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Aim: Modelling species distributions at the community level is required to make effective forecasts of global change impacts on diversity and ecosystem functioning. Community predictions may be achieved using macroecological properties of communities (macroecological models, MEM), or by stacking of individual species distribution models (stacked species distribution models, S-SDMs). To obtain more realistic predictions of species assemblages, the SESAM (spatially explicit species assemblage modelling) framework suggests applying successive filters to the initial species source pool, by combining different modelling approaches and rules. Here we provide a first test of this framework in mountain grassland communities. Location: The western Swiss Alps. Methods: Two implementations of the SESAM framework were tested: a probability ranking' rule based on species richness predictions and rough probabilities from SDMs, and a trait range' rule that uses the predicted upper and lower bound of community-level distribution of three different functional traits (vegetative height, specific leaf area, and seed mass) to constrain a pool of species from binary SDMs predictions. Results: We showed that all independent constraints contributed to reduce species richness overprediction. Only the probability ranking' rule allowed slight but significant improvements in the predictions of community composition. Main conclusions: We tested various implementations of the SESAM framework by integrating macroecological constraints into S-SDM predictions, and report one that is able to improve compositional predictions. We discuss possible improvements, such as further understanding the causality and precision of environmental predictors, using other assembly rules and testing other types of ecological or functional constraints.

The distribution and abundance of plant species are intimately related to their reproductive success, which in turn is affected by a large number of environmental variables. Yet, reproductive success is rarely taken into account in species distribution models (SDMs). In this paper we examine the extent to which consideration of the reproduction niche and its relationship with temperature could improve SDMs. We review the literature on plant reproductive responses to temperature and the influence of these relationships on species range delimitation. We define the reproduction niche and discuss how temperature influences several stages of the reproductive process. Furthermore, we review examples that illustrate how the reproduction niche influences species distributions and discuss how aspects of the reproduction niche could be considered in SDMs. We show that the reproduction niche fundamentally influences species distributions and that in principle it is easy to include aspects of the reproduction niche in SDMs, although sufficient data are only available for a restricted number of species. Bayesian methods and inverse parameterization may be the most efficient way to use existing data.

Biomes are constructs for organising knowledge on the structure and functioning of the world’s ecosystems, and serve as useful units for monitoring how the biosphere responds to anthropogenic drivers, including climate change. The current practice of delimiting biomes relies on expert knowledge. Recent studies have questioned the value of such biome maps for comparative ecology and globalchange research, partly due to their subjective origin. Here we propose a flexible method for developing biome maps objectively. The method uses range modelling of several thousands of plant species to reveal spatial attractors for different growth-form assemblages that define biomes. The workflow is illustrated using distribution data from 23 500 African plant species. In an example application, we create a biome map for Africa and use the fitted species models to project biome shifts. In a second example,wemap gradients of growth-form suitability that can be used to identify sites for comparative ecology. This method provides a flexible framework that (1) allows a range of biome types to be defined according to user needs and (2) enables projections of biome changes that emerge purely from the individualistic responses of plant species to environmental changes.

The ENMTools software package was introduced in 2008 as a platform for making measurements on environmental niche models (ENMs, frequently referred to as species distribution models or SDMs), and for using those measurements in the context of newly developed Monte Carlo tests to evaluate hypotheses regarding niche evolution. Additional functionality was later added for model selection and simulation from ENMs, and the software package has been quite widely used. ENMTools was initially implemented as a Perl script, which was also compiled into an executable file for various platforms. However, the package had a number of significant limitations; it was only designed to fit models using Maxent, it relied on a specific Perl distribution to function, and its internal structure made it difficult to maintain and expand. Subsequently, the R programming language became the platform of choice for most ENM studies, making ENMTools less usable for many practitioners. Here we introduce a new R version of ENMTools that implements much of the functionality of its predecessor as well as numerous additions that simplify the construction, comparison and evaluation of niche models. These additions include new metrics for model fit, methods of measuring ENM overlap, and methods for testing evolutionary hypotheses. The new version of ENMTools is also designed to work within the expanding universe of R tools for ecological biogeography, and as such includes greatly simplified interfaces for analyses from several other R packages.

