Potential distribution of Biscogniauxia mediterranea and Obolarina persica causal agents of oak charcoal disease in Iran's Zagros forests.

In Iran, native oak species are under threat from episodes of Charcoal Disease, a decline syndrome driven by abiotic stressors (e.g. drought, elevated temperature) and biotic components, Biscogniauxia mediterranea (De Not.) Kuntze and Obolarina persica (M. Mirabolfathy). The outbreak is still ongoing and the country's largest ever recorded. Still, the factors driving its' epidemiology in time and space are poorly known and such knowledge is urgently needed to develop strategies to counteract the adverse effects. In this study, we developed a generic framework based on experimental, machine-learning algorithms and spatial analyses for landscape-level prediction of oak charcoal disease outbreaks. Extensive field surveys were conducted during 2013-2015 in eight provinces (more than 50 unique counties) in the Zagros ecoregion. Pathogenic fungi were isolated and characterized through morphological and molecular approaches, and their pathogenicity was assessed under controlled water stress regimes in the greenhouse. Further, we evaluated a set of 29 bioclimatic, environmental, and host layers in modeling for disease incidence data using four well-known machine learning algorithms including the Generalized Linear Model, Gradient Boosting Model, Random Forest model (RF), and Multivariate Adaptive Regression Splines implemented in MaxEnt software. Model validation statistics [Area Under the Curve (AUC), True Skill Statistics (TSS)], and Kappa index were used to evaluate the accuracy of each model. Models with a TSS above 0.65 were used to prepare an ensemble model. The results showed that among the different climate variables, precipitation and temperature (Bio18, Bio7, Bio8, and bio9) in the case of O. persica and similarly, gsl (growing season length TREELIM, highlighting the warming climate and the endophytic/pathogenic nature of the fungus) and precipitation in case of B. mediterranea are the most important influencing variables in disease modeling, while near-surface wind speed (sfcwind) is the least important variant. The RF algorithm generates the most robust predictions (ROC of 0.95; TSS of 0.77 and 0.79 for MP and OP, respectively). Theoretical analysis shows that the ensemble model (ROC of 0.95 and 0.96; TSS = 0.79 and 0.81 for MP and OP, respectively), can efficiently be used in the prediction of the charcoal disease spatiotemporal distribution. The oak mortality varied ranging from 2 to 14%. Wood-boring beetles association with diseased trees was determined at 20%. Results showed that water deficiency is a crucial component of the oak decline phenomenon in Iran. The Northern Zagros forests (Ilam, Lorestan, and Kermanshah provinces) along with the southern Zagros forests (Fars and Kohgilouyeh va-Boyer Ahmad provinces) among others are the most endangered areas of potential future pandemics of charcoal disease. Our findings will significantly improve our understanding of the current situation of the disease to pave the way against pathogenic agents in Iran.

Spatial habitat suitability prediction of essential oil wild plants on Indonesia's degraded lands.

Background: Essential oils are natural products of aromatic plants with numerous uses. Essential oils have been traded worldwide and utilized in various industries. Indonesia is the sixth largest essential oil producing country, but land degradation is a risk to the continuing extraction and utilization of natural products. Production of essential oil plants on degraded lands is a potential strategy to mitigate this risk. This study aimed to identify degraded lands in Indonesia that could be suitable habitats for five wild native essential oil producing plants, namely Acronychia pedunculata (L.) Miq., Baeckea frutescens L., Cynometra cauliflora L., Magnolia montana (Blume) Figlar, and Magnolia sumatrana var. glauca (Blume) Figlar & Noot using various species distribution models. Methods: The habitat suitability of these species was predicted by comparing ten species distribution models, including Bioclim, classification and regression trees (CART), flexible discriminant analysis (FDA), Maxlike, boosted regression trees (BRT), multivariate adaptive regression splines (MARS), generalized linear models (GLM), Ranger, support vector machine (SVM), and Random Forests (RF). Bioclimatic, topographic and soil variables were used as the predictors of the model habitat suitability. The models were evaluated according to their AUC and TSS metrics. Model selection was based on ranking performance. The total suitable area for five native essential oil producing plants in Indonesia's degraded lands was derived by overlaying the models with degraded land locations. Results: The habitat suitability model for these species was well predicted with an AUC value >0.8 and a TSS value >0.7. The most important predictor variables affecting the habitat suitability of these species are mean temperature of wettest quarter, precipitation seasonality, precipitation of warmest quarter, precipitation of coldest quarter, cation exchange capacity, nitrogen, sand, and soil organic carbon. C. cauliflora has the largest predicted suitable area, followed by M. montana, B. frutescens, M. sumatrana var. glauca, and A. pedunculata. The overlapping area between predictive habitat suitability and degraded lands indicates that the majority of degraded lands in Indonesia's forest areas are suitable for those species. Conclusion: The degraded lands predicted as suitable habitats for five native essential oil producing plants were widely spread throughout Indonesia, mostly in its main islands. These findings can be used by the Indonesian Government for evaluating policies for degraded land utilization and restorations that can enhance the lands' productivity.

