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Fingerprints Of Global Warming On Wild Animals And Plants

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Climate Change Could Cause Abrupt Biodiversity Losses This Century

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By Chaosheng Mu Chaosheng Mu Scilit Preprints.org Google Scholar View Publications 1, † , Xuecheng Guo Xuecheng Guo Scilit Preprints.org Google Scholar View Publications 1, 2, † and Youhua Chen Youhua Chen Scilit Preprints.org Google Scholar View Publications *

CAS Key Laboratory of Mountain Ecological Restoration and Biological Resource Utilization and Ecological Restoration Key Laboratory of Biodiversity Conservation Sichuan Province, Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041, China

Pdf) The Effects Of Climate Change On Global Wildlife And Terrestrial Ecosystems

Submitted: 23 March 2022 / Revised: 8 April 2022 / Accepted: 11 April 2022 / Published: 13 April 2022

Eleutherodactylus planirostris has a strong dispersal potential, and the main route of introduction to new regions is through seed transport. This species is considered as one of the most successful invasive amphibian species with direct or indirect negative impacts in several regions. In our study, we predict the potential distribution of E. planirostris in China by species distribution models (SDMs) methods. The results show that this species has a larger habitat area in China than its current distribution suggests, so it is likely to spread from the Pearl River Delta to surrounding areas. Under future warming, its invasive range will expand northward into China.

Species distribution models (SDMs) have become indispensable tools for risk assessment and conservation decision-making for invasive species. Eleutherodactylus planirostris has a strong dispersal potential, and the main route of introduction to new regions is likely transport through vegetation. This species is considered one of the most successful invasive amphibian species with direct or indirect negative impacts in several regions. In this study, we used MaxEnt to evaluate suitable areas for this species under current and future climates globally and in China. We analyzed seven climate variables, three time points (present, 2050, and 2070), and three CO.

Emission scenarios Annual mean temperature, driest month precipitation, and annual precipitation were the most important variables predicting the presence of E. planirostris. This species has a large habitat area in China as indicated by its current distribution, so it is likely to spread from the Pearl River Delta to the surrounding areas. Under future warming, its invasive range will expand northward into China. Finally, this study assessed the invasion risk of this species and made recommendations for management and prevention.

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As the global climate warms, the introduction and spread of invasive alien species (IAS) will likely increase [ 1 , 2 ]. It is predicted that global warming will alter the geographic range of species [3] and the viability of species in a changing climate will reflect their dispersal potential [4]. Species with a narrow niche and poor dispersal potential, such as many threatened species, are more sensitive to these changes [5] than species with good dispersal potential [6] , such as IAS. Most IAS are well-adapted to local conditions, slowly outgrowing invasive species and eventually driving them to extinction [ 7 ].

Global warming is affecting China’s ecosystem, as it is happening in other parts of the world. [8]. Global climate change has shown a major impact on species distribution [9, 10, 11, 12, 13]. For example, global warming has altered the distribution of butterflies [14], birds [15], amphibians [16], and mammals [17]. The adverse effects of global climate change on ecosystem function have attracted the attention of governments and scientists worldwide [18, 19]. China is extremely rich in species and is listed as one of the 12 countries in the world with great biodiversity, also known as a “mega-diversity country” [ 20 ]. China’s diverse habitats and climatic conditions make it particularly vulnerable to IAS colonization.

The greenhouse frog Eleutherodactylus planirostris [21] originated on islands in the Caribbean Sea region [22] and has now spread to the southeastern United States, some Pacific islands [23, 24], and Hong Kong, China [25]. has attacked The species has a strong dispersal potential, and the main route of introduction is likely transport by plants [ 26 , 27 ]. This species is considered one of the most successful invasive amphibian species [ 28 ], with direct or indirect negative impacts in several regions [ 24 , 29 ]. E. planirostris spread rapidly after invading Hawaii. Current densities have been found to be as high as 12,500 people per hour.

In this study, based on the available records of E. planirostris and high-resolution ecological data on climate variability, we modeled the potential global distribution of E. planirostris. We are particularly curious about the potential invasion dynamics of the species in China. The objectives of this study are (1) to identify key environmental variables that are highly correlated with the current range of E. planirostris and (2) to describe the current potential distribution and model its distribution under future climate change scenarios. Banana species invasion in China.

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We obtained occurrence records of E. planirostris from the Global Biodiversity Information Facility (GBIF, accessed 11 January 2022), and occurrence records were removed if they were outside the environmental data. To reduce spatial autocorrelation, each grid cell was analyzed using the ENMTools R package [ 30 ] to ensure that only one occurrence was recorded. Finally, 1450 occurrence records of E. planirostris were obtained for this study.

A total of 19 global bioclimatic variables were obtained from the World Climate Database. Data for future climate scenarios were accessed from the IPCC’s 5th Assessment Report. The IPCC coordinates the climate research community to develop a set of conditions that reflect achievable climate scenarios for the 21st century. “Representative concentration pathways” (RCPs) describe assumptions about possible future concentrations of greenhouse gases [ 31 ]. In this study, we used the global climate model of the Beijing Climate Center (BCC-CSM1-1), because we are mainly concerned about the invasion of this species in China. BCC-CSM1-1 is one of the most widely used global climate models [ 32 ]. We chose RCP 2.6 as a drastic mitigation scenario, RCP 6.0 as a general mitigation scenario, and RCP 8.5 as a scenario with no additional efforts. All atmospheric data were acquired with a high resolution of 2.5 arcmin (5 km × 5 km). We cross-correlated ( | r ) The final climate variables in our modeling and analysis included Bio1 = annual mean temperature, Bio2 = mean diurnal range, Bio3 = isothermality, Bio10 = warmest quarter mean temperature, Bio12 = annual precipitation, Bio14 = driest month and 18 precipitation season, of the warmest quarter.

MaxEnt is a state-of-the-art machine learning method based on the maximum entropy model, which can predict species distribution from data on species occurrence and environmental variables [ 34 , 35 ]. The calibration step is crucial for rigorous model building, and aims to find which combination of parameters best represents the phenomenon by finding the best fit to the data [ 36 , 37 ]. We used the kuenm R package [38] to test candidate solutions, including all 31 possible combinations of 5 feature classes (linear = l, quadratic = q, product = p, threshold = t, and hinge = h). and 10 regularization multiplier settings. (0.1, 0.3, 0.6, 0.9, 1, 2, 3, 4, 5, and 6) Partial receiver operating characteristic (ROC) analysis with 500 iterations, 50% data for bootstrapping, 5% missing rate, and Based on the modified Akaike Information Criterion (AICc). The best candidate models were selected according to the following criteria: (1) prominent models with (2) omission rate ≤ 5%. Then, from these candidate models, models with delta AICc values ​​≤ 2 were selected as the final models for mapping and projection [ 38 ]. We set 70% of the occurrence points for use in model calibration and the remaining 30% for evaluating model predictions, with a logistic output format ranging from 0 (unsuitable environmental conditions) to 1.

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