Potential Distribution Of Species Research Articles

First records of Oxychilus alliarius and O. cellarius (Gastropoda, Stylommatophora, Oxychilidae) in Mexico: mtDNA identification and potential distributions

This paper reports the first Mexican records of Oxychilus alliarius (Puebla, State of Mexico, Mexico City) and O. cellarius (Mexico City), and expands the Mexican distribution of O. draparnaudi to Querétaro, Tlaxcala, and State of Mexico. These three introduced land snail species were identified by combining their genital anatomy and mitochondrial COI DNA sequence data. A two-dimensional geometric morphometric analysis of shell shape variation based on both apertural and apical views showed that there were no significant conchological differences between the three species except, to some degree, size. Using locality data of newly collected specimens, information from previous studies, and data retrieved from GBIF and iNaturalist, an analysis of the potential distributions of Oxychilus species in Mexico was conducted with an R implementation of Maxent. This showed that Oxychilus tends to occupy principally the Southern Highlands and the Transmexican Volcanic Belt Province.

Potential global distributions of an important aphid pest, Rhopalosiphum padi: insights from ensemble models with multiple variables.

Species distribution modeling is extensively used for predicting potential distributions of invasive species. However, an ensemble modeling approach has been less frequently used particularly pest species. The bird cherry-oat aphid Rhopalosiphum padi L. is an important pest of wheat (Triticum aestivum L.) worldwide and causes 30% yield losses. Here, we developed a series of ensemble models with multiple variables to predict the habitat suitability of this pest at a global scale. The current suitable habitat for R. padi is mainly distributed in East Asia, South Asia, Europe, southern North America, southern South America, eastern Australia, and New Zealand. The highly suitable regions are primarily distributed in east of China, Japan, most of North America, southeastern South America, most of Europe, and southeastern edge of Australia. In future scenarios, the suitable habitats will undergo a significant contraction overall northward, and no moderately nor highly suitable habitats are predicted for this pest in other areas. Our findings indicate that a high risk of R. padi outbreaks currently exists for the highly suitable regions mentioned above, especially with wheat cultivation, but the capacity of R. padi to cause such outbreaks will weaken in the future. Climate-associated factors are significantly more important than land use, elevation and host-plant factors, and the BIO11 (mean temperature of the coldest quarter), in particular, predominated in shaping projections of R. padi's distribution. The predicted distribution pattern and key ecological factors affecting this pattern identified herein could provide important guidance for developing management policies targeting this economically important pest.

Double Trouble for Native Species Under Climate Change: Habitat Loss and Increased Environmental Overlap With Non-Native Species.

Climate change and biological invasions are affecting natural ecosystems globally. The effects of these stressors on native species' biogeography have been studied separately, but their combined effects remain overlooked. Here, we develop a framework to assess how climate change influences both the range and niche overlap of native and non-native species using ecological niche models. We hypothesize that species with similar niches will experience both range reductions and increased niche overlap under future climates. We evaluate this using the ongoing invasion of smallmouth bass (Micropterus dolomieu) and northern pike (Esox lucius) on the native habitats of redband trout (Oncorhynchus mykiss) and bull trout (Salvelinus confluentus) in western North America. Future climate conditions will reduce habitat suitability for native and non-native species, but an increased niche overlap might exacerbate negative effects on native fishes. Our framework offers a tool to predict potential species distribution and interactions under climate change, informing adaptive management globally.

