Deteriorating wintertime habitat conditions for waterfowls in Caizi Lake, China: Drivers and adaptive measures

China has made enormous strides to achieve high-quality development and biodiversity conservation, and the establishment of nature-protected areas is one of the essential initiatives. Caizi Lake involves a natural reserve and two national wetland parks, accommodating winter migratory waterfowl over the middle and lower Yangtze River basin in China. However, the water transfer from the Yangtze River to the Huai River (YR-HR water transfer) has modified the winter hydrological conditions of Caizi Lake, negatively affecting wintertime waterfowl habitats. Hence, conserving wintertime waterfowl habitats necessitates knowledge of the dynamical mechanisms behind the impacts of YR-HR water transfer on wintertime waterfowl habitats and adaptive measures. Here we developed a machine learning model, the normalized difference vegetation index, and on-spot observatory datasets such as the spatial distribution of waterfowl species and underwater topography of Caizi Lake. We found that the rising winter water level of Caizi Lake encroaches on winter waterfowl habitat with extremely high suitability. Meanwhile, rising water levels reduced waterfowl food sources. Thus, rising water levels due to YR-HR water transfer deteriorated waterfowl living conditions over Caizi Lake. Therefore, we proposed adaptive measures to alleviate these negative effects, such as water level regulation, artificial feeding of waterfowls, restoration and reconstruction of contiguous mudflats, grass flats. This study highlights human interferences with waterfowl habitats, necessitating biodiversity conservation at regional scales.

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.

Incorporating Implicit Information to Disentangle the Impacts of Hydropower Dams and Climate Change on Basin-Scale Fish Habitat Distribution

The loss of freshwater fish habitats, exacerbated by climate change and dam constructions, poses a critical environmental concern. The upper Yangtze River basin, noted for its abundant fish fauna and concentrated dam development, serves as a crucial locale for investigating the impacts of climate shifts and dam construction. This study aims to disentangle the impacts of hydroelectric dams and climate change on fish habitat distribution by analyzing species presence data across different periods. Species distribution models were constructed using Maxent for Coreius guichenoti (a warm-water endangered fish) and Schizopygopsis malacanthus (a cold-water endangered fish). The model accuracy was assessed using the area under the curve of the receiver operating characteristic. Habitat distribution modeling and prediction for the pre-dam period (1970-2000) and post-dam period (2001-2020), as well as future climate change under two shared socioeconomic pathways scenarios, were conducted. The impacts of climate change and dam construction on the habitat suitability of two fish species were quantified. The results revealed dam construction predominantly diminished habitat suitability and range, with high-suitability habitats in the post-dam period decreasing by 56.3% (720.18 km) and 67.0% (1665.52 km) for the two fishes, respectively. Climate change would enhance the habitat suitability of Coreius guichenoti, while it would decrease the habitat suitability of Schizopygopsis malacanthus. The impact of dam construction is greater that of climate change for them. This study underscores the profound impacts of dam construction on fish habitats, particularly for cold-water species, and highlights the critical need for habitat restoration in sustainable hydropower development. Our method of disentangling these factors also provides a new approach to evaluating environmental impacts in large river basins.

Forecasting habitat suitability and niche shifts of two global maize pests: Ostrinia furnacalis and Ostrinia nubilalis (Lepidoptera: Crambidae)

BACKGROUND

Ostrinia furnacalis (ACB) and Ostrinia nubilalis (ECB) are devastating pests of the agricultural crop maize worldwide. However, little is known about their potential distribution and niche shifts during their global invasion. Since long-term selection to past climate variability has shaped their historical niche breadth, such niche shifts may provide an alternative basis for understanding their responses to present and future climate change. By integrating the niche unfilling, stability, and expansion situations into a single framework, our study quantifies the patterns of niche shift in the spatial distribution of these two pests during the different periods.

RESULTS

Our results show that the overall suitable habitats of ACB and ECB in the future decrease but highly and extremely suitable habitat will become more widespread, suggesting these two insects may occur more frequently in specific regions. Compared with Southeast Asia and Australia, the ACB niche in China exhibited expansion rather than unfilling. For ECB, initial niches have a tendency to be retained in Eurasia despite there also being potential for expansion in North America. The niche equivalency and similarity test results further indicate that niche shifts were common for both ACB and ECB in different survival regions during their colonization of new habitat and their suitable habitat changes during the paleoclimate were associated with climatic changes.

CONCLUSIONS

These findings improve our understanding of the ecological characteristics of ACB and ECB worldwide, and will be useful in the development of prevention and control strategies for two insect pests worldwide. © 2024 Society of Chemical Industry.

Assessing and correcting neighborhood socioeconomic spatial sampling biases in citizen science mosquito data collection

Climatic, ecological, and socioeconomic factors are facilitating the spread of mosquito-borne diseases, heightening the importance of vector surveillance and control. Citizen science is proving to be an effective tool to track mosquito populations, but methods are needed to detect and account for small scale sampling biases in citizen science surveillance. In this article we combine two types of traditional mosquito surveillance records with data from the Mosquito Alert citizen science system to explore the ways in which the socioeconomic characteristics of urban neighborhoods result in sampling biases in citizen scientists' mosquito reports, while also shaping the spatial distribution of mosquito populations themselves. We use Barcelona, Spain, as an example, and focus on Aedes albopictus, an invasive vector species of concern worldwide. Our results suggest citizen scientists' sampling effort is focused more in Barcelona's lower and middle income census tracts than in its higher income ones, whereas Ae. albopictus populations are concentrated in the city's upper-middle income tracts. High resolution estimates of the spatial distribution of Ae. albopictus risk can be improved by controlling for citizen scientists' sampling effort, making it possible to provide better insights for efficiently targeting control efforts. Our methodology can be replicated in other cities faced with vector mosquitoes to improve public health responses to mosquito-borne diseases, which impose massive burdens on communities worldwide.

Analysis of the Interrelation and Seasonal Variation Characteristics of the Spatial Niche of Dominant Fishery Species-A Case Study of the East China Sea

The spatial niche has garnered significant attention in ecological research, particularly regarding species distribution patterns. The East China Sea, known for its favorable natural conditions and abundant fishery resources, exhibits diverse spatial distribution patterns among species, shaped by their seasonal physiological needs. This study utilized a habitat suitability index model to explore the spatial distribution patterns of key fishery resources in the East China Sea across four seasons and their interactions. Two methodologies were employed to identify key environmental factors and assess the ecological niche overlap among different species and seasons. Results indicated that the initial method identified water temperature as the critical factor for hairtail, while the subsequent method emphasized water temperature and salinity for hairtail, salinity for small yellow croaker, and water depth for Bombay duck. The main spatial habitat overlap was observed between paired species, likely driven by predator-prey interactions. During summer and autumn, increased overlap among multiple species was primarily influenced by synchronized life cycles. An overlap index formula quantified the seasonal species overlap, showing an increase from spring to winter, reflecting changes in convergent habitat preferences. The peak overlap occurred in winter, driven by overwintering, reduced food competition, and enhanced coexistence potential, while the lowest overlap was noted in spring as overwintering ended and predation and competition intensified.

Potentially suitable geographical area for Colletotrichum acutatum under current and future climatic scenarios based on optimized MaxEnt model

Global climate warming has led to changes in the suitable habitats for fungi. Colletotrichum acutatum, a common fungus causing anthracnose disease, is widely distributed in southern China. Currently, research on the relationship between C. acutatum and environmental warming was limited. In this study, MaxEnt and ArcGIS software were used to predict the suitable habitats of C. acutatum under current and future climate conditions based on its occurrence records and environmental factors. The optimal MaxEnt model parameters were set as feature combination (FC) = lp and regularization multiplier (RM) = 2.6. Bio15, Bio12, Bio09, and Bio19 were identified as the main environmental factors influencing the distribution of C. acutatum. Under current climate conditions, C. acutatum was distributed across all continents globally, except Antarctica. In China, C. acutatum was primarily distributed south of the Qinling-Huaihe Line, with a total suitable area of 259.52 × 104 km2. Under future climate conditions, the potential suitable habitat area for C. acutatum was expected to increase and spread towards inland China. The results of this study provided timely risk assessment for the distribution and spread of C. acutatum in China and offer scientific guidance for monitoring and timely controlled of its distribution areas.