The influence of climate change on the future distribution of two Thymus species in Iran: MaxEnt model-based prediction.

Within a few decades, the species habitat was reshaped at an alarming rate followed by climate change, leading to mass extinction, especially for sensitive species. Species distribution models (SDMs), which estimate both present and future species distribution, have been extensively developed to investigate the impacts of climate change on species distribution and assess habitat suitability. In the West Asia essential oils of T. daenensis and T. kotschyanus include high amounts of thymol and carvacrol and are commonly used as herbal tea, spice, flavoring agents and medicinal plants. Therefore, this study aimed to model these Thymus species in Iran using the MaxEnt model under two representative concentration pathways (RCP 4.5 and RCP 8.5) for the years 2050 and 2070. The findings revealed that the mean temperature of the warmest quarter (bio10) was the most significant variable affecting the distribution of T. daenensis. In the case of T. kotschyanus, slope percentage was the primary influencing factor. The MaxEnt modeling also demonstrated excellent performance, as indicated by all the Area Under the Curve (AUC) values exceeding 0.9. Moreover, based on the projections, the two mentioned species are expected to undergo negative area changes in the coming years. These results can serve as a valuable achievement for developing adaptive management strategies aimed at enhancing protection and sustainable utilization in the context of global climate change.

Prediction of Potential Suitable Areas and Priority Protection for Cupressus gigantea on the Tibetan Plateau.

Cupressus gigantea (C. gigantea) is an endemic endangered species on the Tibetan Plateau; its potential suitable areas and priority protection in the context of global climate change remain poorly predicted. This study utilized Biomod2 and Marxan to assess the potential suitable areas and priority protection for C. gigantea. Our study revealed that the suitable areas of C. gigantea were concentrated in the southeastern Tibetan Plateau, with the center in Lang County. Temperature was identified as a crucial environmental factor influencing the distribution of C. gigantea. Over the coming decades, the suitable range of C. gigantea expanded modestly, while its overall distribution remained relatively stable. Moreover, the center of the highly suitable areas tended to migrate towards Milin County in the northeast. Presently, significant areas for improvement are needed to establish protected areas for C. gigantea. The most feasible priority protected areas were located between the Lang and Milin counties in Tibet, which have more concentrated and undisturbed habitats. These results provide scientific guidance for the conservation and planning of C. gigantea, contributing to the stability and sustainability of ecosystems.

Effects of Climate Change on the Distribution of Prosthechea mariae (Orchidaceae) and within Protected Areas in Mexico.

The impact of climate change on the distribution of native species in the Neotropics remains uncertain for most species. Prosthechea mariae is an endemic epiphytic orchid in Mexico, categorized as threatened. The objective of this study was to assess the effect of climate change on the natural distribution of P. mariae and the capacity of protected areas (PAs) to safeguard optimal environmental conditions for the species in the future. Historical records were obtained from herbaria collections and through field surveys. We utilized climate variables from WorldClim for the baseline scenario and for the 2050 period, using the general circulation models CCSM4 and CNRM-CM5 (RCP 4.5). Three sets of climate data were created for the distribution models, and multiple models were evaluated using the kuenm package. We found that the species is restricted to the eastern region of the country. The projections of future scenarios predict not only a substantial reduction in habitat but also an increase in habitat fragmentation. Ten PAs were found within the current distribution area of the species; in the future, the species could lose between 36% and 48% of its available habitat within these PAs. The results allowed for the identification of locations where climate change will have the most severe effects, and proposals for long-term conservation are addressed.