Phase-sensitive time-domain reflectometry (Φ-OTDR) can be used for fully distributed long-distance vibration monitoring. There is a fading phenolmenon in the Φ-OTDR, which will cause the signal intensity somewhere to be too low to extract the phase of the signal without distortion. In this paper, the Φ-OTDR based on space-division multiplexing (SDM) is proposed to suppress fading and we used multi-core optical fiber (MCF) to realize SDM. While inheriting the previous optimization strategy, we proposed a strategy based on frequency spectral similarity to process multiple independent signals obtained by SDM. And we compared the two methods. Through the experiments, the distortion rate can be reduced from an average level of 9.34% to less than 2% under continuous running of 270 s, which proves that SDM is a reliable technical route to achieve fading suppression. This method can effectively improve the fading suppression capability of the existed commercial systems.

Species distribution models, also known as ecological niche models or habitat suitability models, have become commonplace for addressing fundamental and applied biodiversity questions. Although the field has progressed rapidly regarding theory and implementation, key assumptions are still frequently violated and recommendations inadvertently overlooked. This leads to poor models being published and used in real‐world applications. In a structured, didactic treatment, we summarize what in our view constitute the ten most problematic issues, or hazards, negatively affecting implementation of correlative approaches to species distribution modeling (specifically those that model suitability by comparing the environments of a species' occurrence records with those of a background or pseudoabsence sample). For each hazard, we state relevant assumptions, detail problems that arise when violating them, and convey straightforward existing recommendations. We also discuss five major outstanding questions of active current research. We hope this contribution will promote more rigorous implementation of these valuable models and stimulate further advancements.

With the continuous increase in the demand for land and natural resources, driven by population growth and economic expansion, the conservation of rare and endangered species faces mounting pressure. Exploring how to achieve the target of conserving 30% of the global terrestrial area proposed by the Kunming–Montreal Global Biodiversity Framework is of great significance for biodiversity and species protection. This study employed a combined SDMs–InVEST modeling approach to predict the current and future potential distributions, habitat quality, and carbon storage of 10 protected plant species in Hubei Province. Using the high‐potential distribution areas of these 10 species as conservation targets, combined with regions of high habitat quality and carbon storage, Marxan was applied to identify conservation gaps for these 10 protected plant species in Hubei Province. Results indicate that the province‐wide mean Habitat Quality Index (HQI) is projected to increase gradually from 0.355 to 0.366, while spatial heterogeneity of HQI will become more pronounced—western mountainous areas show marked HQI improvements, whereas HQI around central–eastern urban agglomerations declines significantly. Total ecosystem carbon storage in Hubei is projected to rise from 2.11 × 109 t to 2.13 × 109 t. On the basis of 423 occurrence records spanning 10 species (10 genera, 9 families), ensemble SDMs found climate to be the primary determinant of potential distributions; however, the future influence of anthropogenic disturbance and effects of habitat patches (EHPs) is projected to increase, leading to a 2.6% contraction in the total area of core potential distribution zones. These findings provide spatially explicit scientific guidance for optimizing regional protected‐area networks and for aligning biodiversity conservation with carbon management objectives under China's dual‐carbon strategy. Furthermore, multi‐period systematic conservation planning revealed significant protection gaps in interprovincial mountainous regions, forming four key aggregation zones: the Jinqian River source region (Shiyan–Shaanxi), the Tongbai Mountain belt (Suizhou–Henan), the Mufu Mountain region (Xianning), and the Wuling Mountain corridor (Enshi–Chongqing). These regions represent future conservation priorities. Our findings indicate that priority should be given to establishing new nature reserves selected from the 219 conservation gap planning units identified in this study, in order to strengthen the regional conservation‐planning system for rare and endangered plants in Hubei Province and to provide scientific and theoretical support for achieving the targets of the Kunming–Montreal Global Biodiversity Framework. An integrated SDMs‐InVEST approach coupled with SPP and HPP was applied to quantify and identify conservation gaps in Hubei Province. Although habitat quality is projected to improve, spatial heterogeneity and landscape fragmentation will intensify across the region. Multi‐period Marxan simulations revealed four major conservation gap clusters in interprovincial mountainous regions of Hubei Province.