MaxEnt Modeling for Predicting the Potential Geographical Distribution of Hydrocera triflora since the Last Interglacial and under Future Climate Scenarios

Hydrocera triflora is a perennial herb found in southeastern and southern Asia. In China, it is only found in Hainan Province. With global climate change, studying the impact of climate change on the distribution of H. triflora can provide a theoretical basis for the scientific protection of this species. In this study, the MaxEnt model was used to predict the potential distribution area of H. triflora in China under historical, current, and future periods based on 66 distribution points and 12 environmental variables. The results were as follows: (i) The main environmental variables affecting the distribution of H. triflora were precipitation in the coldest month and in the wettest quarter, with elevation also being a significant factor. (ii) Over the past three periods, the last interglacial, last glacial maximum, and mid-Holocene, the suitable area for H. triflora initially decreased and then increased. The suitable area reached the lowest value in the last glacial maximum period, at only 27.03 × 104 km2. (iii) The current potential distribution area is 67.81 × 104 km2, and the optimal area is mainly distributed in the Guangxi, Guangdong, and Hainan provinces. (iv) Under future climate scenarios, the potential distribution area of H. triflora is projected to increase by 11.27~90.83 × 104 km2. It is expected to reach a maximum value (158.64 × 104 km2) in 2081~2100 under the SSP-585 climate scenario, with the distribution centroid shifting to higher latitudes. The newly gained optimal habitats will provide potential areas for introduction and ex situ conservation of this species.

Combined climate change and dispersal capacity positively affect Hoplobatrachus chinensis occupancy of agricultural wetlands

Global warming significantly impacts amphibian populations globally, and modeling helps understand these effects. Here, we used MaxEnt and MigClim models to predict the impact of climate change on habitat suitability for Hoplobatrachus chinensis. Our results indicate that temperature is a key factor affecting H. chinensis distribution. Increasing temperatures positively correlated with habitat suitability, with suitable habitat expanding northward by 2060 while maintaining suitability in the southern parts of the range. We found a 25.18% overlap between the current potential suitable habitat of H. chinensis and agricultural wetlands. Our model indicated that H. chinensis might be able to track shifts in suitable habitats under climate change given a 15 km dispersal ability per generation. Climate change will likely expand suitable habitat for H. chinensis. Our predictions offer important guidance for the conservation of the species, especially for the integrated role of natural and agricultural wetlands such as rice paddies.

Modeling the Effects of Temperature and Limiting Nutrients on the Competition of an Invasive Floating Plant, Pontederia crassipes, with Submersed Vegetation in a Shallow Lake

The potential for a non-native plant species to invade a new habitat depends on broadscale factors such as climate, local factors such as nutrient availability, and the biotic community of the habitat into which the plant species is introduced. We developed a spatially explicit model to assess the risk of expansion of a floating invasive aquatic plant species (FAV), the water hyacinth (Pontederia crassipes), an invader in the United States, beyond its present range. Our model used known data on growth rates and competition with a native submersed aquatic macrophyte (SAV). In particular, the model simulated an invasion into a habitat with a mean annual temperature different from its own growth optimum, in which we also simulated seasonal fluctuations in temperature. Twenty different nutrient concentrations and eight different temperature scenarios, with different mean annual amplitudes of seasonal temperature variation around the mean of the invaded habitat, were simulated. In each case, the ability of the water hyacinth to invade and either exclude or coexist with the native vegetation was determined. As the temperature pattern was changed from tropical towards increasingly cooler temperate levels, the competitive advantage shifted from the tropical FAV to the more temperate SAV, with a wide range in which coexistence occurred. High nutrient concentrations allowed the coexistence of FAV, even at cooler annual temperatures. But even at the highest nutrient concentrations in the model, the FAV was unlikely to persist under the current climates of latitudes in the Southeastern United States above that of Northern Alabama. This result may have some implications for where control efforts need to be concentrated.

Climate-driven shifts in freshwater biodiversity will impact mitigation costs for hydropower

Climate change is forecasted to drastically alter freshwater fish and mussel species distribution. Hydropower dam reservoirs, which modify downstream thermal regimes, may interact with climate change's impact on species distribution. This distribution shift may feedback, affecting hydropower operation costs through environmental compliance. We investigated how freshwater species distribution will shift due to climate change and hydropower reservoirs in the conterminous United States (CONUS), and how this will affect biodiversity mitigation costs for privately-owned hydropower plants. In general, using environmental niche modeling, we found that climate change increased the range of both freshwater fish and mussel species on average. For fish, this was mainly due to the expanded habitat for warm-water and cool-water fish species despite the diminish in habitat for cold-water species. Compared to climate change, thermal stratification of hydropower reservoirs had a small impact on the future range changes of these species in the tailwaters but showed an interaction with the effect of climate change on species range. Geographically, we projected an increase of species richness in the west and a decrease in the central and east of CONUS for fish, while projecting uniform increase for mussels. With this shift in species distribution, we estimated that the Northwest region will face the largest increase in mitigation cost, while the majority of plants in the Southeast will experience a decrease in cost.

Synthesis of giant panda habitat suitability evaluations

Degradation, fragmentation, and habitat loss significantly threaten the survival of giant pandas (Ailuropoda melanoleuca). Habitat suitability evaluations (HSEs) represent a crucial component of giant panda habitat research. However, a systematic review of HSE research on giant pandas has not been conducted in recent years. To make up for that, we synthesised HSE research on giant pandas publicated from 2013 to 2022 and conducted a comprehensive analysis of the evaluation scale, evaluation methods, and research findings. We found a correlation between the geographical distribution of giant pandas and HSE-based studies on giant pandas. Furthermore, we observed a trend towards interdisciplinary and large-scale research. Although the evaluation accuracy has improved compared to that of earlier work, some limitations still remain, such as concentrated evaluation areas, incomplete indicators, and outdated data. Current HSE research on giant pandas helps determine suitable habitat distributions, facilitating protection strategies and management planning for protected areas. We suggest that future research should prioritize those unexplored or under-evaluated areas, incorporate a broader range of microenvironmental indicators, and update data resources and methodologies. This study bridges the gap in systematic reviews on HSEs of the giant panda and provides valuable references and recommendations for future research as well as the protection and management of giant panda habitats.

Redistribution of vocal snapping shrimps under climate change

A variety of marine organisms can produce sounds that are important components of the marine soundscape and play a critical role in maintaining marine biodiversity. Climate change has greatly altered the geographical ranges of many marine species, including sound-producing organisms. However, the direction and the magnitude of the potential impact of climate change on the geographical distribution of sound-producing marine organisms in future remain largely unknown. To address this knowledge gap, we selected snapping shrimp, one of the most well-known marine sound-producing organisms, as a model species and explored their redistribution under climate change via species distribution models. We aimed to predict the redistribution of snapping shrimps under climate change and identify the influencing factors, which have important implications for marine conservation. Our models exhibited good discrimination abilities and identified maximum temperature as the most influential predictor of snapping shrimp distribution. Model predictions suggested that species richness is higher in tropical and temperate coastal waters and peaks in the Indo-Pacific region. The majority of snapping shrimp species are expected to respond to the changing climate by shifting their geographical ranges to deeper waters and higher latitudes. Our results showed that, in the future, high-latitude species were more likely to experience range expansion, whereas low-latitude species might experience range contraction. Moreover, the Central Indo-Pacific are predicted to suffer the biggest decline in species richness, whereas areas such as the coastal waters of southern Australia and northern China might serve as climate refuges for snapping shrimps in the future. In summary, this study highlights the potential effects of climate change on the distribution of sound-producing snapping shrimps, which may result in cascading effects on marine ecosystems.

Meta-Analysis and MaxEnt Model Prediction of the Distribution of Phenacoccus solenopsis Tinsley in China under the Context of Climate Change

Phenacoccus solenopsis Tinsley is a pest that poses a significant threat to agricultural crops, especially cotton, and is now widely distributed across many regions worldwide. In this study, we performed a meta-analysis on the collected experimental data and found that within the suitable temperature range, the survival rate of P. solenopsis increases with rising temperatures, indicating that climate plays a decisive role in its distribution. Using the MaxEnt model this study predicted that under three future climate scenarios (SSP1-2.6, SSP3-7.0, and SSP5-8.5), the distribution of P. solenopsis will expand and move towards higher latitudes. Climate change is the primary factor influencing changes in pest distribution. We conducted a meta-analysis of P. solenopsis, including seven independent studies covering 221 observation results, and examined the impact of temperature ranging from 18 °C to 39 °C on the developmental cycle of P. solenopsis. As the temperature rises, the development cycle of P. solenopsis gradually decreases. Additionally, by combining the MaxEnt model, we predicted the current and potential future distribution range of P. solenopsis. The results show that under future climate warming, the distribution area of P. solenopsis in China will expand. This research provides a theoretical basis for early monitoring and control of this pest's occurrence and spread. Therefore, the predictive results of this study will provide important information for managers in monitoring P. solenopsis and help them formulate relevant control strategies.

