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To predict the potential success of an invading non-native species, it is important to understand its dynamics and interactions with native species in the early stages of its invasion. In spatially implicit models, mathematical stability criteria are commonly used to predict whether an invading population grows in number in an early time period. But spatial context is important for real invasions as an invading population may first occur as a small number of individuals scatter spatially. The invasion dynamics are therefore not describable in terms of population level state variables. A better approach is spatially explicit individual-based modeling (IBM). We use an established spatially explicit IBM to predict the invasion of the non-native tree, Melaleuca quinquenervia (Cav.) Blake, to a native community in southern Florida. We show that the initial spatial distribution, both the spatial density of individuals and the area they cover, affects its success in growing numerically and spreading. The formation of a cluster of a sufficient number and density of individuals may be needed for the invader to locally outcompete the native species and become established. Different initial densities, identical in number and density but differing in random positions of individuals, can produce very different trajectories of the invading population through time, even affecting invasion success and failure. [ABSTRACT FROM AUTHOR]

Abstract Effective assessment of potential climate impacts requires the ability to rapidly predict the time‐varying response of climate variables. This prediction must be able to consider different combinations of forcing agents at high resolution. Full‐scale ESMs are too computationally intensive to run large scenario ensembles due to their long lead times and high costs. Faster approaches such as intermediate complexity modeling and pattern scaling are limited by low resolution and invariant response patterns, respectively. We propose a generalizable framework for emulating climate variables to overcome these issues, representing the climate system through spatially resolved impulse response functions. We derive impulse response functions by directly deconvolving effective radiative forcing and near‐surface air temperature time series. This enables rapid emulation of new scenarios through convolution and derivation of other impulse response functions from any forcing to its response. We present results from an application to near‐surface air temperature based on CMIP6 data. We evaluate emulator performance across 5 CMIP6 experiments including the SSPs, demonstrating accurate emulation of global mean and spatially resolved temperature change with respect to CMIP6 ensemble outputs. Global mean relative error in emulated temperature averages 1.49% in mid‐century and 1.25% by end‐of‐century. These errors are likely driven by state‐dependent climate feedbacks, such as the non‐linear effects of Arctic sea ice melt. We additionally show an illustrative example of our emulator for policy evaluation and impact analysis, emulating spatially resolved temperature change for a 1,000 member scenario ensemble in less than a second.

The pace of current climate change is expected to be problematic for alpine flora and fauna, as their adaptive capacity may be limited by small population size. Yet, despite substantial genetic drift following post-glacial recolonization of alpine habitats, alpine species are notable for their success surviving in highly heterogeneous environments. Population genomic analyses demonstrating how alpine species have adapted to novel environments with limited genetic diversity remain rare, yet are important in understanding the potential for species to respond to contemporary climate change. In this study, we explored the evolutionary history of alpine ground beetles in the Nebria ingens complex, including the demographic and adaptive changes that followed the last glacier retreat. We first tested alternative models of evolutionary divergence in the species complex. Using millions of genome-wide SNP markers from hundreds of beetles, we found evidence that the N. ingens complex has been formed by past admixture of lineages responding to glacial cycles. Recolonization of alpine sites involved a distributional range shift to higher elevation, which was accompanied by a reduction in suitable habitat and the emergence of complex spatial genetic structure. We tested several possible genetic pathways involved in adaptation to heterogeneous local environments using genome scan and genotype–environment association approaches. From the identified genes, we found enriched functions associated with abiotic stress responses, with strong evidence for adaptation to hypoxia-related pathways. The results demonstrate that despite rapid demographic change, alpine beetles in the N. ingens complex underwent rapid physiological evolution. [ABSTRACT FROM AUTHOR]

