Browsing by Author "Zhang, Zhixin"
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- Incorporating physiological knowledge into correlative species distribution models minimizes bias introduced by the choice of calibration area.Publication . Zhang, Zhixin; Zhou, Jinxin; García Molinos, Jorge; Mammola, Stefano; Bede-Fazekas, Ákos; Feng, Xiao; Kitazawa, Daisuke; Qiu, Tianlong; Lin, Qiang; Assis, JorgeCorrelative species distribution models (SDMs) are important tools to estimate species' geographic distribution across space and time, but their reliability heavily relies on the availability and quality of occurrence data. Estimations can be biased when occurrences do not fully represent the environmental requirement of a species. We tested to what extent species' physiological knowledge might influence SDM estimations. Focusing on the Japanese sea cucumber within the coastal ocean of East Asia, we compiled a comprehensive dataset of occurrence records. We then explored the importance of incorporating physiological knowledge into SDMs by calibrating two types of correlative SDMs: a naïve model that solely depends on environmental correlates, and a physiologically informed model that further incorporates physiological information as priors. We further tested the models' sensitivity to calibration area choices by fitting them with different buffered areas around known presences. Compared with naïve models, the physiologically informed models successfully captured the negative influence of high temperature on and were less sensitive to the choice of calibration area. The naïve models resulted in more optimistic prediction of the changes of potential distributions under climate change (i.e., larger range expansion and less contraction) than the physiologically informed models. Our findings highlight benefits from incorporating physiological information into correlative SDMs, namely mitigating the uncertainties associated with the choice of calibration area. Given these promising features, we encourage future SDM studies to consider species physiological information where available.
- Intraspecific genetic variation matters when predicting seagrass distribution under climate changePublication . Hu, Zi‐Min; Zhang, Quan‐Sheng; Zhang, Jie; Kass, Jamie M.; Mammola, Stefano; Fresia, Pablo; Draisma, Stefano G. A.; Assis, Jorge; Jueterbock, Alexander; Yokota, Masashi; Zhang, ZhixinSeagrasses play a vital role in structuring coastal marine ecosystems, but their distributional range and genetic diversity have declined rapidly in recent decades. To improve conservation of seagrass species, it is important to predict how climate change may impact their ranges. Such predictions are typically made with correlative species distribution models (SDMs), which can estimate a species' potential distribution under present and future climatic scenarios given species' presence data and climatic predictor variables. However, these models are typically constructed with species-level data, and thus ignore intraspecific genetic variability, which can give rise to populations with adaptations to heterogeneous climatic conditions. Here, we explore the link between intraspecific adaptation and niche differentiation in Thalassia hemprichii, a seagrass broadly distributed in the tropical Indo-Pacific Ocean and a crucial provider of habitat for numerous marine species. By retrieving and re-analysing microsatellite data from previous studies, we delimited two distinct phylogeographical lineages within the nominal species and found an intermediate level of differentiation in their multidimensional environmental niches, suggesting the possibility for local adaptation. We then compared projections of the species' habitat suitability under climate change scenarios using species-level and lineage-level SDMs. In the Central Tropical Indo-Pacific region, models for both levels predicted considerable range contraction in the future, but the lineage-level models predicted more severe habitat loss. Importantly, the two modelling approaches predicted opposite patterns of habitat change in the Western Tropical Indo-Pacific region. Our results highlight the necessity of conserving distinct populations and genetic pools to avoid regional extinction due to climate change and have important implications for guiding future management of seagrasses.