Моделювання поширення видів засобами MaxEnt: огляд і перспективи

Niche modelling of species has become increasingly important in the context of accelerating climate change and anthropogenic impacts on the biosphere. One such tool for predicting the potential distribution of species is the maximum entropy method (MaxEnt). This method is particularly valuable when working with biodiversity data collected from herbaria and museum collections, as such data typically only contain information about where a species has been recorded, rather than where it is absent. It is precisely this feature of MaxEnt that makes it an indispensable tool for biodiversity research based on historical data. This allows for the reconstruction of historical species ranges, the detection of changes in their distribution, and the forecasting of future trends, namely the prediction of potential ranges, the assessment of the impact of climate change and anthropogenic pressure, and the development of effective biodiversity conservation strategies. This article provides a brief overview of the MaxEnt software’s operating principle, its capabilities, and limitations. In particular, it analyses the impact of data quality on modelling results and considers various approaches to assessing the importance of ecological factors for species distribution. One of the key issues discussed in the article is the problem of sampling bias. Sampling bias arises because data on the presence of species are often collected non-randomly and depend on the accessibility of the locality, the interests of researchers, and other factors. This can lead to distortions in modelling results. Various methods can be used to correct these biases, such as the bias grid method and the background points method. Another important aspect is the choice of the territory for the background sample. It should be taken into account that when using projections where cells have different areas, MaxEnt may give incorrect results. The article also emphasises the need for cautious interpretation of modelling results. Assessing niche models solely based on AUC (area under ROC curve) can be misleading, therefore, for a more reliable assessment of variable importance, it is worth supplementing it with permutation importance and the jackknife method. Examples of modelling for various groups, including mammals of the Ukrainian fauna, were considered.

Prediction of change in suitable habitats of Senna obtusifolia and Senna tora under climate change

Senna obtusifolia (L.) Irwin & Barneby and Senna tora (L.) Roxb represent important medicinal resources in traditional Chinese medicine for more than two millennia. Sustainable resource utilization and preservation strategies for Senna species necessitate a thorough understanding of the climatic factors governing their distribution patterns. Therefore, this study aimed to identify the key climate variables shaping the current and potential future global distribution of both Senna species. To achieve this, the MaxEnt ecological niche model was employed, integrating species occurrence data with relevant environmental variables. The results indicated that Bio13 and Bio14 were the most critical variables affecting distribution of S. tora, while Bio6 and Bio14 were crucial for S. obtusifolia. The moderate and high suitability habitats of S. obtusifolia and S. tora consist of ca. 189.69 × 104 km2 and 129.07 × 104 km2, respectively, under current situation. Moreover, the global distribution of both species under various climate scenarios revealed that the suitable habitats of both Senna species will reach the maximum during the 2081–2100 period under the SSP585 scenario. Projections across all four climate scenarios indicate a general northward migration in the core distribution of both Senna species. Intriguingly, the observed high degree of ecological niche overlap between the two species aligns with their close phylogenetic relationship. These findings provide valuable insights into the potential future distribution and ecological niche of Senna species, informing sustainable utilization and preservation strategies for Senna resources.

Representativeness, Complementarity, and Degree of Local Extirpation Risk for Thamnophis Species Inside and Outside of Protected Areas of Mexico

Protected areas (PAs) are geographical spaces intended to conserve populations, communities, and ecosystems, in which species richness must be maximized, the conserved area must be minimized, and anthropogenic pressure must be reduced. The present study analyzed the representativeness, complementarity, and degree of risk of 25 garter snake species of the genus Thamnophis in the PAs of Mexico. This study proposes that at least 17% of the potential geographic distribution (PGD) of species will be found inside PAs and in areas (Aichi Target 11) with a low human footprint (HF). The PGD of species was associated with the PAs and HF layers to identify where and which species could be at local extirpation risk by human activities. The results indicate that the federal PAs contain 85.2% of the species, while the state PAs contain 77.7% of the species. An average of 13.4% of the PGD of these species is found inside PAs, and two species are found outside. In 13 federal PAs and 10 state PAs, the Thamnophis species present high local extirpation risk from human activities. In total, 37% of species are found in PAs with a medium to very high human footprint; therefore, their persistence could be at local extirpation risk. Compared to other taxa, species of the genus Thamnophis are well represented. However, the PDG of more than half of the species achieves Aichi Target 11.