Distribution patterns and potential suitable habitat prediction of Ceracris kiangsu (Orthoptera: Arcypteridae) under climate change- a case study of China and Southeast Asia

Ceracris kiangsu (Orthoptera: Arcypteridae), is greatly affected by climatic factors and exhibits strong adaptability, posing a serious threat to the ecological environment. Therefore, predicting its potential suitable habitat distribution provides a proactive theoretical basis for pest control. This study using the Biomod2 package of R simulated and predicted the current and future potential distribution, area changes, changes in the center points of suitable habitats, and niche shifts of C. kiangsu under two different greenhouse gas emission scenarios, SSP1-26 and SSP5-85. The results show that: (1) Currently, the high suitability areas for C. kiangsu are mainly distributed in Yunnan, Jiangxi, Hunan provinces in southern China and phongsaly province in northern Laos. In the future, the center of the suitable habitat distribution pattern of C. kiangsu will remain unchanged, primarily expanding outward from medium and high suitability areas. Additionally, significant suitable habitats for C. kiangsu were discovered in Southeast Asian countries without previous pest records. (2) Compared to the present, the overall suitable habitat area for C. kiangsu is expected to expand, particularly under the SSP5-85 climate change scenario. (3) In the SSP1-26 and SSP5-85 climate scenarios, the geometric center of the suitable habitat generally shows a trend of gradually shifting northeast. (4) Under different climate scenarios, the suitable habitat of C. kiangsu has highly overlapping, indicating that the suitable habitat of C. kiangsu in the invaded areas is broader than in its native regions. In conclusion, the research findings represent a breakthrough in identifying the potential distribution areas of C. kiangsu, which is of great practical significance for the monitoring and control of C. kiangsu pest infestation in China and Southeast Asian countries.

Using spatial distribution modeling of commercial species to inform management of small-scale fisheries in a Mediterranean marine protected area

Marine protected areas (MPAs) make an essential contribution to the spatial management of critical areas, the conservation of coastal species exploited by human activities, and the sustainable use of marine resources. Within MPAs, fishing closure areas are among the most used small-scale fishery management tools, even though their effectiveness largely remains untested or controversial. To reduce the impact of small-scale fisheries on marine resources, a seasonal fishing closure area (SFCA) was established beginning in 2022 in autumn-winter season inside the Capo Caccia-Isola Piana MPA (Sardinia, northwestern Mediterranean Sea). Here, we assessed a posteriori whether the areas of higher habitat suitability for eight species/taxa of relevant ecological value and economic interest to small-scale fisheries were included in the established SFCA, adequately meeting the ecological objectives of the MPA. Thus, landing data (from 2019 to 2023) were used as occurrence records to develop MaxEnt distribution models for the eight target species/taxa. The model outputs allow us to draw important insights about the spatial adequacy of the SFCA established within the MPA aimed to protect the most exploited marine resources. Furthermore, the modeling exercises were useful for understanding the local processes influencing species' habitat selection and to identify essential areas for the target species that could remain unrevealed in larger-scale investigations.

Long-term changes in spatiotemporal distribution of Noctiluca scintillans in the southern North Sea

To assess the spatiotemporal evolution of the heterotrophic dinoflagellate Noctiluca scintillans in the North Sea, the Helgoland Roads time series and Continuous Plankton Recorder survey were analysed using generalized additive models. Over the last decades, blooms of N. scintillans have occurred more frequently and intensively in many regions. This harmful algal bloom forming species can alter food webs, reduce ecosystem productivity, and lead to economic losses while causing lower aquacultural yields. After the 1990s, N. scintillans abundances have significantly increased by 1.65-fold and a significant prolongation of the bloom window was found (from 27.5 to 98 days in recent decades) off the island of Helgoland, Germany. Significant correlations were found between bloom initiation and nutrients, as well as light availability since these factors lead to increased prey availability. Highest abundances of N. scintillans were associated with water temperatures around 17 °C and wind speed below 6 ms-1 causing dense surface accumulations. Solar radiation of more than 200 Wm-2 was identified as a main driver for post-bloom conditions as it can deteriorate the cells and lead to the decline of N. scintillans abundances. In the southern North Sea, N. scintillans occurrences have intensified and spread since the 1980s with hotspots identified as the coastal waters adjacent to the estuaries of the Elbe and Rhine rivers.

Will climate change cause Sargassum beds in temperate waters to expand or contract? Evidence from the range shift pattern of Sargassum

Understanding the range shift patterns of foundation species (e.g., macroalgae) under future climatic conditions is critical for biodiversity conservation in coastal ecosystems. These predictions are typically made using species distribution models (SDMs), and severe habitat loss has been predicted for most brown algal forests. Nevertheless, some models showed that local adaptation within species can reduce range loss projections. In this study, we used the brown algae Sargassum fusiforme and Sargassum thunbergii, which are distributed in the Northwest Pacific, to determine whether climate change will cause the Sargassum beds in Northwest Pacific temperate waters to expand or contract. We divided S. fusiforme and S. thunbergii into northern and southern lineages, considering the temperature gradients and phylogeographic structures. We quantified the realized niches of the two lineages using an n-dimensional hypervolume. Significant niche differentiation was detected between lineages for both species, suggesting the existence of local adaptation. Based on these results, lineage-level SDMs were constructed for both species. The prediction results showed the different responses of different lineages to climate change. The suitable distribution area for both species was predicted to move northward, retaining part of the suitable habitat at low latitudes (along the East China Sea). Unfortunately, this expansion could not compensate for losing middle-low latitude areas. Our results have important implications for the future management and protection of macroalgae and emphasize the importance of incorporating intraspecific variation into species distribution predictions.

Distribution patterns of Phytoseiulus persimilis in response to climate change

BACKGROUND

The biological control agent Phytoseiulus persimilis is a commercialized specialist predator of two agricultural pest mite species Tetranychus urticae and Tetranychus evansi. Biocontrol of these pest species by P. persimilis has achieved success in biological control in some areas. However, the lack of precise information about the influence of global climate change on the worldwide distribution of this biocontrol agent hampers international efforts to manage pest mites with P. persimilis. With 276 occurrence records and 19 bioclimatic variables, this study investigated the potential global distribution of P. persimilis.

RESULTS

The results demonstrated that the Maximum Entropy (MaxEnt) model performed well, with the area under the curve being 0.956, indicating the high accuracy of this model. Two variables, the minimum temperature of the coldest month (Bio_6) and precipitation of the coldest quarter (Bio_19) were the most important environmental variables that influenced the distribution of P. persimilis, contributing more than 30% to the model, respectively. The suitable area currently occupies 21.67% of the world's land area, spanning latitudes between 60°S and 60°N. Under shared socio-economic pathway (SSP) 5-8.5 (high-carbon emissions), the low suitable area would increase by 1.31% until the 2050s.

CONCLUSION

This study successfully identified that south-eastern China, parts of countries in the Mediterranean coastal regions, including Libya, Algeria, Portugal, Spain, and France, are climatically favorable regions for P. persimilis, providing valuable information about the potential areas where it can be effectively exploited as biocontrol agents in classical biological control programs to manage pest spider mites environmentally friendly. © 2024 Society of Chemical Industry.

Temperature dependence of mosquitoes: Comparing mechanistic and machine learning approaches

Mosquito vectors of pathogens (e.g., Aedes, Anopheles, and Culex spp. which transmit dengue, Zika, chikungunya, West Nile, malaria, and others) are of increasing concern for global public health. These vectors are geographically shifting under climate and other anthropogenic changes. As small-bodied ectotherms, mosquitoes are strongly affected by temperature, which causes unimodal responses in mosquito life history traits (e.g., biting rate, adult mortality rate, mosquito development rate, and probability of egg-to-adult survival) that exhibit upper and lower thermal limits and intermediate thermal optima in laboratory studies. However, it remains unknown how mosquito thermal responses measured in laboratory experiments relate to the realized thermal responses of mosquitoes in the field. To address this gap, we leverage thousands of global mosquito occurrences and geospatial satellite data at high spatial resolution to construct machine-learning based species distribution models, from which vector thermal responses are estimated. We apply methods to restrict models to the relevant mosquito activity season and to conduct ecologically plausible spatial background sampling centered around ecoregions for comparison to mosquito occurrence records. We found that thermal minima estimated from laboratory studies were highly correlated with those from the species distributions (r = 0.87). The thermal optima were less strongly correlated (r = 0.69). For most species, we did not detect thermal maxima from their observed distributions so were unable to compare to laboratory-based estimates. The results suggest that laboratory studies have the potential to be highly transportable to predicting lower thermal limits and thermal optima of mosquitoes in the field. At the same time, lab-based models likely capture physiological limits on mosquito persistence at high temperatures that are not apparent from field-based observational studies but may critically determine mosquito responses to climate warming. Our results indicate that lab-based and field-based studies are highly complementary; performing the analyses in concert can help to more comprehensively understand vector response to climate change.