Generating spatial predictions of species distribution is a central task for research and policy. Currently, correlative species distribution models (cSDMs) are among the most widely used tools for this purpose. However, a fundamental assumption of cSDMs, that species distributions are in equilibrium with their environment, is rarely fulfilled in real data and limits the applicability of cSDMs for dynamic projections. Process‐based, dynamic SDMs (dSDMs) promise to overcome these limitations as they explicitly represent transient dynamics and enhance spatiotemporal transferability. Software tools for implementing dSDMs are becoming increasingly available, but their parameter estimation can be complex. Here, we test the feasibility of calibrating and validating a dSDM using long‐term monitoring data of Swiss red kites (Milvus milvus). This population has shown strong increases in abundance and a progressive range expansion over the last decades, indicating a nonequilibrium situation. We construct an individual‐based model using the RangeShiftR modeling platform and use Bayesian inference for model calibration. This allows the integration of heterogeneous data sources, such as parameter estimates from published literature and observational data from monitoring schemes, with a coherent assessment of parameter uncertainty. Our monitoring data encompass counts of breeding pairs at 267 sites across Switzerland over 22 years. We validate our model using a spatial‐block cross‐validation scheme and assess predictive performance with a rank‐correlation coefficient. Our model showed very good predictive accuracy of spatial projections and represented well the observed population dynamics over the last two decades. Results suggest that reproductive success was a key factor driving the observed range expansion. According to our model, the Swiss red kite population fills large parts of its current range but has potential for further increases in density. We demonstrate the practicality of data integration and validation for dSDMs using RangeShiftR. This approach can improve predictive performance compared to cSDMs. The workflow presented here can be adopted for any population for which some prior knowledge on demographic and dispersal parameters as well as spatiotemporal observations of abundance or presence/absence are available. The fitted model provides improved quantitative insights into the ecology of a species, which can greatly aid conservation and management efforts. [ABSTRACT FROM AUTHOR]

Studying range expansions is central for understanding genetic variation through space and time as well as for identifying refugia and biological invasions. Range expansions are characterized by serial founder events causing clines of decreasing genetic diversity away from the center of origin and asymmetries in the two-dimensional allele frequency spectra. These asymmetries, summarized by the directionality index (ψ), are sensitive to range expansions and persist for longer than clines in genetic diversity. In continuous and finite meta-populations, genetic drift tends to be stronger at the edges of the species distribution in equilibrium populations and populations undergoing range expansions alike. Such boundary effects are expected to affect geographic patterns in genetic diversity and ψ. Here we demonstrate that boundary effects cause high false positive rates in equilibrium meta-populations when testing for range expansions. In the simulations, the absolute value of ψ (| ψ |) in equilibrium data sets was proportional to the fixation index (F ST). By fitting signatures of range expansions as a function of ɛ | ψ |/ F ST and geographic clines in ψ , strong evidence for range expansions could be detected in data from a recent rapid invasion of the cane toad, Rhinella marina , in Australia, but not in 28 previously published empirical data sets from Australian scincid lizards that were significant for the standard range expansion tests. Thus, while clinal variation in ψ is still the most sensitive statistic to range expansions, to detect true signatures of range expansions in natural populations, its magnitude needs to be considered in relation to the overall levels of genetic structuring in the data. [ABSTRACT FROM AUTHOR]

Population trend estimates form the core of avian conservation assessments in North America and indicate important changes in the state of the natural world. The models used to estimate these trends would be more efficient and informative for conservation if they explicitly considered the spatial locations of the monitoring data. We created spatially explicit versions of some standard status and trend models applied to long-term monitoring data for birds across North America. We compared the spatial models to simpler non-spatial versions of the same models, fitting them to simulated data and real data from 3 broad-scale monitoring programs: the North American Breeding Bird Survey (BBS), the Christmas Bird Count, and a collection of programs we refer to as Migrating Shorebird Surveys. All the models generally reproduced the simulated trends and population trajectories when there were many data, and the spatial models performed better when there were fewer data and in locations where the local trends differed from the range-wide means. When fit to real data, the spatial models revealed interesting spatial patterns in trend, such as recent population increases along the Appalachian Mountains for the Eastern Whip-poor-will (Antrostomus vociferus), that were much less apparent in results from the non-spatial versions. The spatial models also had higher out-of-sample predictive accuracy than the non-spatial models for a selection of species using BBS data. The spatially explicit sharing of information allows fitting the models with much smaller strata, allowing for finer-grained patterns in trends. Spatially informed trends will facilitate more locally relevant conservation, highlight areas of conservation successes and challenges, and help generate and test hypotheses about the spatially dependent drivers of population change. [ABSTRACT FROM AUTHOR]