Potential sources of time lags in calibrating species distribution models.

The Anthropocene is characterized by a rapid pace of environmental change and is causing a multitude of biotic responses, including those that affect the spatial distribution of species. Lagged responses are frequent and species distributions and assemblages are consequently pushed into a disequilibrium state. How the characteristics of environmental change-for example, gradual 'press' disturbances such as rising temperatures due to climate change versus infrequent 'pulse' disturbances such as extreme events-affect the magnitude of responses and the relaxation times of biota has been insufficiently explored. It is also not well understood how widely used approaches to assess or project the responses of species to changing environmental conditions can deal with time lags. It, therefore, remains unclear to what extent time lags in species distributions are accounted for in biodiversity assessments, scenarios and models; this has ramifications for policymaking and conservation science alike. This perspective piece reflects on lagged species responses to environmental change and discusses the potential consequences for species distribution models (SDMs), the tools of choice in biodiversity modelling. We suggest ways to better account for time lags in calibrating these models and to reduce their leverage effects in projections for improved biodiversity science and policy.

The future of endemic and threatened birds of the Amazon in the face of global climate change.

The anthropogenic impacts on the environment, including deforestation and the escalating emissions of greenhouse gases, have significantly contributed to global climate change that can lead to alterations in ecosystems. In this context, protected areas (PAs) are pillars for biodiversity conservation by being able, for example, to maintain the viability of populations of endangered species. On the other hand, the species range shifts do not follow the limits of PAs, jeopardizing the conservation of these species. Furthermore, the effectiveness of PAs is consistently undermined by impacts stemming from land use, hunting activities, and illegal exploitation, both within the designated areas and in their adjacent zones. The objectives of this study are to quantify the impacts of climate change on the distribution of threatened and endemic birds of the Amazon biome, evaluate the effectiveness of PAs in protecting the richness of threatened birds, and analyze the representativeness of species within PAs. We found with our results that climate suitability loss is above 80 for 65% of taxa in the optimistic scenario and above 93% in the pessimistic scenario. The results show that PAs are not effective in protecting the richness of Amazonian birds, just as they are ineffective in protecting most of the taxa studied when analyzed individually Although some taxa are presented as "Protected," in future scenarios these taxa may suffer major shrinkages in their distributions and consequently present population unviability. The loss of climatically suitable areas and the effectiveness of PAs can directly influence the loss of ecosystem services, fundamental to maintaining the balance of biodiversity. Therefore, our study paves the way for conservation actions aimed at these taxa so that they can mitigate current and future extinctions due to climate change.

Incorporating plant phenological responses into species distribution models reduces estimates of future species loss and turnover.

Anthropogenetic climate change has caused range shifts among many species. Species distribution models (SDMs) are used to predict how species ranges may change in the future. However, most SDMs rarely consider how climate-sensitive traits, such as phenology, which affect individuals' demography and fitness, may influence species' ranges. Using > 120 000 herbarium specimens representing 360 plant species distributed across the eastern United States, we developed a novel 'phenology-informed' SDM that integrates phenological responses to changing climates. We compared the ranges of each species forecast by the phenology-informed SDM with those from conventional SDMs. We further validated the modeling approach using hindcasting. When examining the range changes of all species, our phenology-informed SDMs forecast less species loss and turnover under climate change than conventional SDMs. These results suggest that dynamic phenological responses of species may help them adjust their ecological niches and persist in their habitats as the climate changes. Plant phenology can modulate species' responses to climate change, mitigating its negative effects on species persistence. Further application of our framework will contribute to a generalized understanding of how traits affect species distributions along environmental gradients and facilitate the use of trait-based SDMs across spatial and taxonomic scales.

Modelling heterogeneity in the classification process in multi-species distribution models can improve predictive performance.