In‐situ and ex‐situ conservation priorities and distribution of lentil wild relatives under climate change: A modelling approach

Abstract Lentil wild relatives are an important source of desirable traits that can be used for improving the productivity and resilience of cultivated lentil. Yet, our understanding of their habitat suitability and associated environmental factors remains limited. This study aimed to (i) assess climate change's impact on the potential distribution of six wild lentil species (Lens culinaris subsp. orientalis, L. culinaris subsp. tomentosus, L. culinaris subsp. odemensis, Lens ervoides, Lens lamotte and Lens nigricans) under various climate scenarios and (ii) assess their risk of extinction and determine their in‐situ and ex‐situ conservation status. We used a species distribution modelling approach with MaxEnt to assess the present and future potential distribution of wild lentil species. Extinction risk was evaluated based on International Union of Conservation of Nature criterion B, and the conservation status was assessed using the GapAnalysis method. The precipitation of the coldest quarter (bio19) and the minimum temperature of the coldest month (bio6) were found as the most important variables influencing the distribution of wild lentil species. Final Conservation Score (FCS) ranged from 17.85 and 37.55, highlighting three wild lentil species (L. ervoides, L. nigricans, and L. culinaris subsp. tomentosus) with high priority for conservation and medium priority for the remaining species. L. culinaris subsp. tomentosus is categorized as a vulnerable species, while the other five species are of least concern or near threatened. Synthesis and applications: This study underscores the urgent need for policy development to safeguard the diversity of lentil wild relatives in the face of climate change. The identified vulnerability of Lens culinaris subsp. tomentosus, among others, needs prompt and proactive conservation actions. Key management practices include the establishment and expansion of protected areas, habitat restoration, and the promotion of sustainable land use practices. The integration of effective in‐situ and ex‐situ conservation strategies, along with ecological management practices, is essential. These measures, not only, enhance biodiversity conservation but also improve the resilience of agricultural ecosystems. Such an approach is pivotal in shaping effective conservation management practices for lentil wild relatives, promoting a sustainable agricultural system and ensuring food security in an evolving climate scenario.

Molecular and niche modeling approaches to identify potential amplifying hosts for an emerging tick-borne pathogen, Rickettsia rickettsii subsp. californica, the causative agent of Pacific Coast tick fever.

Pacific Coast tick fever is a recently described zoonotic disease in California caused by a spotted fever group rickettsia, Rickettsia rickettsii subsp. californica (formerly Rickettsia 364D) and transmitted by the Pacific Coast tick, Dermacentor occidentalis. Like many emerging vector-borne diseases, knowledge regarding the transmission cycle, contribution from potential amplifying hosts, and geographic distribution of R. rickettsii californica is limited. We paired molecular analysis with comparative spatial niche modeling to identify vertebrate hosts potentially involved in the transmission cycle of this pathogen. We identified R. rickettsii californica DNA in three mammal species (Otospermophilus beecheyi, Lepus californicus, and Sylvilagus audubonii). This is the first record of R. rickettsii californica detected in mammals and may indicate potential amplifying hosts for this human pathogen. Species niche modeling of uninfected and infected D. occidentalis identified areas of high suitability along the coast and Sierra Nevada foothills of California. These findings support the hypothesis that amplifying host(s) may support higher infection prevalence in the infected tick regions compared to other parts of the tick's range. Potential host species distribution models (SDMs) were constructed from museum records and niche overlap statistics were used to compare habitat suitability with R. rickettsii californica-infected tick SDMs. We found higher than null overlap of infected ticks with California ground squirrels (O. beecheyii) and trending, but nonsignificant, overlap with two lagomorph species. Pairing molecular and niche modeling may be a useful approach to identify species that are involved in the maintenance of emerging tick-borne zoonoses.