Multi-model assessment of potential natural vegetation to support ecological restoration

Ecological restoration is imperative for controlling desertification. Potential natural vegetation (PNV), the theoretical vegetation succession state, can guides near-natural restoration. Although a rising transition from traditional statistical methods to advanced machine learning and deep learning is observed in PNV simulation, a comprehensive comparison of their performance is still unexplored. Therefore, we overview the performance of PNV mapping in terms of 12 commonly used methods with varying spatial scales and sample sizes. Our findings indicate that the methodology should be carefully selected due to the variation in performance of different model types, with Area Under the Curve (AUC) values ranging from 0.65 to 0.95 for models with sample sizes up to 80% of the total sample size. Specifically, semi-supervised learning performs best with small sample sizes (i.e., 10 to 200), while Random Forest, XGBoost, and artificial neural networks perform better with large sample sizes (i.e., over 500). Further, the performance of all models tends to improve significantly as the sample size increases and the grain size of the crystals becomes smaller. Take the downstream Tarim River Basin, a hyper-arid region undergoing ecological restoration, as a case study. We showed that its potential restored areas were overestimated by 2-3 fold as the spatial scale became coarser, revealing the caution needed while planning restoration projects at coarse resolution. These findings enhance the application of PNV in the design of restoration programs to prevent desertification.

Climatic niche shifts and ecological sky-island dynamics in Mesoamerican montane birds

An ongoing challenge in evolutionary and ecological research focuses on testing biogeographic hypotheses for the understanding of both species' distributional patterns and of the factors influencing range limits. In this study, we described the climatic niches of Neotropical humid montane forest birds through the analysis of factors driving their evolution at inter- and intraspecific levels; and tested for differences among allopatric lineages within Aulacorhynchus, Chlorospingus, Cardellina, and Eupherusa. We employed ecological niche models (ENMs) along with an ordination approach with kernel smoothing to perform niche overlap analyses and test hypotheses of niche equivalence/similarity among lineages. In addition, we described the potential distributions of each lineage during the Late Pleistocene climate fluctuations, identifying historical range expansions, connectivity, and stability. Overall, we observed differences in environmental variables influencing climatic requirements and distributional patterns for our selected species. We detected the highest values of niche overlap mainly between Eupherusa and some Chlorospingus lineages. At both interspecific and intraspecific levels, sister lineages showed non-identical environmental niches. Our results offer weak support to a moist forest model, in which populations followed the expansion and contraction cycles of montane forests, leading to a lack of niche conservatism among lineages (they tend to occupy not identical climatic environments) throughout Mesoamerica. Therefore, historical climatic conditions may act as ecological barriers determining the distributional ranges of these species.

Should I stay or move? Quantifying landscape of fear to enhance environmental management of road networks in a highly transformed landscape

The development and expansion of road networks pose considerable threats to natural habitats and wildlife, fostering a landscape of fear. In addition to direct mortality caused by road collisions, road construction and maintenance often result in habitat fragmentation and loss, impeding animal movement and gene flow between populations. Mountain ungulates are already confined to fragmented habitat patches and roads can cause substantial disturbances to their critical ecological processes, such as dispersal and migration. In this study, we employed two key mountain ungulates, the wild goat (Capra aegagrus) and mouflon (Ovis gmelini), as functional models to examine how road networks impact the quantity and connectivity of natural habitats in southwestern Iran, where extensive road construction has led to significant landscape changes. We used the MaxEnt method to predict species distribution, the circuit theory to evaluate habitat connectivity, and the Spatial Road Disturbance Index (SPROADI) to assess road impacts. During the modeling process, we selected eleven important variables and employed a model parametrization strategy to identify the optimal configuration for the MaxEnt model. For SPROADI index we used three sub-indices, including traffic intensity, vicinity impact, and fragmentation grade. We then integrated the results of these analyses to identify areas with the most significant environmental impacts of roads on the coherency of the natural habitats. The findings indicate that suitable habitats for wild goats are widely distributed across the study area, while suitable habitats for mouflon are primarily concentrated in the northeastern region. Conservation gap analysis revealed that only 8% of wild goat habitats and 7% of mouflon habitats are covered by protected areas (PAs). The SPROADI map highlighted that 23% of the study area is negatively influenced by road networks. Moreover, 30.4% of highest-probability corridors for mouflon, and 25.7% for wild goat, were highly vulnerable to the impacts of roads. Our combined approach enabled us to quantitatively assess species-specific vulnerability to the impacts of heavy road networks. This study emphasizes the urgent need to address the negative effects of road networks on wildlife habitats and connectivity corridors. Our approach effectively identifies sensitive areas, which can help inform mitigation strategies and support more effective conservation planning in significantly transformed landscapes.

The accuracy of predicting maladaptation to new environments with genomic data

Rapid environmental change poses unprecedented challenges to species persistence. To understand the extent that continued change could have, genomic offset methods have been used to forecast maladaptation of natural populations to future environmental change. However, while their use has become increasingly common, little is known regarding their predictive performance across a wide array of realistic and challenging scenarios. Here, we evaluate the performance of currently available offset methods (gradientForest, the Risk-Of-Non-Adaptedness, redundancy analysis with and without structure correction and LFMM2) using an extensive set of simulated data sets that vary demography, adaptive architecture and the number and spatial patterns of adaptive environments. For each data set, we train models using either all, adaptive or neutral marker sets and evaluate performance using in silico common gardens by correlating known fitness with projected offset. Using over 4,849,600 of such evaluations, we find that (1) method performance is largely due to the degree of local adaptation across the metapopulation (LA), (2) adaptive marker sets provide minimal performance advantages, (3) performance within the species range is variable across gardens and declines when offset models are trained using additional non-adaptive environments and (4) despite (1) performance declines more rapidly in globally novel climates (i.e. a climate without an analogue within the species range) for metapopulations with greater LA than lesser LA. We discuss the implications of these results for management, assisted gene flow and assisted migration.

Predicting the Potential Global Distribution of the Plum Fruit Moth Grapholita funebrana Treitscheke Using Ensemble Models

The plum fruit moth, Grapholita funebrana Treitschke, is one of the most significant borer pests, often causing huge economic losses in fruit production. However, the potential distribution range of this economically important pest is still poorly understood. For this study, we simulated an ensemble species distribution model to predict the spatiotemporal distribution pattern of G. funebrana at a global scale. The results show that the suitable habitats for this moth, under current environmental conditions, are mainly distributed in Europe; East Asia, including China and Japan; Central Asia; and some parts of America. In future projections, the suitable habitats are predicted to generally expand northward, while the suitable area will remain unchanged overall. However, the area of highly suitable habitat will decrease to only 17.49% of that found under current conditions. None of the nine factors used were revealed to be predominant predictors in terms of contributing to the model, suggesting that the integrated effects of these variables shape G. funebrana's distribution. In this study, the distribution range that has been predicted, especially for the regions with a highly suitable habitat, poses a high risk of G. funebrana outbreaks, highlighting the urgency of pest management. Moreover, in the United States of America (USA) and Japan (for which G. funebrana distributions were not previously recorded), especially in areas highly suitable for this moth, monitoring and quarantine measures should be strengthened to prevent the colonization and further dispersal of this pest, as seen with its close relative G. molesta, which has become a cosmopolitan pest species, migrating from its native region (East Asia) to other continents, including the Americas.

Habitat Loss in the IUCN Extent: Climate Change-Induced Threat on the Red Goral (Naemorhedus baileyi) in the Temperate Mountains of South Asia

Climate change has severely impacted many species, causing rapid declines or extinctions within their essential ecological niches. This deterioration is expected to worsen, particularly in remote high-altitude regions like the Himalayas, which are home to diverse flora and fauna, including many mountainous ungulates. Unfortunately, many of these species lack adaptive strategies to cope with novel climatic conditions. The Red Goral (Naemorhedus baileyi) is a cliff-dwelling species classified as "Vulnerable" by the IUCN due to its small population and restricted range extent. This species has the most restricted range of all goral species, residing in the temperate mountains of northeastern India, northern Myanmar, and China. Given its restricted range and small population, this species is highly threatened by climate change and habitat disruptions, making habitat mapping and modeling crucial for effective conservation. This study employs an ensemble approach (BRT, GLM, MARS, and MaxEnt) in species distribution modeling to assess the distribution, habitat suitability, and connectivity of this species, addressing critical gaps in its understanding. The findings reveal deeply concerning trends, as the model identified only 21,363 km2 (13.01%) of the total IUCN extent as suitable habitat under current conditions. This limited extent is alarming, as it leaves the species with very little refuge to thrive. Furthermore, this situation is compounded by the fact that only around 22.29% of this identified suitable habitat falls within protected areas (PAs), further constraining the species' ability to survive in a protected landscape. The future projections paint even degraded scenarios, with a predicted decline of over 34% and excessive fragmentation in suitable habitat extent. In addition, the present study identifies precipitation seasonality and elevation as the primary contributing predictors to the distribution of this species. Furthermore, the study identifies nine designated transboundary PAs within the IUCN extent of the Red Goral and the connectivity among them to highlight the crucial role in supporting the species' survival over time. Moreover, the Dibang Wildlife Sanctuary (DWLS) and Hkakaborazi National Park are revealed as the PAs with the largest extent of suitable habitat in the present scenario. Furthermore, the highest mean connectivity was found between DWLS and Mehao Wildlife Sanctuary (0.0583), while the lowest connectivity was observed between Kamlang Wildlife Sanctuary and Namdapha National Park (0.0172). The study also suggests strategic management planning that is a vital foundation for future research and conservation initiatives, aiming to ensure the long-term survival of the species in its natural habitat.