Species distribution models and maps from large-scale biodiversity data are necessary for conservation management. One current issue is that biodiversity data are prone to taxonomic misclassifications. Methods to account for these misclassifications in multi-species distribution models have assumed that the classification probabilities are constant throughout the study. In reality, classification probabilities are likely to vary with several covariates. Failure to account for such heterogeneity can lead to biased prediction of species distributions. Here, we present a general multi-species distribution model that accounts for heterogeneity in the classification process. The proposed model assumes a multinomial generalised linear model for the classification confusion matrix. We compare the performance of the heterogeneous classification model to that of the homogeneous classification model by assessing how well they estimate the parameters in the model and their predictive performance on hold-out samples. We applied the model to gull data from Norway, Denmark and Finland, obtained from the Global Biodiversity Information Facility. Our simulation study showed that accounting for heterogeneity in the classification process increased the precision of true species' identity predictions by 30% and accuracy and recall by 6%. Since all the models in this study accounted for misclassification of some sort, there was no significant effect of accounting for heterogeneity in the classification process on the inference about the ecological process. Applying the model framework to the gull dataset did not improve the predictive performance between the homogeneous and heterogeneous models (with parametric distributions) due to the smaller misclassified sample sizes. However, when machine learning predictive scores were used as weights to inform the species distribution models about the classification process, the precision increased by 70%. We recommend multiple multinomial regression to be used to model the variation in the classification process when the data contains relatively larger misclassified samples. Machine learning prediction scores should be used when the data contains relatively smaller misclassified samples.

With super SDMs (machine learning, open access big data, and the cloud) towards more holistic global squirrel hotspots and coldspots.

Species-habitat associations are correlative, can be quantified, and used for powerful inference. Nowadays, Species Distribution Models (SDMs) play a big role, e.g. using Machine Learning and AI algorithms, but their best-available technical opportunities remain still not used for their potential e.g. in the policy sector. Here we present Super SDMs that invoke ML, OA Big Data, and the Cloud with a workflow for the best-possible inference for the 300 + global squirrel species. Such global Big Data models are especially important for the many marginalized squirrel species and the high number of endangered and data-deficient species in the world, specifically in tropical regions. While our work shows common issues with SDMs and the maxent algorithm ('Shallow Learning'), here we present a multi-species Big Data SDM template for subsequent ensemble models and generic progress to tackle global species hotspot and coldspot assessments for a more inclusive and holistic inference.

Characterization and Future Distribution Prospects of "Carciofo di Malegno" Landrace for Its In Situ Conservation.

"Carciofo di Malegno" is a little-known landrace of Cynara cardunculus subsp. scolymus cultivated in Camonica Valley (northern Italy). The morphological and phytochemical characteristics of this landrace were investigated; furthermore, a species distribution model (MaxEnt algorithm) was used to explore its ecological niche and the geographical area where it could be grown in the future. Due to its spiky shape, "Carciofo di Malegno" was distinct from any other artichoke sample considered, and it appears to be similar to those belonging to the "Spinosi" group. The concentration of chlorogenic acid (497.2 ± 116.0 mg/100 g DW) and cynarine (7.4 ± 1.2 mg/100 g DW) in "Carciofo di Malegno" was comparable to that of the commercial cultivars. In "Carciofo di Malegno," luteolin was detected in a significant amount (9.4 ± 1.5 mg/100 g DW) only in the stems and in the edible parts of the capitula. A MaxEnt distribution model showed that in the coming decades (2040-2060s), the cultivation of this landrace could expand to the pre-Alps and Alps of Lombardy. Climate change may promote the diffusion of "Carciofo di Malegno", contributing to preservation and the enhancement of this landrace and generating sustainable income opportunities in mountain areas through exploring new food or medicinal applications.

Unravelling the role of tropical cyclones in shaping present species distributions.