Predicted occurrence and abundance habitat suitability of invasive plants in the contiguous United States: updates for the INHABIT web tool

Invasive plant species have substantial negative ecological and economic impacts. Geographic information on the potential and actual distributions of invasive plants is critical for their effective management. For many regions, numerous sources of predictive geographic information exist for invasive plants, often in the form of outputs from species distribution models (SDMs). The creation of a repository of consistently produced SDMs of regional- or national-scale information predicting the potential distribution of invasive plant species could provide information to managers in the prioritisation of invasive species management. Here, we present a novel set of not only habitat suitability models for occurrence for 259 manager requested invasive plant species in the contiguous United States (USA), but also habitat suitability models for abundance (≥ 5% cover) and high abundance (≥ 25% cover). These data provide an update to the Invasive Species Habitat Tool (INHABIT; gis.usgs.gov/inhabit). This tool contains information on the majority of invasive plant species in the contiguous USA with sufficient location data for model building. INHABIT provides a canonical set of predicted geographic distributions for invasive plants in the contiguous USA that can aid in the search for new populations of invasive plant species and help create watch lists for emerging invaders. As this tool contains information on nearly all of the most problematic invasive plants in the contiguous USA, it helps in prioritising management strategies by showing which plants are already present or abundant in a land management area and which may become present or abundant in the future.

Predicting the potential distribution of the invasive species, Ophelimus maskelli (Ashmead) (Hymenoptera: Eulophidae), and its natural enemy Closterocerus chamaeleon (Hymenoptera: Eulophidae), under current and future climate conditions.

Invasive species pose a threat to ecosystems and humans worldwide, which is exacerbated by climate change, causing the expansion of species distributions. Ophelimus maskelli (Ashmead) (Hymenoptera: Eulophidae) causes leaf drying and shedding in eucalyptus trees, forming blister-like galls that negatively impact the growth of the trees. Closterocerus chamaeleon (Hymenoptera: Eulophidae) is a recognized parasitoid of O. maskelli. This study used the MaxEnt and CLIMEX models to predict the potential distribution under current and future climate scenarios for O. maskelli and its natural enemy, C. chamaeleon. The MaxEnt model result indicated that isothermality was the most critical factor predicting the distribution of O. maskelli, while the mean temperature of the driest quarter was the most critical factor predicting the distribution of C. chamaeleon. Under current climate conditions, the CLIMEX model predicted a wider potential distribution for O. maskelli and a smaller distribution for C. chamaeleon than the MaxEnt model. MaxEnt and CLIMEX prediction results indicated that South America and Africa were suitable for O. maskelli and C. chamaeleon. The MaxEnt model indicated that under SSP245 climate conditions, the potentially suitable regions for these species expanded, while under the SSP126 climate scenario, the region contracted significantly. The CLIMEX model indicated that under the A1B and A2 climate scenarios, the marginally suitable areas increased, while the moderately and highly suitable areas decreased. This study provides a theoretical basis for creating early monitoring, quarantine, and control methods for invasive pests.

Fish distribution shifts due to climate change in the Northeast Atlantic: Using a hierarchical filtering approach on marine-estuarine opportunist species

Marine-estuarine opportunist (MEO) species are fish that occur in the continental shelf and use estuaries and/or shallow coastal areas as nurseries. These commercially important resources are facing significant environmental modifications caused by direct and/or indirect anthropogenic climate change effects. In this study, we investigated the directionality and the magnitude of the distribution shifts (i.e., range size, gravity centroids, and margins) in marine environment suitability for six main MEO fish species within the Northeast Atlantic expected for the end of the 21st century. In the framework of this study, we have distinguished ‘sub-boreal’ from ‘sub-tropical’ species. The ‘hierarchical filters’ concept was adopted for modelling the potential species distributions and combined the predictions of i) a bioclimatic model with ii) a habitat model. The bioclimatic model is based on large-scale and time-variant variables while variables of the habitat model are fine-grained and time-invariant. Two Intergovernmental Panel on Climate Change (IPCC) scenarios are tested: an intermediate (SSP2-4.5) and a pessimistic one (SSP5-8.5). We applied this framework using international databases of biodiversity occurrences, ensemble forecasting producing consensual predictions, and innovative indices of distribution shifts. A visible north-westward shift was predicted for all six species in our study area. However, the northward expansion was greater for ‘sub-tropical’ than for ‘sub-boreal’ species due to faster gravity centroid displacement shifts and faster margins shifts. These range shifts may lead to major ecological impacts (e.g., changes in recruitment to estuarine and coastal nurseries, as well as changes in spawning grounds) that may alter populations' connectivity.

