Climate change and conservation in a warm North American desert: effect in shrubby plants

Abstract

Background Deserts are biologically rich habitats with a vast array of animals and plants adapted to xeric conditions, and most deserts are among the planet’s last remaining areas of total wilderness. Among North American deserts, the Chihuahuan Desert has the highest levels of diversity and endemism. To understand the effect of future climate change on plants distributed in this arid land and propose effective conservation planning, we focused on five endemic shrubby species that characterize the Chihuahuan Desert and used an integrative approach. Methods Ecological niche-based modeling, spatial genetics and ecological resistance analyses were carried out to identify the effect of global warming on the studied five shrubby species. Key areas that need to be preserved were identified taking into account the existing protected areas within the Chihuahuan Desert. Results The extent of future distribution will vary among these species, and on average expansion will occur in the western part of the Chihuahuan Desert. For most species low environmental resistance to gene flow was predicted, while higher future resistance was predicted for one species that would lead to increased population isolation. The highest haplotype diversity was identified in three hotspots. Based on future suitability of habitat and in the haplotype diversity we suggest preserving two hotspots of genetic diversity in the Sierra Madre Oriental, located in areas without protection. The third hotspot was detected in the well preserved Tehuacán-Cuicatlán Man and Biosphere Reserve. Conclusion Global climate change will have an effect in arid adapted plants, favoring expansion in the western of the Chihuahuan Desert however negatively affecting others with high ecological resistance disrupting gene flow. Two hotspots of genetic diversity in the Sierra Madre Oriental should be protected.

Predicting Habitat Suitability and Conserving Juniperus spp. Habitat Using SVM and Maximum Entropy Machine Learning Techniques

“…Habitat and biodiversity loss are global concerns related to climate change-especially drought-and serve as an enormous warnings for the future [1][2][3]. Based on a continuous rate of global warming, a temperature increase of~4 • C is anticipated in tropical zones and a mean global temperature increase of~2.5 • C is anticipated by 2100 AD [4,5], which, in turn, changes the habitat of species.…”

Potential Effects of Climate Change on the Geographic Distribution of the Endangered Plant Species Manihot walkerae

“…As a consensus, the future climate change models show a restriction in future distribution for Manihot walkerae with the lowest loss of distribution calculated as −2.08% for the year 2050 with an RCP of 8.5, and the highest, −14.37%, for the year 2070 with an RCP of 4.5, whereas for two of the future climate change scenarios at an RCP of 4.5 for the year 2050, it is predicted that there could be a potential increase of approximately 7% in distribution (Table 2). Similarly, another SDM study conducted in the Chihuahan desert found that some endemic plants were shown to be affected by climate change and expanded in distribution [48]. The areas that were shown to be most affected by climate change were those in the northeastern and southernmost portions of the Tamaulipan Thornscrub ecoregion (Figures 3-5).…”

“…Some limitations of our study are that we relied solely on bioclimatic variables for our modeling and that we used a small number of occurrences to create our models. Using bioclimatic variables for climate change modeling is common and has been used to model the effects of climate change on the distribution of different species of plants and animals, some of which are endangered and restricted [23,48,49]. Including static topographic variables could have improved the reliability of our models, but in some instances, such as when topographic variables like elevation and bioclimatic variables are highly correlated, they could hinder the statistic reliability of the model [50].…”