Genomic vulnerability and socio-economic threats under climate change in an African rainforest bird

Abstract

Preserving biodiversity under rapidly changing climate conditions is challenging. One approach for estimating impacts and their magnitude is to model current relationships between genomic and environmental data and then to forecast those relationships under future climate scenarios. In this way, understanding future genomic and environmental relationships can help guide management decisions, such as where to establish new protected areas where populations might be buffered from high temperatures or major changes in rainfall. However, climate warming is only one of many anthropogenic threats one must consider in rapidly developing parts of the world. In Central Africa, deforestation, mining, and infrastructure development are accelerating population declines of rainforest species. Here we investigate multiple anthropogenic threats in a Central African rainforest songbird, the little greenbul (Andropadus virens). We examine current climate and genomic variation in order to explore the association between genome and environment under future climate conditions. Specifically, we estimate Genomic Vulnerability, defined as the mismatch between current and predicted future genomic variation based on genotype-environment relationships modeled across contemporary populations. We do so while considering other anthropogenic impacts. We find that coastal and central Cameroon populations will require the greatest shifts in adaptive genomic variation, because both climate and land use in these areas are predicted to change dramatically. In contrast, in the more northern forest-savanna ecotones, genomic shifts required to keep pace with climate will be more moderate, and other anthropogenic impacts are expected to be comparatively low in magnitude. While an analysis of diverse taxa will be necessary for making comprehensive conservation decisions, the species-specific results presented illustrate how evolutionary genomics and other anthropogenic threats may be mapped and used to inform mitigation efforts. To this end, we present an integrated conceptual model demonstrating how the approach for a single species can be expanded to many taxonomically diverse species.

Keywords: Central Africa; climate change; conservation biology; evolutionary genomics; genomic vulnerability.

FIGURE 1

Genomic variation and turnover of the little greenbul across its range (a) with sample locations in Cameroon indicated and forest cover represented as background. Variation in the genome of a species can be visualized by color (b), where greater differences in colors represent greater adaptive genomic variation between populations across environments ((a) and (b) modified from Zhen et al., 2017). These differences are further quantified and represented in (c), with higher (red) or lower (blue) adaptive turnover across regions. High turnover areas vary in their correspondence to species richness (green polygons) or endemism (blue polygons) (see text for details). Current protected areas are represented by black‐outlined polygons in (b) and (c)

FIGURE 2

Patterns of genomic diversity and genomic vulnerability under current and future climate. (a) The relationship between current genomic and environmental variation of a species (same as Figure 1b). (b) The predicted genomic and environmental variation under future climate conditions (RCP 4.5 2080 scenario). (c) The absolute difference between (a) and (b) is the estimated genomic vulnerability under climate change. High vulnerability areas, shown in red, are where population genomes must change rapidly, and low vulnerability areas, shown in green, are where populations will need to change less to keep pace with climate change. Socio‐economic threats, indicated in black (includes logging and mining, see Figures S3–S5, S8, and S9 for additional threats) will limit conservation efforts in those areas. Ecotone regions, at the center part of the country, show relatively fewer current threats and low genomic vulnerability under climate change. In contrast, coastal and some southern regions show the highest genomic vulnerability and high socio‐economic threats. Current protected areas in the country are represented by black‐outlined polygons

FIGURE 3

Framework to map current and potential future biodiversity across a landscape. The results, when combined with the degree of threat and socio‐economic impacts, can be used to prioritize areas of importance for conserving evolutionary processes under climate change (modified from Thomassen et al., 2011). Relevant figures are indicated at the bottom to illustrate respective steps that can be taken and combined for multiple species to build a comprehensive management strategy