Predictions of species distributions based only on models estimating future climate change are not reliable

Changes in climate and land use are the most often mentioned factors responsible for the current decline in species diversity. To reduce the effect of these factors, we need reliable predictions of future species distributions. This is usually done by utilizing species distribution models (SDMs) based on expected climate. Here we explore the accuracy of such projections: we use orchid (Orchidaceae) recordings and environmental (mainly climatic) data from the years 1901–1950 in SDMs to predict maps of potential species distributions in 1980–2014. This should enable us to compare the predictions of species distributions in 1980–2014, based on records of species distribution in the years 1901–1950, with real data in the 1980–2014 period. We found that the predictions of the SDMs often differ from reality in this experiment. The results clearly indicate that SDM predictions of future species distributions as a reaction to climate change must be treated with caution.

Predicting the geographical potential distribution of species Opisina arenosella Walker in China under different climate scenarios based on the MaxEnt model.

As global warming increases with the frequency of extreme weather, the distribution of species is inevitably affected. Among them, highly damaging invasive species are of particular concern. Being able to effectively predict the geographic distribution of invasive species and future distribution trends is a key entry point for their control. Opisina arenosella Walker is an invasive species, and its ability to live on the backs of foliage and generate canals to hide adds to the difficulty of control. In this paper, the current and future distributions of O. arenosella under three typical emission scenarios in 2050 and 2090 are projected based on the MaxEnt model combining 19 bioclimatic variables. Filter through the variables to find the four key environment variables: BIO 1, BIO 6, BIO 11 and BIO 4. The results show that O. arenosella is distributed only in the eight provinces of Tibet, Yunnan, Fujian, Guangxi, Taiwan, Guangdong, Hong Kong and Hainan in the southeastern region. Its high suitability area is concentrated in Taiwan and Hainan. In the long run, highly suitable areas will continue to increase in size, while moderately suitable areas and poorly suitable areas will decrease to varying degrees. This paper aims to provide theoretical references for the control of O. arenosella.

A Dataset for Species Distribution Modelling of Mangroves in Vietnam: Based on the National Forest Inventory Monitoring

Mangroves provides essential ecosystem services such as protection of coastal areas, carbon sequestration, and habitat provision for diverse species in coastal ecosystems. Species distribution models (SDMs) are powerful tools for predicting the potential distribution of mangrove species, which support impact assessments of climate changes on biodiversity and ecological functions of mangrove ecosystems. A comprehensive dataset for mangrove occurrence information derived from the Forest Inventory Map of Vietnam was designed to facilitate the building and projection of SDMs. The prediction data designed for training SDMs integrates ecological information including 701 field survey-based mangrove occurrences at the genus level and 21 environmental variables such as bioclimatic variables, digital elevation model and soil properties with 1 km spatial resolution. The projection data for provide sets of predictors aligned with four shared socioeconomic pathways scenarios representing two future periods to support the projection of SDM results under future climate conditions in Vietnam. This dataset serves as a valuable ecological information resource, enabling the modeling and predicting of potential mangrove habitats and distributions for the protection and restoration of mangroves in Vietnam under changing environmental conditions.

Distribution and diversity of ticks determined by environmental factors in Ningxia, China

