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Aim: Understanding the impacts of past and future climate change on genetic diversity and structure is a current major research gap. We ask whether past range shifts explain the observed genetic diversity of surfgrass species and if future climate change projections anticipate genetic diversity losses. Our study aims to identify regions of long‐term climate suitability with higher and unique seagrass genetic diversity and predict future impacts of climate change on them. Location: Northeast Pacific. Time Period: Analyses considered a timeframe from the Last Glacial Maximum (LGM; 20 kybp) until one Representative Concentration Pathway (RCP) scenario of future climate changes (RCP 8.5; 2100). Major Taxa Studied: Two seagrass species belonging to the genus Phyllospadix. Methods: We estimated population genetic diversity and structure using 11 polymorphic microsatellite markers. We predicted the distribution of the species for the present, LGM, and near future (RCP 8.5, no climate mitigation) using Species Distribution Models (SDMs). Results: SDMs revealed southward range shifts during the LGM and potential poleward expansions in the future. Genetic diversity of Phyllospadix torreyi decreases from north to south, but in Phyllospadix scouleri the trend is variable. Phyllospadix scouleri displays signals of genome admixture at the southernmost and northernmost edges of its distribution. Main Conclusions: The genetic patterns observed in the present reveal the influence of climate‐driven range shifts in the past and suggest further consequences of climate change in the future, with potential loss of unique gene pools. This study also shows that investigating climate links to present genetic information at multiple timescales can establish a historical context for analyses of the future evolutionary history of populations. [ABSTRACT FROM AUTHOR]

Understanding how past and current environmental conditions shape the demographic and genetic distributions of organisms facilitates our predictions of how future environmental patterns may affect populations. The Canyon Rubyspot damselfly (Odonata: Zygoptera: Hetaerina vulnerata) is an insect with a range distribution from Colombia to the arid southwestern United States, where it inhabits shaded mountain streams in the arid southwestern United States. Past spatial fragmentation of habitat and limited dispersal capacity of H. vulnerata may cause population isolation and genetic differentiation, and projected climate change may exacerbate isolation by further restricting the species' distribution. We constructed species distribution models (SDMs) based on occurrences of H. vulnerata and environmental variables characterizing the species' niche. We inferred seven current potential population clusters isolated by unsuitable habitat. Paleoclimate models indicated habitat contiguity in past conditions; projected models indicated some habitat fragmentation in future scenarios. Seventy‐eight H. vulnerata individuals from six of the current clusters were sequenced via ddRADseq and processed with Stacks. Principal components and phylogeographic analyses resolved three subpopulations; Structure resolved four subpopulations. FST values were low (<0.05) for nearby populations and >0.15 for populations separated by expanses of unsuitable habitat. Isolation by distance was an existing but weak factor in determining genomic structure; isolation by environment and the intervening landscape explained a significant proportion of genetic distance. Hetaerina vulnerata populations were shown to be isolated by a lack of tree canopy coverage, an important habitat predictor for oviposition and territoriality. Thus, H. vulnerata populations are likely separated and are genetically isolated. Integrating SDMs with landscape genetics allowed us to identify populations separated by distance and unsuitable habitat, explaining population genetic patterns and probable fates for populations under future climate scenarios. [ABSTRACT FROM AUTHOR]

Aim: To evaluate the performance of species distribution models in predicting observed colonisations, persistences and extirpations in response to changes in climate and land use over a multi‐decadal period. Location: Sweden. Methods: We use historical (early 20th century) land use and climate data to build species distribution models for 84 plant species across three provinces of Sweden. Model performance was then evaluated internally using a subset of the historical data for cross‐validation, as well as by using the models to project occurrences to the modern day and validating them with observed occurrences from 1990 to 2020. We then analysed predicted and observed occurrences in the modern period in terms of persistence, extirpation (local extinction) and colonisation in relation to species' habitat and climate associations. Results: We found overall high agreement between evaluation methods, although internal evaluation gave consistently higher values for model performance (using true skill statistic, TSS). Overall, extirpations were worst predicted, with on average fewer than one‐third of each species' extirpations being foreseen by the models. Colonisations were better predicted, while persistences were relatively well‐predicted. Predictive accuracy of colonisations was higher for species with relatively warmer temperature associations (climate‐driven expansion), while extirpations were better predicted in cool‐related species (retractions at cool edges). Colonisations of forest‐associated species were more common than predicted (underpredicted), despite widespread patterns of afforestation. Assessing grid‐cell level turnover, we found that in grid cells that experienced the largest changes in terms of climate and land use, predicted extirpations were less likely to have happened. Main Conclusions: We found that commonly applied modelling approaches have limited ability to predict observed changes in species occurrences, especially extirpations. This suggests that we should take predictions of future biodiversity loss very seriously. However, the ability for species to (at least temporarily) persist in unsuitable conditions could be an opportunity for biodiversity conservation. [ABSTRACT FROM AUTHOR]

