Current breeding distributions and predicted range shifts under climate change in two subspecies of Black‐tailed Godwits in Asia

Zhu, Bing‐Run; Verhoeven, Mo A.; Velasco, Nicolás; Sanchez-Aguilar, Lisa; Zhang, Zhengwang; Piersma, Theunis

doi:10.1111/gcb.16308

Cited by 20 publications

(16 citation statements)

References 64 publications

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“…Up to date, the ENM approach (especially the Maxent modeling) has been successfully used in the potential distribution of plants and animals (Cao et al, 2022;Cao, Bai, Wu, et al, 2020;Ma et al, 2022;Puchałka et al, 2021;Zhu et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

“…The habitat suitability (P s ) of each host animal was determined based on the distribution possibility generated by Maxent modeling. (Cao et al, 2016;Cenni et al, 2023;Gomes et al, 2019;Ma et al, 2022;Zhu et al, 2022). For each zoonotic disease, the layer of comprehensive suitability (P scom ) was calculated as average suitability layers of all the host animals.…”

Section: Classification Of Host Habitat Suitabilitymentioning

confidence: 99%

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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

Cao

Bai

et al. 2023

Global Change Biology

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Climate has critical roles in the origin, pathogenesis and transmission of infectious zoonotic diseases. However, large‐scale epidemiologic trend and specific response pattern of zoonotic diseases under future climate scenarios are poorly understood. Here, we projected the distribution shifts of transmission risks of main zoonotic diseases under climate change in China. First, we shaped the global habitat distribution of main host animals for three representative zoonotic diseases (2, 6, and 12 hosts for dengue, hemorrhagic fever, and plague, respectively) with 253,049 occurrence records using maximum entropy (Maxent) modeling. Meanwhile, we predicted the risk distribution of the above three diseases with 197,098 disease incidence records from 2004 to 2017 in China using an integrated Maxent modeling approach. The comparative analysis showed that there exist highly coincident niche distributions between habitat distribution of hosts and risk distribution of diseases, indicating that the integrated Maxent modeling is accurate and effective for predicting the potential risk of zoonotic diseases. On this basis, we further projected the current and future transmission risks of 11 main zoonotic diseases under four representative concentration pathways (RCPs) (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) in 2050 and 2070 in China using the above integrated Maxent modeling with 1,001,416 disease incidence records. We found that Central China, Southeast China, and South China are concentrated regions with high transmission risks for main zoonotic diseases. More specifically, zoonotic diseases had diverse shift patterns of transmission risks including increase, decrease, and unstable. Further correlation analysis indicated that these patterns of shifts were highly correlated with global warming and precipitation increase. Our results revealed how specific zoonotic diseases respond in a changing climate, thereby calling for effective administration and prevention strategies. Furthermore, these results will shed light on guiding future epidemiologic prediction of emerging infectious diseases under global climate change.

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Section: Discussionmentioning

confidence: 99%

Section: Classification Of Host Habitat Suitabilitymentioning

confidence: 99%

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

Cao

Bai

et al. 2023

Global Change Biology

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“…For this reason, we limited our tracked individuals to birds caught in their wintering range, and not at stopover, breeding or moulting sites (e.g. Zhu et al 2022). Both in 2008-2010 and in 2018-2020, we found individual geese nesting in all four regions known to host breeding barnacle geese: Novaya Zemlya, Vaygach Island, Kolguev Island and the Nenets District mainland.…”

Section: Discussionmentioning

confidence: 99%

Barnacle geese Branta leucopsis breeding on Novaya Zemlya: current distribution and population size estimated from tracking data