Ticks are important vectors of zoonotic pathogens, and represent an increasing threat for human and animal health. Considering the complex natural environments of Ningxia Hui Autonomous Region, China, we expect the diverse tick species in this region. Here, we conduct a field survey on parasitic and host-seeking ticks. A total of 10,419 ticks were collected, which belonged to nine species of four genera. There were significant differences in terms of vegetation index, altitude, and seven climatic factors among the four tick genera —Hyalomma, Dermacentor, Haemaphysalis, and Ixodes, except between Haemaphysalis and Ixodes, where no significant differences were observed in these factors. The ecological niche modelling revealed that the suitable habitats for Hyalomma asiaticum was in the northwest Ningxia, with annual ground surface temperature as the most important factor. The suitable area for Dermacentor nuttalli was in the southwest and eastern regions of Ningxia with elevation as the highest contribution. D. silvarum was best suited to the southern Ningxia also with elevation as the most important factor. The four tick species including Haemaphysalis longicornis, Hae. qinghaiensis, Hae. japonica, and Ixodes persulcatus were best suited to the southernmost Ningxia with annual precipitation as the main factors for Hae. longicornis and elevation for the other three ticks. The results of predicted potential distribution of different tick species provide a scientific basis for the prevention and control of ticks and tick-borne diseases in the region. Furthermore, the subsequent impacts of the Greening Program to regain forests and grasslands from former agricultural lands in Ningxia on tick population dynamics deserve further investigation.

Impacts of Distribution Data on Accurate Species Modeling: A Case Study of Litsea auriculata (Lauraceae).

Global warming has caused many species to become endangered or even extinct. Describing and predicting how species will respond to global warming is one of the hotspots of biodiversity research. Species distribution models predict the potential distribution of species based on species occurrence data. However, the impact of the accuracy of the distribution data on the prediction results is poorly studied. In this study, we used the endemic plant Litsea auriculata (Lauraceae) as a case study. By collecting and assembling six different datasets of this species, we used MaxEnt to perform species distribution modeling and then conducted comparative analyses. The results show that, based on our updated complete correct dataset (dataset 1), the suitable distribution of this species is mainly located in the Ta-pieh Mountain, southwestern Hubei and northern Zhejiang, and that mean diurnal temperature range (MDTR) and temperature annual range (TAR) play important roles in shaping the distribution of Litsea auriculata. Compared with the correct data, the wrong data leads to a larger and expanded range in the predicted distribution area, whereas the species modeling based on the correct but incomplete data predicts a small and contracted range. We found that only about 23.38% of Litsea auriculata is located within nature reserves, so there is a huge conservation gap. Our study emphasized the importance of correct and complete distribution data for accurate prediction of species distribution regions; both incomplete and incorrect data can give misleading prediction results. In addition, our study also revealed the distribution characteristics and conservation gap of Litsea auriculata, laying the foundation for the development of reasonable conservation strategies for this species.

Precipitation and temperature timings underlying bioclimatic variables rearrange under climate change globally.

Modeling how climate change may affect the potential distribution of species and communities typically utilizes bioclimatic variables. Distribution predictions rely on the values of the bioclimatic variable (e.g., precipitation of the wettest quarter). However, the ecological meaning of most of these variables depends strongly on the within-year position of a specific climate period (SCP), for example, the wettest quarter of the year, which is often overlooked. Our aim was to determine how the within-year position of the SCPs would shift (SCP shift) in reaction to climate change in a global context. We calculated the deviations of the future within-year position of the SCPs relative to the reference period. We used four future time periods, four scenarios, and four CMIP6 global climate models (GCMs) to provide an ensemble of expectations regarding SCP shifts and locate the spatial hotspots of the shifts. Also, the size and frequency of the SCP shifts were subjected to linear models to evaluate the importance of the impact modeler's decision on time period, scenario, and GCM. We found ample examples of SCP shifts exceeding 2 months, with 6-month shifts being predicted as well. Many areas in the tropics are expected to experience both temperature and precipitation-related shifts, but precipitation-related shifts are abundantly predicted for the temperate and arctic zones as well. The combined shifts at the Equator reinforce the likelihood of the emergence of no-analogue climates there. The shifts become more pronounced as time and scenario progress, while GCMs could not be ranked in a clear order in this respect. For most SCPs, the modeler's decision on the GCM was the least important, while the choice of time period was typically more important than the choice of scenario. Future predictive distribution models should account for SCP shifts and incorporate the phenomenon in the modeling efforts.