Aims: The Middle East, located in the arid belt of the Earth, is home to a diverse range of biodiversity, with its mountain ecosystems being the most important centres of species diversity and endemism. In this study, the impact of climate change on alpine bird species in the Middle East was assessed across five mountain systems: Alborz–Kopet‐Dagh, Caucasus–Pontic, Levant–Taurus, Sarawat–Hijaz and Zagros–Central Iran. Location: Middle East. Methods: Using species distribution models (SDMs), 38 native alpine bird species were analysed under different climate change scenarios. We also identified future multispecies in situ and ex situ climate refugia and assessed the efficiency of the current protected areas (PAs) system in protecting them. Results: The results indicated that, on average, habitat suitability for these species is projected to decline by 36.83% (2050, SSP2‐4.5) to 60.10% (2070, SSP5‐8.5) with an upward range shift. Based on stacking range change of the species, Levant–Taurus, Zagros–Central Iran and Alborz–Kopet–Dagh mountain ranges will experience the highest amount of habitat loss, respectively, with Caucasus–Pontic being least affected. The gap analysis showed that the existing PAs system covers only 13% and 10% of the in situ and ex situ climatic refugia, respectively. Conclusions: Our findings underscore the significance of mountainous regions in the Middle East for the persistence of alpine bird species and the urgent need to prioritize climate refugia in transboundary and participatory conservation plans. It is crucial to prevent habitat degradation and alteration resulting from human activities in these areas to ensure the persistence of alpine species and their habitats. [ABSTRACT FROM AUTHOR]

The use of species distribution models (SDMs) to predict local abundance has been often proposed and contested. We tested whether SDMs at different spatiotemporal resolutions may predict the local density of 14 bird species of open/semi‐open habitats. SDMs were built at 1 ha and 1 km, and with long‐term versus a mix of current and long‐term climatic variables. The estimated environmental suitability was used to predict local abundance obtained by means of 275 linear transects. We tested SDM ability to predict abundance for all sampled sites versus occurrence sites, using N‐mixture models to account for imperfect detection. Then, we related the R2 of N‐mixture models to SDM traits. Fine‐grain SDMs appeared generally more robust than large‐grain ones. Considering the all‐transects models, for all species environmental suitability displayed a positive and highly significant effect at all the four combinations of spatial and temporal grains. When focusing only on occurrence transects, at the 1 km grain only one species showed a significant and positive effect. At the 1 ha grain, 62% of species models showed (over both climatic sets) a significant or nearly significant positive effect of environmental suitability on abundance. Grain was the only factor significantly affecting the model's explanatory power: 1 km grain led to lower amounts of variation explained by models. Our work re‐opens the debate about predicting abundance using SDM‐derived suitability, emphasizing the importance of grains and of spatiotemporal resolution more in general. The incorporation of local variables into SDMs at fine grains is key to predict local abundance. SDMs worked out at really fine grains, approaching the average size of territory or home range of target species, are needed to predict local abundance effectively. This may result from the fact that each single cell may represent a potential territory/home range, and hence a higher suitability over a given area means that more potential territories occur there. [ABSTRACT FROM AUTHOR]