Driven by climate change, tropical cyclones (TCs) are predicted to change in intensity and frequency through time. Given these forecasted changes, developing an understanding of how TCs impact insular wildlife is of heightened importance. Previous work has shown that extreme weather events may shape species distributions more strongly than climatic averages; however, given the coarse spatial and temporal scales at which TC data are often reported, the influence of TCs on species distributions has yet to be explored. Using TC data from the National Hurricane Center, we developed spatially and temporally explicit species distribution models (SDMs) to examine the role of TCs in shaping present-day distributions of Puerto Rico's 10 Anolis lizard species. We created six predictor variables to represent the intensity and frequency of TCs. For each occurrence of a species, we calculated these variables for TCs that came within 500 km of the center of Puerto Rico and occurred within the 1-year window prior to when that occurrence was recorded. We also included predictor variables related to landcover, climate, topography, canopy cover and geology. We used random forests to assess model performance and variable importance in models with and without TC variables. We found that the inclusion of TC variables improved model performance for the majority of Puerto Rico's 10 anole species. The magnitude of the improvement varied by species, with generalist species that occur throughout the island experiencing the greatest improvements in model performance. Range-restricted species experienced small, almost negligible, improvements but also had more predictive models both with and without the inclusion of TC variables compared to generalist species. Our findings suggest that incorporating data on TCs into SDMs may be important for modeling insular species that are prone to experiencing these types of extreme weather events.

The influence of scale-dependent geodiversity on species distribution models in a biodiversity hotspot.

Improving models of species' distributions is essential for conservation, especially in light of global change. Species distribution models (SDMs) often rely on mean environmental conditions, yet species distributions are also a function of environmental heterogeneity and filtering acting at multiple spatial scales. Geodiversity, which we define as the variation of abiotic features and processes of Earth's entire geosphere (inclusive of climate), has potential to improve SDMs and conservation assessments, as they capture multiple abiotic dimensions of species niches, however they have not been sufficiently tested in SDMs. We tested a range of geodiversity variables computed at varying scales using climate and elevation data. We compared predictive performance of MaxEnt SDMs generated using CHELSA bioclimatic variables to those also including geodiversity variables for 31 mammalian species in Colombia. Results show the spatial grain of geodiversity variables affects SDM performance. Some variables consistently exhibited an increasing or decreasing trend in variable importance with spatial grain, showing slight scale-dependence and indicating that some geodiversity variables are more relevant at particular scales for some species. Incorporating geodiversity variables into SDMs, and doing so at the appropriate spatial scales, enhances the ability to model species-environment relationships, thereby contributing to the conservation and management of biodiversity. This article is part of the Theo Murphy meeting issue 'Geodiversity for science and society'.

Predicting the current habitat refugia of Himalayan Musk deer (Moschus chrysogaster) across Nepal.

Himalayan Musk deer, Moschus chrysogaster is widely distributed but one of the least studied species in Nepal. In this study, we compiled a total of 429 current presence points of direct observation of the species, pellets droppings, and hoofmarks based on field-based surveys during 2018-2021 and periodic data held by the Department of National Park and Wildlife Conservation. We developed the species distribution model using an ensemble modeling approach. We used a combination of bioclimatic, anthropogenic, topographic, and vegetation-related variables to predict the current suitable habitat for Himalayan Musk deer in Nepal. A total of 16 predictor variables were used for habitat suitability modeling after the multicollinearity test. The study shows that the 6973.76 km2 (5%) area of Nepal is highly suitable and 8387.11 km2 (6%) is moderately suitable for HMD. The distribution of HMD shows mainly by precipitation seasonality, precipitation of the warmest quarter, temperature ranges, distance to water bodies, anthropogenic variables, and land use and land cover change (LULC). The probability of occurrence is less in habitats with low forest cover. The response curves indicate that the probability of occurrence of HMD decreases with an increase in precipitation seasonality and remains constant with an increase in precipitation of the warmest quarter. Thus, the fortune of the species distribution will be limited by anthropogenic factors like poaching, hunting, habitat fragmentation and habitat degradation, and long-term forces of climate change.

Global Trends in Climate Suitability of Bees: Ups and Downs in a Warming World.