Abstract A central challenge in ecology is understanding the emergence of patterns as the result of interactions among individuals. Dynamic forest models can provide a fine‐scale description of the ecological, physiological and environmental processes that explain the demography of coexisting tree species. This in turn helps predict changes under future scenarios. However, model accessibility is a major obstacle to a wide use and communication across scientific disciplines and for educational purposes. Here, we present the R package rcontroll, which provides access to the TROLL forest simulator in the R environment. TROLL is individual‐based and spatially explicit and leverages knowledge of ecology, biogeochemistry and tree ecophysiology through a trait‐based parameterisation. TROLL has been used to simulate carbon fluxes and tree diversity in tropical and subtropical forests and to explore forest resilience to disturbance and environmental changes more generally. rcontroll provides a user‐friendly environment to set up and analyse TROLL simulations with varying community compositions, ecological parameters and climate conditions. We show how to test parameter sensitivity in TROLL using the rcontroll R package. We also demonstrate the flexibility and ease of use of rcontroll by replicating a previously published study based on the TROLL simulator. Both examples are included with reproducible code documents. Complex forest simulators are important scientific tools for science and education, and wide access to these tools is an important condition for their adoption. TROLL is designed to address a wide range of ecological and environmental questions, and the new R package rcontroll is designed to be an entry point for TROLL model users.

The economic viability of an urban area in terms of trade and shopping significantly impacts its residents’ quality of life and is crucial for any sustainable development initiative. Geographic information systems (GIS) are well established, but the use of GIS technology within finance and trade analysis is still in its infancy. In this article, we highlighted the potential of GIS technology and big data analytics and demonstrated the importance of thinking in spatial terms for analysing patterns within the trade and finance industries. We studied spatiotemporal trade and shopping patterns in the city of Tabriz using data generated by customer purchase transactions obtained from 5200 stores, shopping, business and service centres. We employed time series transaction data collected from the points of sale in stores, shopping, service and business centres located in different areas of the city. We applied four well known geospatial big data driven approaches including machine learning nearest neighbour, kernel density estimation, space–time pattern mining and spatiotemporal coupling tele-coupling for detecting and mapping of spatial trade hotspot patterns. The results of this study indicated the potential of GIScience methods for the explicit spatial mapping of trade and shopping patterns. The results revealed that the city centre, particularly the Bazaar of Tabriz, acts as the city’s heart of trade, and we identify additional major business hotspots. Furthermore, the results allow for studying the impacts of unbalanced urban development in Tabriz, where the wealthy suburbs with high quality of life, such as Valiasr and Elguli, host the major shopping hotspots. The spatial patterns obtained enable local stakeholders, decision makers and authorities to develop strategic plans for urban sustainable development in Tabriz. The geospatial big data approach used can stimulate novel and progressive research. Results of this study demonstrate methodological advancements in GIScience by ’spatializing’ individual purchase data and therefor proposing an explicit geospatial big data analysis approach.

