la Fuente et al 2018a). However, these protected spaces exclude many key current biodiversity areas and are expected to challenge their efficacy as climate change continues increasing (Elsen et al 2020;Dreiss et al 2022;Parks et al 2022). Other connectivity studies assess the movements between actual species occurrences or distribution areas.…”