Reassessment of French breeding bird population sizes using citizen science and accounting for species detectability

Higher efficiency in large-scale and long-term biodiversity monitoring can be obtained through the use of Essential Biodiversity Variables, among which species population sizes provide key data for conservation programs. Relevant estimations and assessment of actual population sizes are critical for species conservation, especially in the current context of global biodiversity erosion. However, knowledge on population size varies greatly, depending on species conservation status and ranges. While the most threatened or restricted-range species generally benefit from exhaustive counts and surveys, monitoring common and widespread species population size tends to be neglected or is simply more challenging to achieve. In such a context, citizen science (CS) is a powerful tool for the long-term monitoring of common species through the engagement of various volunteers, permitting data acquisition on the long term and over large spatial scales. Despite this substantially increased sampling effort, detectability issues imply that even common species may remain unnoticed at suitable sites. The use of structured CS schemes, including repeated visits, enables to model the detection process, permitting reliable inferences of population size estimates. Here, we relied on a large French structured CS scheme (EPOC-ODF) comprising 27,156 complete checklists over 3,873 sites collected during the 2021-2023 breeding seasons to estimate the population size of 63 common bird species using hierarchical distance sampling (HDS). These population size estimates were compared to the previous expert-based French breeding bird atlas estimations, which did not account for detectability issues. We found that population size estimates from the former French breeding bird atlas were lower than those estimated using HDS for 65% of species. Such a prevalence of lower estimations is likely due to more conservative estimates inferred from semi-quantitative expert-based assessments used for the previous atlas. We also found that species with long-range songs such as the Common Cuckoo (Cuculus canorus), Eurasian Hoopoe (Upupa epops) or the Eurasian Blackbird (Turdus merula) had, in contrast, higher estimated population sizes in the previous atlas than in our HDS models. Our study highlights the need to rely on sound statistical methodology to ensure reliable ecological inferences with adequate uncertainty estimation and advocates for a higher reliance on structured CS in support of long-term biodiversity monitoring.

Impact of climate change on Colombian Pacific coast mangrove bivalves distribution

The mangrove bivalves, Anadara tuberculosa and Anadara similis, are pivotal for the Colombian Pacific coast mangrove ecosystems and economies. In this study, the current and future potential distribution of these bivalves is modeled considering climate change. The future models (2030 and 2050) were projected considering the new climate scenarios (SSP1, SSP2, and SSP5) proposed by the IPCC in its sixth report. Our findings reveal areas in the Colombian Pacific coast, notably Nariño, Cauca, southern Valle del Cauca, and Chocó, with high environmental suitability for these bivalves. However, the 2050 projections, especially under the pessimistic SSP5 scenario, indicate potential adverse impacts from climate change. By 2030 and 2050, the species might lean more toward a southwesterly distribution in the Colombian Pacific coast. Climate-induced spatiotemporal mismatches could occur between the bivalves and the mangroves in some areas. These insights are crucial for effective conservation and management strategies for these species.

Physiologically-informed predictions of climate warming effects on native and non-native populations of blue catfish

Blue catfish Ictalurus furcatus has been widely introduced throughout the United States to enhance recreational fisheries. Its success in both its native and non-native range, especially in the context of climate change, will be influenced by its thermal performance. We conducted a laboratory experiment to investigate the responses of wild-captured, subadult blue catfish to temperatures ranging from 7 °C to 38 °C. Blue catfish had relatively low standard metabolic rates, indicating low energetic demands, and hence an ability to survive well even during low-food conditions. Metabolic scope and food consumption rate increased with temperature, with metabolic scope peaking at 29.1 °C, and consumption rate peaking at 32 °C. Body condition remained high up to 32 °C, but decreased drastically thereafter, suggesting limitations in maintaining metabolism through food consumption at temperatures >32 °C; blue catfish cannot survive in such habitats indefinitely. Yet, many fish were able to survive temperatures as high as 38 °C for 5 days, suggesting that acute and occasionally chronic heat waves will not limit this species. Using these results, we also predicted the performance of blue catfish under prevailing conditions and under climate warming at seven locations throughout their current range in the U.S. We found that some blue catfish populations in southern and southeastern areas will likely experience temperatures above the optimal temperature for extended periods due to climate change, thus limiting potential habitat availability for this species. But, many non-native populations, especially those in northern areas such as Idaho, North Dakota, and northern California, may benefit from the expected warmer temperatures during spring and fall.

Impact of carbon neutralization policy on the suitable habitat distribution of the North China leopard

The Chinese government has introduced a carbon neutral policy to cope with the rapid changes in the global climate. It is not clear what impact this policy will have on wildlife. Therefore, this study analyzed the suitable habitat distribution of China's unique leopard subspecies in northern Shaanxi, and simulated the potential suitable habitat distribution under different carbon emission scenarios at two time points of future carbon peak and carbon neutralization. We found that in the future SSPs 126 scenario, the suitable habitat area and the number of suitable habitat patches of North China leopard will continue to increase. With the increase of carbon emissions, it is expected that the suitable habitat of North China leopard will continue to be fragmented and shifted. When the annual average temperature is lower than 8 °C, the precipitation seasonality is 80-90 mm and the precipitation of the warmest quarter is greater than 260 mm, the probability of occurrence of North China leopard is higher. The increase in carbon emissions will lead to the reduction, migration, and fragmentation of the suitable habitat distribution of the North China leopard. Carbon neutrality policies can protect suitable wild habitats. In the future, the impact of carbon neutrality policies on future wildlife habitat protection should be carried out in depth to effectively promote the construction of wildlife protection projects.

Species distribution modeling for disease ecology: A multi-scale case study for schistosomiasis host snails in Brazil

Species distribution models (SDMs) are increasingly popular tools for profiling disease risk in ecology, particularly for infectious diseases of public health importance that include an obligate non-human host in their transmission cycle. SDMs can create high-resolution maps of host distribution across geographical scales, reflecting baseline risk of disease. However, as SDM computational methods have rapidly expanded, there are many outstanding methodological questions. Here we address key questions about SDM application, using schistosomiasis risk in Brazil as a case study. Schistosomiasis is transmitted to humans through contact with the free-living infectious stage of Schistosoma spp. parasites released from freshwater snails, the parasite's obligate intermediate hosts. In this study, we compared snail SDM performance across machine learning (ML) approaches (MaxEnt, Random Forest, and Boosted Regression Trees), geographic extents (national, regional, and state), types of presence data (expert-collected and publicly-available), and snail species (Biomphalaria glabrata, B. straminea, and B. tenagophila). We used high-resolution (1km) climate, hydrology, land-use/land-cover (LULC), and soil property data to describe the snails' ecological niche and evaluated models on multiple criteria. Although all ML approaches produced comparable spatially cross-validated performance metrics, their suitability maps showed major qualitative differences that required validation based on local expert knowledge. Additionally, our findings revealed varying importance of LULC and bioclimatic variables for different snail species at different spatial scales. Finally, we found that models using publicly-available data predicted snail distribution with comparable AUC values to models using expert-collected data. This work serves as an instructional guide to SDM methods that can be applied to a range of vector-borne and zoonotic diseases. In addition, it advances our understanding of the relevant environment and bioclimatic determinants of schistosomiasis risk in Brazil.

