Past climate cooling promoted global dispersal of amphipods from Tian Shan montane lakes to circumboreal lakes

Abstract

Climate changes have substantial impacts on the geographic distribution of montane lakes and evolutionary dynamics of cold‐adapted species. Past climate cooling is hypothesized to have promoted the dispersal of cold‐adapted species via montane lakes, while future climate warming is thought to constrain their distributions. We test this hypothesis by using phylogeographic analysis and niche modeling of the Holarctic crustacean Gammarus lacustris with global sampling comprised of 567 sequenced individuals and 3180 occurrence records. We found that the species arose in Tian Shan in Central Asia and dispersed into montane lakes along the Alps, Himalayas, Tibet, East Asia, and the North American Rocky Mountain ranges, with accelerated diversification rates outside Tian Shan. Climatically suitable regions for geographic lineages of G. lacustris were larger during cooling periods (LGM), but smaller during warming periods (Mid‐Holocene). In the future (2070) scenario, potential distributions in the Himalayas, North Tibet, South Tibet and North America are predicted to expand, whereas ranges in East Asia, Europe and Tian Shan will decline. Our results suggest that Mid‐Miocene‐to‐Pleistocene continuous cooling promoted multiple independent dispersal events out of Tian Shan due to increased availability of montane lakes via “budding” of lineages. Montane lakes are conduits through which cold‐adapted amphipods globally dispersed, dominating circumboreal lakes. However, future climate warming is likely to force organisms to shift upward in altitude and northward in latitude, leading to a future change in local populations. These findings highlight the importance of conservation of montane lakes, especially in the context of climate change.

Diversity of endemic cold‐water amphipods threatened by climate warming in northwestern China

“…Results of PD and sesPD analyses indicate that areas of high PD occur mainly near the Tian Shan mountains (Figure 3a,b), suggesting the existence of some ancient lineages in this area. Consistent with our results in patterns of PD, the G. lacustris species complex has spread and colonized areas surrounding the Tian Shan mountains, having been influenced by tectonic events and the Paratethys Sea (Hou et al, 2022).…”

“…org). In total, seven bioclimatic variables were obtained (Table S4) based on previous studies (Copilaș-Ciocianu et al, 2019;Hou et al, 2022;Vereshchagina et al, 2016).…”

“…Climate warming is dramatically altering freshwater ecosystems, and displacing species to higher latitudes or altitudes (Hou et al., 2022; Lindholm et al., 2012). Anthropogenic disturbance is accelerating the increase in average air temperatures, which increase water temperatures globally (Wanders et al., 2019).…”

Tracing the future of epidemics: Coincident niche distribution of host animals and disease incidence revealed climate‐correlated risk shifts of main zoonotic diseases in China

“…In this study, we adopted the “ Spatially Rarefy Occurrence Data Tool ” in the SDM toolbox to eliminate spatial clusters and environmental biases of host occurrence records and disease incidence records. By reducing occurrence localities to a single point within user‐specified Euclidian distance, this tool has been widely used in a series of distribution prediction studies (Cao et al, 2016; Hou et al, 2022; Martinez‐Lopez et al, 2021; Zahoor et al, 2021). The detailed elimination process is as below: (1) prepare the occurrence data in .shp format (named “occurrence.shp”) with species/disease identity, latitude and longitude; (2) import the “occurrence.shp” file in SDM toolbox in ArcGIS and check the “multi‐distance data rarefying” step; and (3) set the parameters of “multi‐distance data rarefying.” Here we use the following conditions: Input heterogeneity raster: Altitude_hetero_China.tif; Number of heterogeneity classes: 5; Classification type: natural_breaks; Maximum distance: 25 km; Minimum distance: 2 km.…”

“…In this study, we adopted the "Spatially Rarefy Occurrence Data Tool" in the SDM toolbox to eliminate spatial clusters and environmental biases of host occurrence records and disease incidence records. By reducing occurrence localities to a single point within user-specified Euclidian distance, this tool has been widely used in a series of distribution prediction studies (Cao et al, 2016;Hou et al, 2022;Martinez-Lopez et al, 2021;Zahoor et al, 2021). The detailed elimination process is as below: (1) prepare the occurrence data in .shp format (named "occurrence.shp") with species/disease identity, latitude and longitude; The eliminated datasets of occurrence records were then visualized and verified in ArcGIS to ensure accuracy.…”