et al. 2022

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The Russian breeding population of barnacle geese Branta leucopsis has shown a rapid increase in numbers since 1980, which has coincided with a southwest-wards breeding range expansion within the Russian Arctic. Here barnacle geese also started to occupy coastal and marsh land habitats, in which they were not know to nest on their traditional breeding grounds. While these changes have been well documented by studies and observations throughout the new breeding range of barnacle geese, observations are lacking from the traditional breeding grounds on Novaya Zemlya, as this area is remote and difficult to access. This is especially relevant given rapid climate warming in this area, which may impact local distribution and population size. We used GPS-tracking and behavioural biologging data from 46 individual barnacle geese captured on their wintering grounds to locate nest sites in the Russian Arctic and study nesting distribution in 2008–2010 and 2018–2020. Extrapolating from nest counts on Kolguev Island, we estimate the breeding population on Novaya Zemlya in 2018–2020 to range around 75,250 pairs although the confidence interval around this estimate was large. A comparison with the historical size of the barnacle goose population suggests an increase in the breeding population on Novaya Zemlya, corresponding with changes in other areas of the breeding range. Our results show that many barnacle geese on Novaya Zemlya currently nest on lowland tundra on Gusinaya Zemlya Peninsula. This region has been occupied by barnacle geese only since 1990 and appears to be mainly available for nesting in years with early spring. Tracking data are a valuable tool to increase our knowledge of remote locations, but counts of breeding individuals or nests are needed to further corroborate estimates of breeding populations based on tracking data.

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“…Maxent is a machine-learning algorithm that uses presence-only data to determine the predicted suitability of local conditions for a given species ( Phillips et al, 2006 ). Maxent model has been used in previous studies to assess the distribution of species ( Xu et al, 2021 ; Yue et al, 2022 ; Zhu et al, 2022 ). This study aimed to explore the distribution of species screened under different conditions in Inner Mongolia.…”

Section: Methodsmentioning

confidence: 99%

Complex ecological and socioeconomic impacts on medicinal plant diversity

Zhang

Chen²,

Guo³

et al. 2022

Front. Pharmacol.

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Medicinal plant diversity (MPD) is an important component of plant diversity. Over-collection based on medicinal and economic value has the potential to damage the stability of the regional ecosystem. It is important to understand the current distribution of MPD and the factors influencing it. However, it is still unclear whether environmental and socioeconomic conditions have an impact on their distribution. We selected the Inner Mongolia as a representative study area which covers a wide area, accounting for 12.29% of China’s national land area and 0.79% of the world’s land area. At the same time, the region is a long-standing traditional medicinal area for Mongolians in China. Therefore, the region is significantly influenced by changes in environmental factors and socio-economic factors. We used 9-years field survey of the distribution of medicinal plants in Inner Mongolia for assessing the distribution of MPD as influenced by environmental and socioeconomic activities by combining spatial analyses, species distribution models, and generalized additive models. The results from the spatial analysis show that the western region of Inner Mongolia is the main cold spot area of the MPD, and the central-eastern and northeastern regions of Inner Mongolia are the main hot spot areas of the MPD. At the same time, the distribution of cold spots and hot spots of MPD is more obvious at large spatial scales, and with the refinement of spatial scales, the cold spots in scattered areas are gradually revealed, which is indicative for the conservation and development of MPD at different spatial scales. Under the future climate change of shared socioeconomic pathways (SSP), areas with high habitat suitability for medicinal plants remain mainly dominated by the Yellow River, Yin Mountains, and Greater Khingan Range. Notably, the SSP245 development pathway remains the most significant concern in either long- or short-term development. The nonlinear relationship between the driving factors of MPD at different spatial scales shows that temperature, precipitation and socioeconomic development do have complex effects on MPD. The presence of a certain temperature, altitude, and precipitation range has an optimal facilitation effect on MPD, rather than a single facilitation effect. This complex nonlinear correlation provides a reference for further studies on plant diversity and sustainable development and management. In this study, the spatial distribution of medicinal plant resources and the extent to which they are driven by ecological and socioeconomic factors were analyzed through a macroscopic approach. This provides a reference for larger-scale studies on the environmental and socioeconomic influences on the distribution of plant resources.

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Current breeding distributions and predicted range shifts under climate change in two subspecies of Black‐tailed Godwits in Asia

Cited by 20 publications

References 64 publications

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

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

Barnacle geese Branta leucopsis breeding on Novaya Zemlya: current distribution and population size estimated from tracking data

Complex ecological and socioeconomic impacts on medicinal plant diversity

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