Estimating encounter-habitat relationships with scale-integrated resource selection functions

Encounters between animals occur when animals are close in space and time. Encounters are important in many ecological processes including sociality, predation and disease transmission. Despite this, there is little theory regarding the spatial distribution of encounters and no formal framework to relate environmental characteristics to encounters. The probability of encounter could be estimated with resource selection functions (RSFs) by comparing locations where encounters occurred to available locations where they may have occurred, but this estimate is complicated by the hierarchical nature of habitat selection. We developed a method to relate resources to the relative probability of encounter based on a scale-integrated habitat selection framework. This framework integrates habitat selection at multiple scales to obtain an appropriate estimate of availability for encounters. Using this approach, we related encounter probabilities to landscape resources. The RSFs describe habitat associations at four scales, home ranges within the study area, areas of overlap within home ranges, locations within areas of overlap, and encounters compared to other locations, which can be combined into a single scale-integrated RSF. We apply this method to intraspecific encounter data from two species: white-tailed deer (Odocoileus virginianus) and elk (Cervus elaphus) and interspecific encounter data from a two-species system of caribou (Rangifer tarandus) and coyote (Canis latrans). Our method produced scale-integrated RSFs that represented the relative probability of encounter. The predicted spatial distribution of encounters obtained based on this scale-integrated approach produced distributions that more accurately predicted novel encounters than a naïve approach or any individual scale alone. Our results highlight the importance of accounting for the conditional nature of habitat selection in estimating the habitat associations of animal encounters as opposed to 'naïve' comparisons of encounter locations with general availability. This method has direct relevance for testing hypotheses about the relationship between habitat and social or predator-prey behaviour and generating spatial predictions of encounters. Such spatial predictions may be vital for understanding the distribution of encounters driving disease transmission, predation rates and other population and community-level processes.

Fragile futures: Evaluating habitat and climate change response of hog badgers (Mustelidae: Arctonyx) in the conservation landscape of mainland Asia

The small mammalian fauna plays pivotal roles in ecosystem dynamics and as crucial biodiversity indicators. However, recent research has raised concerns about the decline of mammalian species due to climate change. Consequently, significant attention is directed toward studying various big flagship mammalian species for conservation. However, small mammals such as the hog badgers (Mustelidae: Arctonyx) remain understudied regarding the impacts of climate change in Asia. The present study offers a comprehensive analysis of climate change effects on two mainland hog badger species, utilizing ensemble species distribution modeling. Findings reveal concerning outcomes, as only 52% of the IUCN extent is deemed suitable for the Great Hog Badger (Arctonyx collaris) and a mere 17% is ideal for the Northern Hog Badger (Arctonyx albogularis). Notably, projections suggest a potential reduction of over 26% in suitable areas for both species under future climate scenarios, with the most severe decline anticipated in the high-emission scenario of SSP585. These declines translate into evident habitat fragmentation, particularly impacting A. collaris, whose patches shrink substantially, contrasting with the relatively stable patches of A. albogularis. However, despite their differences, niche overlap analysis reveals an intriguing increase in overlap between the two species, indicating potential ecological shifts. The study underscores the importance of integrating climate change and habitat fragmentation considerations into conservation strategies, urging a reassessment of the IUCN status of A. albogularis. The insights gained from this research are crucial for improving protection measures by ensuring adequate legal safeguards and maintaining ecological corridors between viable habitat patches, which are essential for the conservation of hog badgers across mainland Asia. Furthermore, emphasizing the urgency of proactive efforts, particularly in countries with suitable habitats can help safeguard these small mammalian species and their ecosystems from the detrimental impacts of climate change.

Modeling the current and future habitat suitability of Neltuma pallida in the dry forest of northern Peru under climate change scenarios to 2100

The development of anthropic activities and climate change effects impact worldwide species' ecosystems and habitats. Habitats' adequate prediction can be an important tool to assess current and future trends. In addition, it allows strategies development for their conservation. The Neltuma pallida of the forest region in northern Peru, although very significant, has experienced a decline in recent years. The objective of this research is to evaluate the current and future distribution and conservation status of N. pallida in the Peruvian dry forest under climate change (Location: Republic of Peru). A total of 132 forest presence records and 10 variables (bioclimatic, topographic, and soil) were processed and selected to obtain the current and future distribution for 2100, using Google Earth Engine (GEE), RStudio, and MaxEnt. The area under the curve values fell within the range of 0.93-0.95, demonstrating a strong predictive capability for both present and future potential habitats. The findings indicated that the likely range of habitats for N. pallida was shaped by factors such as the average temperature of wettest quarter, maximum temperature of warmest month, elevation, rainfall, and precipitation of driest month. The main suitable areas were in the central regions of the geographical departments of Tumbes, Piura, and Lambayeque, as well as in the northern part of La Libertad. It is critical to determine the habitat suitability of plant species for conservation managers since this information stimulates the development of policies that favor sustainable use programs. In addition, these results can contribute significantly to identify new areas for designing strategies for populations conserving and recovering with an ecological restoration approach.

Reconstructing 120 years of climate change impacts on Joshua tree flowering

Quantifying how global change impacts wild populations remains challenging, especially for species poorly represented by systematic datasets. Here, we infer climate change effects on masting by Joshua trees (Yucca brevifolia and Y. jaegeriana), keystone perennials of the Mojave Desert, from 15 years of crowdsourced observations. We annotated phenophase in 10,212 geo-referenced images of Joshua trees on the iNaturalist crowdsourcing platform, and used them to train machine learning models predicting flowering from annual weather records. Hindcasting to 1900 with a trained model successfully recovers flowering events in independent historical records and reveals a slightly rising frequency of conditions supporting flowering since the early 20th Century. This reflects increased variation in annual precipitation, which drives masting events in wet years-but also increasing temperatures and drought stress, which may have net negative impacts on recruitment. Our findings reaffirm the value of crowdsourcing for understanding climate change impacts on biodiversity.

Exploring the distribution and habitat preferences of Polytrichaceae (Bryophyta) in Tibet, China

The Qinghai-Tibet Plateau stands as one of the most ecologically fragile and biodiversity-rich regions globally. Understanding the distribution of different taxa and their relationship with environmental factors is crucial for effective conservation and sustainable management. Polytrichaceae, a significant bryophyte family widely distributed in Tibet, displays distinct structural, morphological, and phylogenetic traits compared to other mosses. Despite its importance, the distribution of Polytrichaceae in Tibet and its correlation with environmental factors have yet to be explored. In this study, we used an optimized Maximum Entropy (MaxEnt) model to explore the potential suitable habitats of Polytrichaceae in Tibet, aiming to clarify their geographic distribution pattern as well as the key environmental influence factors. The model had high accuracy with an average Area Under the Curve (AUC) of 0.933 and True Skill Statistics (TSS) value of 0.789. The results showed that the potential suitability habitats of Polytrichaceae were mainly located in southeastern Tibet, and the low suitable, moderately suitable, and highly suitable habitats accounted for 12.53 %, 6.84 %, and 3.31 % of the total area of Tibet respectively. Unsuitable habitats were mainly located in northwestern Tibet, accounting for about 77.32 %. In Tibet, temperature factors (Mean Temperature of Coldest Quarter (Bio11) and Annual Mean Temperature (Bio1)) played a pivotal role in determining the potential suitable habitats for Polytrichaceae, and elevation, precipitation, and vegetation coverage also had an important influence. The family preferred warm, moist and densely vegetated habitats in Tibet. This study enriched our ecological understanding of bryophyte ecology in this region and provided data-driven support for biodiversity conservation and ecosystem management in Tibet.

Genetic Diversity Analysis and Prediction of Potential Suitable Areas for the Rare and Endangered Wild Plant Henckelia longisepala

Henckelia longisepala (H. W. Li) D. J. Middleton & Mich. Möller is a rare and endangered plant species found only in Southeastern Yunnan, China, and Northern Vietnam. It is listed as a threatened species in China and recognized as a plant species with extremely small populations (PSESP), while also having high ornamental value and utilization potential. This study used ddRAD-seq technology to quantify genetic diversity and structure for 32 samples from three extant populations of H. longisepala. The H. longisepala populations were found to have low levels of genetic diversity (Ho = 0.1216, He = 0.1302, Pi = 0.1731, FIS = 0.1456), with greater genetic differentiation observed among populations (FST = 0.3225). As indicated by genetic structure and phylogenetic analyses, samples clustered into three distinct genetic groups that corresponded to geographically separate populations. MaxEnt modeling was used to identify suitable areas for H. longisepala across three time periods and two climate scenarios (SSP1-2.6, SSP5-8.5). High-suitability areas were identified in Southeastern Yunnan Province, Northern Vietnam, and Eastern Laos. Future H. longisepala distribution was predicted to remain centered in these areas, but with a decrease in the total amount of suitable habitat. The present study provides key data on H. longisepala genetic diversity, as well as a theoretical basis for the conservation, development, and utilization of its germplasm resources.