Habitat suitability calculated from species distribution models (SDMs) has been used to assess population performance, but empirical studies have provided weak or inconclusive support to this approach. Novel approaches measuring population distances to niche centroid and margin in environmental space have been recently proposed to explain population performance, particularly when populations experience exceptional environmental conditions that may place them outside of the species niche. Here, we use data of co‐occurring species' decay, gathered after an extreme drought event occurring in the southeast of the Iberian Peninsula which highly affected rich semiarid shrubland communities, to compare the relationship between population decay (mortality and remaining green canopy) and 1) distances between populations' location and species niche margin and centroid in the environmental space, and 2) climatic suitability estimated from frequently used SDMs (here MaxEnt) considering both the extreme climatic episode and the average reference climatic period before this. We found that both SDMs‐derived suitability and distances to species niche properly predict populations performance when considering the reference climatic period; but climatic suitability failed to predict performance considering the extreme climate period. In addition, while distance to niche margins accurately predict both mortality and remaining green canopy responses, centroid distances failed to explain mortality, suggesting that indexes containing information about the position to niche margin (inside or outside) are better to predict binary responses. We conclude that the location of populations in the environmental space is consistent with performance responses to extreme drought. Niche distances appear to be a more efficient approach than the use of climate suitability indices derived from more frequently used SDMs to explain population performance when dealing with environmental conditions that are located outside the species environmental niche. The use of this alternative metrics may be particularly useful when designing conservation measures to mitigate impacts of shifting environmental conditions. [ABSTRACT FROM AUTHOR]

Aim: Climate Change Vulnerability Assessment (CCVA) prescribes the quantification of species vulnerability based on three components: sensitivity, adaptive capacity and exposure. Such assessments should be performed through combined approaches that integrate trait‐based elements (e.g., measures of species sensitivity such as niche width) with correlative tools quantifying exposure (magnitude of changes in climate within species habitat). Furthermore, as land use alterations may increase climate impacts on biodiversity, CCVAs should focus on both climate and land use change effects. Unfortunately, most of such assessments have so far focused exclusively on exposure to climate change. Location: Himalaya. Methods: We evaluated the vulnerability of three otter species occurring in the Himalayan region, that is, Aonyx cinereus, Lutra lutra and Lutrogale perspicillata, to 2050 climate and land use change through the recently proposed Climate Niche Factor Analysis (CNFA) framework combined with Species Distribution Models. Results: Future climate and land use change will reduce (6%–15%) and shift (10%–18%) the geographical range of the three species in the Himalaya, with land use alterations exerting far more severe effects than climate change. Among vulnerability components, sensitivity played a greater role than exposure in determining the vulnerability of the otters. Specifically, the most specialist species, L. perspicillata, showed the highest vulnerability in comparison with the most generalist, L. lutra. Main conclusions: Our results underline how coupling climate and land use change components in CCVAs can generate diverging predictions of species vulnerability compared to approaches relying on climate change only. Moreover, intrinsic components, such as species sensitivity, proved significantly more important in determining vulnerability than extrinsic metrics such as habitat exposure. [ABSTRACT FROM AUTHOR]

Accurately predicting the future distribution of species is crucial for understanding how species will response to global environmental change and for evaluating the effectiveness of current protected areas (PAs). Here, we assessed the effect of climate and land use change on the projected suitable habitats of Davidia involucrata Baill under different future scenarios using the following two types of models: (a) only climate covariates (climate SDMs) and (b) climate and land use covariates (full SDMs). We found that full SDMs perform significantly better than climate SDMs in terms of both AUC (p <.001) and TSS (p <.001) and also projected more suitable habitat than climate SDMs both in the whole study area and in its current suitable range, although D. involucrate is predicted to loss at least 26.96% of its suitable area under all future scenarios. Similarly, we found that these range contractions projected by climate SDMs would negate the effectiveness of current PAs to a greater extent relative to full SDMs. These results suggest that although D. involucrate is extremely vulnerability to future climate change, conservation intervention to manage habitat may be an effective option to offset some of the negative effects of a changing climate on D. involucrate and can improve the effectiveness of current PAs. Overall, this study highlights the necessity of integrating climate and land use change to project the future distribution of species. [ABSTRACT FROM AUTHOR]