Precipitation and temperature primarily determine the reptile distributions in China

Reptiles make up one‐third of tetrapods, however they are often omitted from global conservation analyses. Understanding the determinants of reptile distribution is the foundation for reptile conservation research. We assembled a dataset on the distribution of 231 reptile species (nearly 50% of recorded species in China). We then investigated the association of species range filling (the proportion of observed ranges compared to species potential climate distributions) with climate, range size, topography and human activity, using three regression methods. At the species level, we found the most primary factors influencing the recent distribution pattern of reptiles across China were the mean annual precipitation (MAP) and the mean annual temperature (MAT). In contrast, human activity came in last. Similarly, at a spatial level, MAP and MAT were still the most important factors. Geographically, the south and east of China support the highest reptile diversity, partially due to high precipitation. Contrary to the prevailing notion that reptile distributions are primarily shaped by human activities, our study re‐emphasizes the importance of climate in determining reptile distribution. It accentuates the necessity of integrating climate variables into future animal conservation strategies in China. Our findings also offer valuable insights for informing conservation practices, including the site planning of natural reserves and national parks, as well as monitoring of environmental factors in protected areas.

Paleo-species distribution modelling in the marine realm: protocol for a global scoping review

Background Studying the spatial distribution of marine species and its alterations through time is fundamental for understanding how they respond to environmental changes, as well as the changes of marine biodiversity, its geographic patterns, and how to best preserve it. Species distribution models (SDM) and Ecological Niche Models (ENM) are statistical tools widely used to investigate distribution range shifts under future and past climate change. The present work provides a protocol for conducting a scoping review (ScR) to assess the contribution of SDM and ENM to the study of past environmental suitability, its spatial and temporal extent and gaps, as well as to identify methodological challenges. Protocol The ScR follows the Joanna Briggs Institute (JBI) methodology. Two online databases, Web of Science and Scopus, will be used for the bibliographic search. Inclusion criteria will be as follows: studies applying SDM and/or ENM in the marine realms worldwide, with clear methodological details, to identify to what extent they contributed to paleobiology. Both peer-reviewed articles, book chapters and grey literature will be considered for eligibility. No search limitations will be applied regarding publication year, geographic area and source type. Studies in English, Spanish and French will be reviewed. The Covidence software will be used for document selection and data extraction. Conclusions This comprehensive approach will provide a visual representation of the available studies, enhancing the understanding and interpretation of the results focusing on the contribution of SDM and ENM to species potential ancient distribution knowledge, identifying the spatial and temporal extent and the presence of gaps of knowledge in their use.

Uncertainty Assessment of Species Distribution Prediction Using Multiple Global Climate Models on the Tibetan Plateau: A Case Study of Gentiana yunnanensis and Gentiana siphonantha

Species distribution models (SDMs) have been widely used to project how species respond to future climate changes as forecasted by global climate models (GCMs). While uncertainties in GCMs specific to the Tibetan Plateau have been acknowledged, their impacts on species distribution modeling needs to be explored. Here, we employed ten algorithms to evaluate the uncertainties of SDMs across four GCMs (ACCESS-CM2, CMCC-ESM2, MPI-ESM1-2-HR, and UKESM1-0-LL) under two shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5) at two time stages. We selected two endemic species of the Tibetan Plateau, Gentiana yunnanensis and G. siphonantha, distributed in the Hengduan Mountain regions of the southeast plateau and northeast plateau regions, respectively, as case studies. Under the two SSPs and two time periods, there are significant differences in the distribution areas of G. yunnanensis predicted by different GCMs, with some showing increases and others showing decreases. In contrast, the distribution range trends for G. siphonantha predicted by different GCMs are consistent, initially increasing and then decreasing. The CMCC-ESM2 model predicted the largest increase in the distribution range of G. yunnanensis, while the UKESM1-0-LL model predicted the greatest decrease in the distribution range of G. siphonantha. Our findings highlight that the four selected GCMs still lead to some variations in the final outcome despite the existence of similar trends. We recommend employing the average values from the four selected GCMs to simulate species potential distribution under future climate change scenarios to mitigate uncertainties among GCMs.