Patterns and Drivers of Bumblebee Diversity in Gansu

Understanding the influence of factors responsible for shaping community assemblage is crucial for biodiversity management and conservation. Gansu is one of the richest regions for bumblebee species in the world. We explored the distribution data of 52 bumblebee species collected in Gansu and its surroundings between 2002 and 2022, predicting habitat suitability based on 17 environmental variables using MaxEnt. The factors influencing community assemblage were assessed using canonical correspondence analysis. Net primary productivity, water vapor pressure, temperature seasonality, annual precipitation, and precipitation seasonality were some of the most influential drivers of species distributions. Based on Ward's agglomerative cluster analysis, four biogeographic zones are described: the Southern humid zone, the Western Qilian snow mountain zone, the Eastern Loess plateau zone, and the Western dry mountain zone. In the clusters of grid cells based on beta diversity values, the Southern humid zone comprised 42.5% of the grid cells, followed by the Eastern Loess plateau zone (32.5%), the Western dry mountain zone (20%), and the Western Qilian snow mountain zone (5%). Almost all the environmental factors showed a significant contribution to the assemblages of bumblebees of different groups. Our findings highlight the need for better data to understand species biogeography and diversity patterns, and they provide key baseline data for refining conservation strategies.

Bioindicator aquatic insects at risk from climate change in a biodiversity hotspot

Climate change can affect biological assemblages by shifting their species' geographic range and changing species richness. Aquatic insects represent more than half of the freshwater animal species but have been neglected mainly in climate change assessments, particularly in tropical ecosystems. Among the aquatic insect taxa, Ephemeroptera, Plecoptera, and Trichoptera (EPT) are well-known bioindicators of environmental changes and encompass an essential metric for rivers and streams' biomonitoring. Here, we use ecological niche models to project the impact of climate change on the distribution range and richness of EPT in the Atlantic Forest biodiversity hotspot. We found EPT to be at high risk from future climate change, with Plecoptera as the order of greatest concern. We projected range contraction of ca. 90 % of the analyzed EPT genera, resulting in a reduction in the richness of EPT genera under future climatic conditions. We projected >50 % contraction in the distribution of 50 % of Plecoptera, ≈14 % of Trichoptera, and ≈7 % of Ephemeroptera genera. The remaining climatically suitable regions in the Atlantic Forest are concentrated in the high-altitude areas, which may act as climate refuges for EPT biodiversity in the future. The projected changes in EPT's distribution range and richness may impact biomonitoring programs conducted in tropical ecosystems. Restricting EPT's geographic distribution may undermine its potential as a bioindicator and influence the composition of EPT assemblages at reference sites, which may lead to shifting baseline conditions. We reinforce the importance of considering future climatic conditions when planning long-term biomonitoring and priority areas for conservation.

Bluetongue Risk Map for Vaccination and Surveillance Strategies in India

Bluetongue virus (BTV, Sedoreoviridae: Orbivirus) causes an economically important disease, namely, bluetongue (BT), in domestic and wild ruminants worldwide. BTV is endemic to South India and has occurred with varying severity every year since the virus was first reported in 1963. BT can cause high morbidity and mortality to sheep flocks in this region, resulting in serious economic losses to subsistence farmers, with impacts on food security. The epidemiology of BTV in South India is complex, characterized by an unusually wide diversity of susceptible ruminant hosts, multiple vector species biting midges (Culicoides spp., Diptera: Ceratopogonidae), which have been implicated in the transmission of BTV and numerous co-circulating virus serotypes and strains. BT presence data (1997-2011) for South India were obtained from multiple sources to develop a presence/absence model for the disease. A non-linear discriminant analysis (NLDA) was carried out using temporal Fourier transformed variables that were remotely sensed as potential predictors of BT distribution. Predictive performance was then characterized using a range of different accuracy statistics (sensitivity, specificity, and Kappa). The top ten variables selected to explain BT distribution were primarily thermal metrics (land surface temperature, i.e., LST, and middle infrared, i.e., MIR) and a measure of plant photosynthetic activity (the Normalized Difference Vegetation Index, i.e., NDVI). A model that used pseudo-absence points, with three presence and absence clusters each, outperformed the model that used only the recorded absence points and showed high correspondence with past BTV outbreaks. The resulting risk maps may be suitable for informing disease managers concerned with vaccination, prevention, and control of BT in high-risk areas and for planning future state-wide vector and virus surveillance activities.

Fish and tips: Historical and projected changes in commercial fish species' habitat suitability in the Southern Hemisphere

Global warming has significantly altered fish distribution patterns in the ocean, shifting towards higher latitudes and deeper waters. This is particularly relevant in high-latitude marine ecosystems, where climate-driven environmental changes are occurring at higher rates than the global average. Species Distribution Models (SDMs) are increasingly being used for predicting distributional shifts in habitat suitability for marine species as a response to climate change. Here, we used SDMs to project habitat suitability changes for a range of high-latitude, pelagic and benthopelagic commercial fish species and crustaceans (10 species); from 1850 to two future climate change scenarios (SSP1-2.6: low climate forcing; and SSP5-8.5: high climate forcing). The study includes 11 Large Marine Ecosystems (LME) spanning South America, Southern Africa, Australia, and New Zealand. We identified declining and southward-shifting patterns in suitable habitat areas for most species, particularly under the SSP5-8.5 scenario and for some species such as Argentine hake (Merluccius hubbsi) in South America, or snoek (Thyrsites atun) off Southern Africa. Geographical constraints will likely result in species from Southern Africa, Australia, and New Zealand facing the most pronounced habitat losses due to rising sea surface temperatures (SST). In contrast, South American species might encounter greater opportunities for migrating southward. Additionally, the SSP5-8.5 scenario predicts that South America will be more environmentally stable compared to other regions. Overall, our findings suggest that the Patagonian shelf could serve as a climate refuge, due to higher environmental stability highlighting the importance of proactive management strategies in this area for species conservation. This study significantly contributes to fisheries and conservation management, providing valuable insights for future protection efforts in the Southern Hemisphere.

Predicting the impact of climate change and land use change on the potential distribution of two economic forest trees in Northeastern China

Young shoots of Aralia elata and young leaves of Eleutherococcus senticosus are two major non-timber forest products in northeastern China. However, human activities and climate change have resulted in serious threats to the habitats of two trees, which greatly limits resource conservation and exploitation of economic forest trees. We used the MaxEnt model to predict the suitable habitats of the two economic trees and analyzed the dominant factors affecting their distribution. The results showed that the suitable habitat areas of A. elata and E. senticosus in the current period were 159950 km2 and 123449 km2, respectively, and the suitable habitats of both economic forest trees were located in the eastern part of the northeast region. Climate factors (Annual precipitation, Precipitation Seasonality) and land use factors are important variables influencing changes in suitable habitat for both trees. With the change of climate and land use in the future, the overall trend of suitable habitat for both economic forest trees shows a northward and then a southward migration. These results may provide assistance in developing strategies for resource conservation and sustainable use of A. elata and E. senticosus, and we suggest that stable and suitable habitats should be selected as areas for in situ conservation and breeding of the two economic forest trees.

Refining Ixodes scapularis (Acari: Ixodidae) distribution models: a comparison of current methods to an established protocol

Blacklegged ticks (Ixodes scapularis Say) pose an enormous public health risk in eastern North America as the vector responsible for transmitting 7 human pathogens, including those causing the most common vector-borne disease in the United States, Lyme disease. Species distribution modeling is an increasingly popular method for predicting the potential distribution and subsequent risk of blacklegged ticks, however, the development of such models thus far is highly variable and would benefit from the use of standardized protocols. To identify where standardized protocols would most benefit current distribution models, we completed the "Overview, Data, Model, Assessment, and Prediction" (ODMAP) distribution modeling protocol for 21 publications reporting 22 blacklegged tick distribution models. We calculated an average adherence of 73.4% (SD ± 29%). Most prominently, we found that authors could better justify and connect their selection of variables and associated spatial scales to blacklegged tick ecology. In addition, the authors could provide clearer descriptions of model development, including checks for multicollinearity, spatial autocorrelation, and plausibility. Finally, authors could improve their reporting of variable effects to avoid undermining the models' utility in informing species-environment relationships. To enhance future model rigor and reproducibility, we recommend utilizing several resources including the ODMAP protocol, and suggest that journals make protocol compliance a publication prerequisite.