Aim: Detection of rare species is limited by their intrinsic nature and by the constraints associated with traditional field surveys. Remote sensing (RS) provides a powerful alternative to traditional detection methods through the increasing availability of RS products. Here, we assess the capacity of RS at high and medium resolution to detect rare plants with direct and indirect approaches, and how the performance of RS can be influenced by the characteristics of species. Methods: An extensive literature review was conducted to synthesize the use of RS to detect or predict rare plant occurrence at high and medium resolution (<30 m and 30–300 m, respectively). The concept of "rarity" was based on Rabinowitz's rare species classification. The literature review was performed in Scopus for the period 1990–2020. Results: While direct detection is often limited, it is possible with high and very high spatial resolution data for rare plants with distinctive traits. RS is also able to capture biophysical conditions driving rare plant distributions, which can indirectly provide accurate predictions for them. Both approaches have the potential to discover new populations of rare plants. RS can also feed SAMs of rare plants, which combined with SDMs can provide a valuable approach for rare plant detection. While direct detection is limited by the space occupied by a species within its habitat and its morphological, phenological and physiological characteristics, the predictive performance of RS‐based SDMs (indirect detection) can be influenced by habitat size, habitat specificity and phenological features of rare plants. Similarly, model predictive performance can be influenced by the rarity form of the target species according to the rarity classification criteria. Main conclusions. With this synthesis, the strong potential of RS for the purposes of detection and prediction of rare plant has been highlighted, with practical applications for conservation and management. [ABSTRACT FROM AUTHOR]

The striped dolphin (Stenella coeruleoalba) is one of the most abundant cetaceans of the Mediterranean Sea. Nevertheless, it is ranked as 'Vulnerable' by IUCN Red List experts.To implement conservation strategies within the Mediterranean Sea, it is crucial to understand how striped dolphins are distributed at different geographical scales. The present study investigates its distribution within the EU Adriatic and Ionian Sea Region.To model striped dolphin distribution, topographic (distance from shore, depth and slope) and oceanographic variables (sea surface temperature and phytoplankton concentration) were employed, and species distribution models (SDMs) were developed using Maxent software. Generalized additive models (GAMs) were used to improve the information supplied by the SDMs, allowing the description of response curves to striped dolphin occurrences.Statistical analysis shows that striped dolphins are not evenly distributed within the investigated area. The higher encounter rates observed in the Gulf of Taranto and the Gulf of Corinth confirm that both sub‐regions are crucial for its conservation. Furthermore, SDMs also indicate suitable areas in the offshore waters of the Adriatic Sea, along the continental slope of the north‐eastern Ionian Sea and over its continental shelf. Within the EU Adriatic and Ionian Sea Region, striped dolphins prefer areas at a greater distance from shore, with steeper slopes, deeper waters, and higher sea surface temperature. The topographic variables are the most explicative factors for both modelling approaches.The analysis of striped dolphin SDMs allows the identification of important areas for conservation and highlights zones where appropriate mitigation strategies could reduce the impact of human activities on this Vulnerable species. Further analysis of SDMs for different periods of the year, as well as a genetic study, could highlight seasonal movements across and within sub‐regions. [ABSTRACT FROM AUTHOR]

Aim: As a prominent geographical distribution centre for the dark coniferous forests, mountains of Southwest China (MSWC) is experiencing an unprecedented warming trend, posing severe challenges to the survival of dominant fir (Abies) species. Although plant's migration ability is a prerequisite for its survival in changing environments, it has often been ignored in species distribution models (SDMs). This study aimed to quantify the magnitude and direction of range changes by the year 2080 for six dominant fir species, that is Abies recurvata, Abies faxoniana, Abies squamata, Abies ernestii, Abies forrestii and Abies georgei, with an emphasis on exploring the relationship between migration ability and projected distributions. Location: The mountains of Southwest China. Methods: We applied the Maximum Entropy (Maxent) algorithm to calibrate ecological niche models and to project the climatically suitable areas (CSAs) of each species under two emission scenarios (RCP 4.5 and RCP 8.5). Additionally, we delimited future species ranges by three migration scenarios (full‐, no‐ and partial‐migration scenarios). Results: The simulations showed the distinctive responses of the six fir species to anthropogenic climate change (ACC). By 2080, the distribution areas of Abies recurvata were projected to decline only in the no‐migration scenario but increase under the full‐ and partial‐migration scenarios, while the other five species were projected to decline in the majority of emission × migration scenarios. Fir species in the southern region were predicted to be more vulnerable to ACC due to the larger losses in CSAs and a stronger effect of the partial‐migration scenario on the newly colonized areas of this group. The studied species showed a simulated migration trend (northward and westward) to the interior Qinghai‐Tibet Plateau under ACC. Main conclusions: Benefits or losses for species under ACC depended on the geographical location, their ecological niches and migration abilities, which provide essential insights for a spatial conservation assessment of biodiversity hotspots in the future. [ABSTRACT FROM AUTHOR]