In the marine environment Species Distribution Models (SDMs) have been used in hundreds of papers for predicting the present and future geographic range and environmental niche of species. We have analysed ways in which SDMs are being applied to marine species in order to recommend best practice in future studies. This systematic review was registered as a protocol on the Open Science Framework: https://osf.io/tngs6/. The literature reviewed (236 papers) was published between 1992 and July 2016. The number of papers significantly increased through time (R2=0.92, p<0.05). The studies were predominantly carried out in the Temperate Northern Atlantic (45%) followed by studies of global scale (11%) and studies in Temperate Australasia (10%). The majority of studies reviewed focused on theoretical ecology (37%) including investigations of biological invasions by non-native organisms, conservation planning (19%), and climate change predictions (17%). Most of the studies were published in ecological, multidisciplinary or biodiversity conservation journals. Most of the studies (94%) failed to report the amount of uncertainty derived from data deficiencies and model parameters. Best practice recommendations are proposed here to ensure that novice and advanced SDM users can (a) understand the main elements of SDMs, (b) reproduce standard methods and analysis, and (c) identify potential limitations with their data. We suggest that in the future, studies of marine SDMs should report on key features of the approaches employed, data deficiencies, the selection of the best explanatory model, and the approach taken to validate the SDM results. In addition, based on the literature reviewed, we suggest that future marine SDMs should account for uncertainty levels as part of the modelling process.

Species distribution models (SDMs) are often used to understand changes to species' distributions and their habitats under different land‐use scenarios, enabling decision makers to prioritize areas for management efforts and balance environmental conservation with socio‐economic demands on the landscape. However, the application of SDMs in land‐use planning and Environmental Impact Assessments (EIAs) remains limited due to challenges in interpreting and communicating continuous predictions resulting from these SDMs. Although different binarization methods have been used to overcome such challenges, the choice of threshold can profoundly alter the resulting binary habitat map, and most methods lack simplicity and require access to underlying species occurrence and environmental data used to develop the SDMs. Hence, there is a demand for testing simple alternative binarization methods to enable in‐house application of SDMs by practitioners and to facilitate interpretation and communication. Using SDMs of 103 boreal bird species in Alberta, Canada, we transform species relative abundance predictions of SDMs into direct estimates of habitat area, a proxy for habitat suitability, using four simple and three complex thresholding methods. We compare the performance of the binarized models for each bird species and between forest specialists vs. generalists under land‐use change scenarios. We found that thresholded models reflect losses in suitable habitat under industrial disturbance scenarios more realistically compared to continuous relative abundance models. Notably, simple thresholding methods, particularly the mean predicted relative abundance, performed similarly to complex thresholding methods in predicting suitable habitat areas, as indicated by model evaluations using the area under the curve. These findings suggest that using the mean as a binarization threshold can effectively bridge the gap between complex SDMs and their application in policy and planning, without sacrificing predictive accuracy. We conclude that simple threshold binarization methods, such as the mean, can leverage the strong predictive power of SDMs to provide insights into future changes in species' habitat during land‐use planning scenarios, account for their uncertainties and expand their utility to facilitate interpretation for science‐informed decision‐making in multifunctional landscapes.

The Mediterranean Basin has historically been subject to alien plant invasions that threaten its unique biodiversity. This seasonally dry and densely populated region is undergoing severe climatic and socioeconomic changes, and it is unclear whether these changes will worsen or mitigate plant invasions. Predictions are often biased, as species may not be in equilibrium in the invaded environment, depending on their invasion stage and ecological characteristics. To address future predictions uncertainty, we identified invasion hotspots across multiple biased modelling scenarios and ecological characteristics of successful invaders. We selected 92 alien plant species widespread in Mediterranean Europe and compiled data on their distribution in the Mediterranean and worldwide. We combined these data with environmental and propagule pressure variables to model global and regional species niches, and map their current and future habitat suitability. We identified invasion hotspots, examined their potential future shifts, and compared the results of different modelling strategies. Finally, we generalised our findings by using linear models to determine the traits and biogeographic features of invaders most likely to benefit from global change. Currently, invasion hotspots are found near ports and coastlines throughout Mediterranean Europe. However, many species occupy only a small portion of the environmental conditions to which they are preadapted, suggesting that their invasion is still an ongoing process. Future conditions will lead to declines in many currently widespread aliens, which will tend to move to higher elevations and latitudes. Our trait models indicate that future climates will generally favour species with conservative ecological strategies that can cope with reduced water availability, such as those with short stature and low specific leaf area. Taken together, our results suggest that in future environments, these conservative aliens will move farther from the introduction areas and upslope, threatening mountain ecosystems that have been spared from invasions so far.