An ecophysiological perspective on likely giant panda habitat responses to climate change

Abstract

Threatened and endangered species are more vulnerable to climate change due to small population and specific geographical distribution. Therefore, identifying and incorporating the biological processes underlying a species' adaptation to its environment are important for determining whether they can persist in situ. Correlative models are widely used to predict species' distribution changes, but generally fail to capture the buffering capacity of organisms. Giant pandas (Ailuropoda melanoleuca) live in topographically complex mountains and are known to avoid heat stress. Although many studies have found that climate change will lead to severe habitat loss and threaten previous conservation efforts, the mechanisms underlying panda's responses to climate change have not been explored. Here, we present a case study in Daxiangling Mountains, one of the six Mountain Systems that giant panda distributes. We used a mechanistic model, Niche Mapper, to explore what are likely panda habitat response to climate change taking physiological, behavioral and ecological responses into account, through which we map panda's climatic suitable activity area (SAA) for the first time. We combined SAA with bamboo forest distribution to yield highly suitable habitat (HSH) and seasonal suitable habitat (SSH), and their temporal dynamics under climate change were predicted. In general, SAA in the hottest month (July) would reduce 11.7%-52.2% by 2070, which is more moderate than predicted bamboo habitat loss (45.6%-86.9%). Limited by the availability of bamboo and forest, panda's suitable habitat loss increases, and only 15.5%-68.8% of current HSH would remain in 2070. Our method of mechanistic modeling can help to distinguish whether habitat loss is caused by thermal environmental deterioration or food loss under climate change. Furthermore, mechanistic models can produce robust predictions by incorporating ecophysiological feedbacks and minimizing extrapolation into novel environments. We suggest that a mechanistic approach should be incorporated into distribution predictions and conservation planning.

Infrared thermography cannot be used to approximate core body temperature in wild primates

“…MapperTM: Porter & Mitchell, 2006) that use principles of heat and mass transfer, coupled with information on an animal's morphology, behavior, and microclimate, may also be used to predict an animal's energetic requirements as a function of environmental conditions (e.g., Moyer-Horner, Mathewson, Jones, Kearney, & Porter, 2015;Natori & Porter, 2007;Zhang, Mathewson, Zhang, Porter, & Ran, 2018;Long et al, 2014;Mathewson & Porter, 2013;Briscoe, Kearney, Taylor, & Brendan, 2016;Mathewson et al, 2018), including in vervet monkeys (Mathewson, 2018). Ultimately, an integrative approach to understanding heat transfer and physiological plasticity can complement accurate measures of core body temperature, to provide greater insight into how primates respond to environmental stress.…”

Distribution of a giant panda population influenced by land cover

“…It will also be important to differentiate the scope, timing, and intensity of human activities and develop more effective indices to represent other socio‐economic factors that may influence panda distributions. Other factors to consider will include the mechanistic links between the functional traits of pandas (e.g., behavioral and physiological adaptations; Zhang et al ) and their dynamic environments in species distribution models. Future studies should also incorporate biotic interactions into habitat predictions (e.g., predators (Werner et al ), food resources, distribution) given their effect on species distributions.…”

Modeling Dragons: Using linked mechanistic physiological and microclimate models to explore environmental, physiological, and morphological constraints on the early evolution of dinosaurs

“…The microclimate model has 51 variables relating to seasonality, insolation, shade, wind, air temperature, humidity, cloud, and soil properties. The biophysical model is composed of 270 morphological, physiological, and behavioral parameters previously described in detail and tested over a wide range of animal taxa including reptiles, amphibians, birds, mammals and insects [e.g., 12,13,15,16,23,[30][31][32][33][34][35][36][37][38][39]. The user is able to control how many days (1 to 365) and how those days are distributed throughout the year, and how many of the variables (such as air temperature, feather density, or body mass) vary for each modeled day (see S1 Appendix).…”