Aedes aegypti (Diptera: Culicidae) is an urban mosquito involved in the transmission of numerous viruses, including dengue, chikungunya and Zika. In Argentina, Ae. aegypti is the main vector of dengue virus and has been involved in several outbreaks in regions ranging from northern to central Argentina since 2009. In order to evaluate areas of potential vector‐borne disease transmission in the city of Córdoba, Argentina, the present study aimed to identify the environmental, socioeconomic and demographic factors driving the distribution of Ae. aegypti larvae through spatial analysis in the form of species distribution models (SDMs). These models elucidate relationships between known occurrences of a species and environmental data in order to identify areas with suitable habitats for that species and the consequent risk for disease transmission. The maximum entropy species distribution model was able to fit the training data well, with an average area under the receiver operating characteristic curve (AUC) of > 0.8, and produced models with fair extrapolation capacity (average test AUC: > 0.75). Human population density, distance to vegetation and water channels were the main variables predictive of the vector suitability of an area. The results of this work will be used to target surveillance and prevention measures, as well as in mosquito management. Factors driving the distribution of Aedes aegypti (Diptera: Culicidae) larvae in Córdoba, Argentina were identified using species distribution models.Local socioeconomic and demographic studies, as well as city cleaning campaigns, are important to avoid the availability of man‐made containers in public places.Intrinsic knowledge of the study area is key to producing empirical models of mosquito vectors. [ABSTRACT FROM AUTHOR]

Aim Quaternary palaeopalynological records collected throughout the Iberian Peninsula and species distribution models (SDMs) were integrated to gain a better understanding of the historical biogeography of the Iberian Abies species (i.e. Abies pinsapo and Abies alba). We hypothesize that SDMs and Abies palaeorecords are closely correlated, assuming a certain stasis in climatic and topographic ecological niche dimensions. In addition, the modelling results were used to assign the fossil records to A. alba or A. pinsapo, to identify environmental variables affecting their distribution, and to evaluate the ecological segregation between the two taxa. Location The Iberian Peninsula. Methods For the estimation of past Abies distributions, a hindcasting process was used. Abies pinsapo and A. alba were modelled individually, first calibrating the model for their current distributions in relation to the present climate, and then projecting it into the past—the last glacial maximum (LGM) and the Middle Holocene periods—in relation to palaeoclimate simulations. The resulting models were compared with Iberian-wide fossil pollen records to detect areas of overlap. Results The overlap observed between past Abies refugia—inferred from fossil pollen records—and the SDMs helped to construct the Quaternary distribution of the Iberian Abies species. SDMs yielded two well-differentiated potential distributions: A. pinsapo throughout the Baetic mountain Range and A. alba along the Pyrenees and Cantabrian Range. These results propose that the two taxa remained isolated throughout the Quaternary, indicating a significant geographical and ecological segregation. In addition, no significant differences were detected comparing the three projections (present-day, Mid-Holocene and LGM), suggesting a relative climate stasis in the refuge areas during the Quaternary. Main conclusions Our results confirm that SDM projections can provide a useful complement to palaeoecological studies, offering a less subjective and spatially explicit hypothesis concerning past geographic patterns of Iberian Abies species. The integration of ecological-niche characteristics from known occurrences of Abies species in conjunction with palaeoecological studies could constitute a suitable tool to define appropriate areas in which to focus proactive conservation strategies. [ABSTRACT FROM AUTHOR]