Bees represent vital natural assets contributing significantly to global food production and the maintenance of ecosystems. While studies on climate change effects impacting major pollinators like honeybees and bumblebees raise concerns about global diversity and crop productivity, comprehensive global-scale analyses remain limited. This study explores the repercussions of global warming on 1365 bees across seven families of bees worldwide. To compile a robust global bee occurrence dataset, we utilized the innovative 'BeeBDC' R package that amalgamated over 18.3 million bee occurrence records sourced from various repositories. Through species distribution models under the SSP585 scenario in the year 2070, we assessed how climate change influences the climate suitability of bees on a global scale, examining the impacts across continents. Our findings suggested that approximately 65% of bees are likely to witness a decrease in their distribution, with reductions averaging between 28% in Australia and 56% in Europe. Moreover, our analysis indicated that climate change's impact on bees is projected to be more severe in Africa and Europe, while North America is expected to witness a higher number (336) of bees expanding their distribution. Climate change's anticipated effects on bee distributions could potentially disrupt existing pollinator-plant networks, posing ecological challenges that emphasize the importance of pollinator diversity, synchrony between plants and bees, and the necessity for focused conservation efforts.

Uncovering Broad Macroecological Patterns by Comparing the Shape of Species' Distributions along Environmental Gradients.

AbstractSpecies' distributions can take many different forms. For example, fat-tailed or skewed distributions are very common in nature, as these can naturally emerge as a result of individual variability and asymmetric environmental tolerances, respectively. Studying the basic shape of distributions can teach us a lot about the ways climatic processes and historical contingencies shape ecological communities. Yet we still lack a general understanding of how their shapes and properties compare to each other along gradients. Here, we use Bayesian nonlinear models to quantify range shape properties in empirical plant distributions. With this approach, we are able to distil the shape of plant distributions and compare them along gradients and across species. Studying the relationship between distribution properties, we revealed the existence of broad macroecological patterns along environmental gradients-such as those expected from Rapoport's rule and the abiotic stress limitation hypothesis. We also find that some aspects of the shape of observed ranges-such as kurtosis and skewness of the distributions-could be intrinsic properties of species or the result of their historical contexts. Overall, our modeling approach and results untangle the general shape of plant distributions and provide a mapping of how this changes along environmental gradients.

Geographical distribution of two major quarantine fruit flies (Bactrocera minax Enderlein and Bactrocera dorsalis Hendel) in Sichuan Basin based on four SDMs.

Both Bactrocera minax and Bactrocera dorsalis are phytophagous insects, and their larvae are latent feeders, which cause great damage and economic losses to agriculture production and trade. This study aimed to provide a scientific reference for researching and developing the feasible countermeasures against these two pests. Based on the distribution data of B. minax and B. dorsalis in China, obtained from the Chinese herbaria, investigation and literature. Four niche models (Garp, Bioclim, Domain, and Maxent) were used to analyze the key environmental factors affecting the distribution of both pests and to build prediction models of the potential distribution in Sichuan Basin. Combined with two statistical standards, area under the receiver operating characteristic curve (AUC) and Kappa, the validity of prediction models were analyzed and compared. The results show that: the average AUC values of the four models are all above 0.90, and the average Kappa values are all above 0.75, indicating that the four models are suitable for predicting the potential distribution area of B. minax and B. dorsalis. The annual range of temperature, the mean temperature in the driest quarter, the mean temperature in the warmest quarter, the annual precipitation, and the precipitation in driest month are the key environmental factors affecting the distribution of B. minax, while the mean diurnal temperature range, the mean temperature in the driest quarter, the seasonal temperature variations and the precipitation in driest month affect the potential distribution of B. dorsalis. The suitable areas for B. minax are mainly concentrated in the eastern of Sichuan Basin, while the suitable areas for B. dorsalis are concentrated in the southeastern. Except for the Bioclim model, the highly-suitable area for both pests predicted by the other three models are all greater than 15.94 × 104 km2 and the moderately-suitable areas are greater than 13.57 × 104 km2. In conclusion, the suitable areas for both pests in Sichuan Basin are quite wide. Therefore, the relevant authorities should be given strengthened monitoring of both pests, especially in areas with high incursion rates.

Genetic Structure and Population History of the Zaisan Toad-Headed Agama (Phrynocephalus melanurus) Inferred from Mitochondrial DNA.