Harmful Ostreopsis cf. ovata blooms could extend in time span with climate change in the Western Mediterranean Sea

Fast environmental changes and high coastal human pressures and impacts threaten the Mediterranean Sea. Over the last decade, recurrent blooms of the harmful dinoflagellate Ostreopsis cf. ovata have been recorded in many Mediterranean beaches. These microalgae produce toxins that affect marine organisms and human health. Understanding the environmental conditions that influence the appearance and magnitude of O. cf. ovata blooms, as well as how climate change will modify its future distribution and dynamics, is crucial for predicting and managing their effects. This study investigates whether the spatio-temporal distribution of this microalga and the frequency of its blooms could be altered in future climate change scenarios in the Mediterranean Western basin. For the first time, an ecological habitat model (EHM) is forced by physico-chemical climate change simulations at high-resolution, under the strong greenhouse gas emission trajectory (RCP8.5). It allows to characterize how O. cf. ovata may respond to projected conditions and how its distribution could shift over a wide spatial scale, in this plausible future. Before being applied to the EHM, future climate simulations are further refined by using a statistical adaptation method (Cumulative Distribution Function transform) to improve the predictions robustness. Temperature (optimum 23-26 °C), high salinity (>38 psu) and high inorganic nutrient concentrations (nitrate >0.25 mmol N·m-3 and phosphate >0.035 mmol P·m-3) drive O. cf. ovata abundances. High spatial disparities in future abundances are observed. Namely, O. cf. ovata abundances could increase on the Mediterranean coasts of France, Spain and the Adriatic Sea while a decrease is expected in the Tyrrhenian Sea. The bloom period could be extended, starting earlier and continuing later in the year. From a methodological point of view, this study highlights best practices of EHMs in the context of climate change to identify sensitive areas for current and future harmful algal blooms.

Climate change can disproportionately reduce habitats of stream fishes with restricted ranges in southern South America

Freshwater fishes are among the most threatened taxa worldwide owing to changes in land use, species introductions, and climate change. Although more than half of the freshwater fishes in the Chilean Mediterranean ecoregion are considered vulnerable or endangered, still little is known about their biogeography. Fishes of the family Perciliidae are endemic of this region and ideal cases to study potential implications of global warming given their endangered conservation status, small size, restricted range, and limited dispersal capacity in fragmented habitats. Here, we model the spatial distribution of habitats for Percilia irwini and P. gillissi under current (1970-2000) and future (2050-2080) climatic scenarios (SSP245, SSP585). We implement maximum entropy (MaxEnt) models adapted for stream networks using high-resolution datasets of selected geophysical and climatic variables. At present, both species inhabit relatively low-quality habitats. In the future (SSP585), suitable habitats for P. irwini are predicted to be reduced drastically (99%) with potential local extirpations in its northern range. Similarly, up to 62% of suitable habitats for P. gillissi would also be reduced in the future. Our study provides insights about assessing future threats and vulnerability of endemic, endangered, range-restricted, and small-bodied freshwater species in this region and elsewhere.

Trees have overlapping potential niches that extend beyond their realized niches

Tree species appear to prefer distinct climatic conditions, but the true nature of these preferences is obscured by species interactions and dispersal, which limit species' ranges. We quantified realized and potential thermal niches of 188 North American tree species to conduct a continental-scale test of the architecture of niches. We found strong and consistent evidence that species occurring at thermal extremes occupy less than three-quarters of their potential niches, and species' potential niches overlap at a mean annual temperature of ~12°C. These results clarify the breadth of thermal tolerances of temperate tree species and support the centrifugal organization of thermal niches. Accounting for the nonrealized components of ecological niches will advance theory and prediction in global change ecology.

Phenotypic plasticity rather than ecotypic differentiation explains the broad realized niche of a Neotropical orchid species

Local adaptation is common in plant species, and knowing whether a population is locally adapted has fundamental and applied relevance. However, local adaptation in tropical plants remains largely less studied, and covering this gap is not simple since reciprocal transplantation - the gold standard for detecting local adaptation - is not feasible for most species. Here, we combined genetic, climatic and phenotypic data to investigate ecotypic differentiation, an important aspect of local adaptation, in coastal and inland populations of the orchid Epidendrum fulgens Brongn., a long-lived tropical plant for which reciprocal transplantation would not be feasible. We used nine microsatellite markers to estimate genetic divergence between inland and coastal populations. Moreover, occurrence data and climate data were used to test for differences in the realized niche of those populations. Finally, we assessed saturated water content, leaf specific area, height, and stomatal density in common garden and in situ to investigate the effects of ecotypic differentiation and plasticity on the phenotype. Coastal and inland groups' niches do not overlap, the former occupying a wetter and warmer area. However, this differentiation does not seem to be driven by ecotypic differentiation since there was no positive correlation between genetic structure and climate dissimilarity. Moreover, specific leaf area and leaf saturated water content, which are important phenotypic traits related to soil fertility and drought stress, were rather plastic. We conclude that ecotypic differentiation is absent, since phenotypic plasticity is an important mechanism explaining the niche broadness of this species.

Gaps and opportunities in modelling human influence on species distributions in the Anthropocene

Understanding species distributions is a global priority for mitigating environmental pressures from human activities. Ample studies have identified key environmental (climate and habitat) predictors and the spatial scales at which they influence species distributions. However, regarding human influence, such understandings are largely lacking. Here, to advance knowledge concerning human influence on species distributions, we systematically reviewed species distribution modelling (SDM) articles and assessed current modelling efforts. We searched 12,854 articles and found only 1,429 articles using human predictors within SDMs. Collectively, these studies of >58,000 species used 2,307 unique human predictors, suggesting that in contrast to environmental predictors, there is no 'rule of thumb' for human predictor selection in SDMs. The number of human predictors used across studies also varied (usually one to four per study). Moreover, nearly half the articles projecting to future climates held human predictors constant over time, risking false optimism about the effects of human activities compared with climate change. Advances in using human predictors in SDMs are paramount for accurately informing and advancing policy, conservation, management and ecology. We show considerable gaps in including human predictors to understand current and future species distributions in the Anthropocene, opening opportunities for new inquiries. We pose 15 questions to advance ecological theory, methods and real-world applications.

Global climate change-driven impacts on the Asian distribution of Limassolla leafhoppers, with implications for biological and environmental conservation

Knowing the impacts of global climate change on the habitat suitability distribution of Limassolla leafhoppers contributes to understanding the feedback of organisms on climate change from a macroecological perspective, and provides important scientific basis for protecting the ecological environment and biodiversity. However, there is limited knowledge on this aspect. Thus, our study aimed to address this gap by analyzing Asian habitat suitability and centroid shifts of Limassolla based on 19 bioclimatic variables and occurrence records. Selecting five ecological niche models with the outstanding predictive performance (Maxlike, generalized linear model, generalized additive model, random forest, and maximum entropy) along with their ensemble model from 12 models, the current habitat suitability of Limassolla and its future habitat suitability under two Shared Socio-economic Pathways (SSP1-2.6 and SSP5-8.5) in the 2050s and 2090s were predicted. The results showed that the prediction results of the five models are generally consistent. Based on ensemble model, 11 potential biodiversity hotspots with high suitability were identified. With climate change, the suitable range of Limassolla will experience both expansion and contraction. In SSP5-8.52050s, the expansion area is 118.56 × 104 km2, while the contraction area is 25.40 × 104 km2; in SSP1-2.62090s, the expansion area is 91.71 × 104 km2, and the contraction area is 26.54 × 104 km2. Furthermore, the distribution core of Limassolla will shift toward higher latitudes in the northeast direction, and the precipitation of warmest quarter was found to have the greatest impact on the distribution of Limassolla. Our research results supported our four hypotheses. Finally, this research suggests establishing ecological reserves in identified contraction to prevent habitat loss, enhancing the protection of biodiversity hotspots, and pursuing a sustainable development path with reduced emissions.

Mangrove Health Index, Community Structure and Canopy Cover in Small Islands of Bunaken National Park, Indonesia: Insights into Dominant Mangrove Species and Overall Mangrove Condition

Mangrove ecosystems are crucial for protecting littoral regions, preserving biodiversity and sequestering carbon. The implementation of effective conservation and management strategies requires a comprehensive understanding of mangrove community structure, canopy coverage and overall health. This investigation focused on four small islands located within the Bunaken National Park in Indonesia: Bunaken, Manado Tua, Mantehage and Nain. Utilising the line transect quadrant method and hemispherical photography, the investigation comprised a total of 12 observation stations. Nain had the greatest average canopy coverage at 76.09%, followed by Mantehage, Manado Tua and Bunaken at 75.82%, 71.83% and 70.01%, respectively. Mantehage had the maximum species density, with 770.83 ind/ha, followed by Bunaken, Nain and Manado Tua with 675 ind/ha, 616.67 ind/ha and 483.34 ind/ha, respectively. The predominant sediment type observed was sandy mud and the mangrove species identified were Avicennia officinalis (AO), Bruguiera gymnorrhiza (BG), Rhizophora apiculata (RA), R. mucronata (RM), and Sonneratia alba (SA). On the small islands, S. alba emerged as the dominant mangrove species based on the importance value index (IVI). In addition, the Mangrove Health Index revealed that only 6.79% of the region exhibited poor health values, while 50% of the region was categorised as being in outstanding condition. These findings indicate that the overall condition of mangroves on these islands was relatively favourable.