The agamid lizard Phrynocephalus melanurus is restricted to Northwest China (Dzungar Basin) and the adjacent Eastern Kazakhstan (Zaisan and Alakol basins). To elucidate the phylogeography of P. melanurus, we obtained the mitochondrial DNA COI segments of 175 sampled lizards from 44 localities across the whole distribution. Phylogenetic analyses revealed two main Clades comprising five geographically structured lineages (I, IIa, IIb1, IIb2, and IIb3) that fit an isolation-by-distance (IBD) model. The divergence from the most recent common ancestor was dated to ~1.87 million years ago (Ma). Demographic analyses demonstrated lineage-specific response to past climate change: stable population for Clade I, Subclade IIb1; past population expansion for IIb3 since 0.18 Ma, respectively. Bayesian phylogeographic diffusion analyses detected initial spreading at the Saur Mount vicinity, approximately 1.8 Ma. Historical species distribution model (SDM) projected expansion of the suitable habitat in the last interglacial and shift and contraction in the last glacial maximum and Holocene epochs. The SDM predicted a drastic reduction in suitable area throughout the range as a response to future climate change. Our findings suggest that the evolution of P. melanurus followed a parapatric divergence with subsequent dispersal and adaptation to cold and dry environments during the Quaternary. Overall, this work improves our understanding of the lineage diversification and population dynamics of P. melanurus, providing further insights into the evolutionary processes that occurred in Northwest China and adjacent Eastern Kazakhstan.

Projected climate and canopy change lead to thermophilization and homogenization of forest floor vegetation in a hotspot of plant species richness.

Mountain forests are plant diversity hotspots, but changing climate and increasing forest disturbances will likely lead to far-reaching plant community change. Projecting future change, however, is challenging for forest understory plants, which respond to forest structure and composition as well as climate. Here, we jointly assessed the effects of both climate and forest change, including wind and bark beetle disturbances, using the process-based simulation model iLand in a protected landscape in the northern Alps (Berchtesgaden National Park, Germany), asking: (1) How do understory plant communities respond to 21st-century change in a topographically complex mountain landscape, representing a hotspot of plant species richness? (2) How important are climatic changes (i.e., direct climate effects) versus forest structure and composition changes (i.e., indirect climate effects and recovery from past land use) in driving understory responses at landscape scales? Stacked individual species distribution models fit with climate, forest, and soil predictors (248 species currently present in the landscape, derived from 150 field plots stratified by elevation and forest development, overall area under the receiving operator characteristic curve = 0.86) were driven with projected climate (RCP4.5 and RCP8.5) and modeled forest variables to predict plant community change. Nearly all species persisted in the landscape in 2050, but on average 8% of the species pool was lost by the end of the century. By 2100, landscape mean species richness and understory cover declined (-13% and -8%, respectively), warm-adapted species increasingly dominated plant communities (i.e., thermophilization, +12%), and plot-level turnover was high (62%). Subalpine forests experienced the greatest richness declines (-16%), most thermophilization (+17%), and highest turnover (67%), resulting in plant community homogenization across elevation zones. Climate rather than forest change was the dominant driver of understory responses. The magnitude of unabated 21st-century change is likely to erode plant diversity in a species richness hotspot, calling for stronger conservation and climate mitigation efforts.

Environmental drivers behind the genetic differentiation in mountain chickadees (Poecile gambeli).

Anthropogenic climate change has a large impact on wildlife populations and the scale of the impacts has been increasing. In this study, we utilised 3dRAD sequence data to investigate genetic divergence and identify the environmental drivers of genetic differentiation between 12 populations of mountain chickadees, family Paridae, sampled across North America. To examine patterns of genetic variation across the range, we conducted a discriminant analysis of principal components (DAPC), admixture analysis, and calculated pairwise Fst values. The DAPC revealed four clusters: southern California, eastern Rocky Mountains, northwestern Rocky Mountains, and Oregon/northern California. We then used BayeScEnv to highlight significant outlier SNPs associated with the five environmental variables. We identified over 150 genes linked to outlier SNPs associated with more than 15 pathways, including stress response and circadian rhythm. We also found a strong signal of isolation by distance and local temperature was highly correlated with genetic distance. Maxent simulations showed a northward range shift over the next 50 years and a decrease in suitable habitat, highlighting the need for immediate conservation action.