• A model for SARS-CoV-2 with spatial heterogeneity, mobility, variants of concern, seasonality, and vaccination is built. • The model can be used to generate projections for policymakers and to study the influence of mobility on SARS-CoV-2 spread. • Incorporating mobility is not necessary to obtain an accurate description of the 2020–2021 SARS-CoV2 pandemic in Belgium. • Adding spatial heterogeneity to the model results in greater uncertainty on the model projections for policymakers. • Lowering social contact is a more efficient strategy than lowering mobility to contain an epidemic in a given spatial patch. In this work, we extend our previously developed compartmental SEIQRD model for SARS-CoV-2 in Belgium. We introduce SARS-CoV-2 variants of concern, vaccines, and seasonality in our model, as their addition has proven necessary for modelling SARS-CoV-2 transmission dynamics during the 2020–2021 COVID-19 pandemic in Belgium. The model is geographically stratified into eleven spatial patches (provinces), and a telecommunication dataset provided by Belgium's biggest operator is used to incorporate interprovincial mobility. We calibrate the model using the daily number of hospitalisations in each province and serological data. We find the model adequately describes these data, but the addition of interprovincial mobility was not necessary to obtain an accurate description of the 2020–2021 SARS-CoV-2 pandemic in Belgium. We further demonstrate how our model can be used to help policymakers decide on the optimal timing of the release of social restrictions. We find that adding spatial heterogeneity by geographically stratifying the model results in more uncertain model projections as compared to an equivalent nation-level model, which has both communicative advantages and disadvantages. We finally discuss the impact of imposing local mobility or social contact restrictions to contain an epidemic in a given province and find that lowering social contact is a more effective strategy than lowering mobility. [ABSTRACT FROM AUTHOR]

A central challenge in ecology is understanding the emergence of patterns as the result of interactions among individuals. Dynamic forest models can provide a fine‐scale description of the ecological, physiological and environmental processes that explain the demography of coexisting tree species. This in turn helps predict changes under future scenarios. However, model accessibility is a major obstacle to a wide use and communication across scientific disciplines and for educational purposes.Here, we present the R package rcontroll, which provides access to the TROLL forest simulator in the R environment. TROLL is individual‐based and spatially explicit and leverages knowledge of ecology, biogeochemistry and tree ecophysiology through a trait‐based parameterisation. TROLL has been used to simulate carbon fluxes and tree diversity in tropical and subtropical forests and to explore forest resilience to disturbance and environmental changes more generally. rcontroll provides a user‐friendly environment to set up and analyse TROLL simulations with varying community compositions, ecological parameters and climate conditions.We show how to test parameter sensitivity in TROLL using the rcontroll R package. We also demonstrate the flexibility and ease of use of rcontroll by replicating a previously published study based on the TROLL simulator. Both examples are included with reproducible code documents.Complex forest simulators are important scientific tools for science and education, and wide access to these tools is an important condition for their adoption. TROLL is designed to address a wide range of ecological and environmental questions, and the new R package rcontroll is designed to be an entry point for TROLL model users. [ABSTRACT FROM AUTHOR]

Sustainable water management requires an in-depth understanding of dissolved inorganic nitrogen (DIN) in watersheds. However, it remains challenging in the context of intensifying human-induced and naturally impacted stresses in a changing environment. A spatially explicit interpretable machine-learning method was proposed based on the annual variation in land use/cover, Catchment Land Surface Model, Noah land surface model, population dynamics, and nightlight in a coastal Chinese watershed. The proposed model was effective for delineating DIN processes and sources in watersheds. The d (index of agreement), R2 (coefficient of determination), |PBIAS| (percent bias), and KGE (Kling-Gupta efficiency)during the training period were 0.91–0.98, 0.76–0.96, 0.38–3.12, and 0.73–0.87, respectively. The d, R2, |PBIAS|, and KGE during the testing period were 0.85–0.94, 0.56–0.79, 3.59–6.18, and 0.65–0.86, respectively. NH4+-N in the watersheds may be strongly related to the effects of urbanization within the watersheds while the agricultural activities may modify the patterns of NO3–-N in the watersheds. The NH4+-N was highly related to the urbanization effects, which contributed 20 %–30 % of the riverine NH4+-N in the North River. In contrast, agricultural activities may modify patterns of NO3–-N in watersheds, and agricultural activities in the West River contributed 50 %–70 % riverine NO3–-N in the watershed. Moreover, urbanization may alter the water content, soil properties, and regional climate patterns within the watersheds, while repeated DIN input through agricultural activities in the agricultural watersheds may change the nitrogen processes in the soil and groundwater. Paired with agricultural activities, dams or reservoirs may amplify NH4+-N to watersheds with modifying the advection and diffusion of DIN from agricultural activities, and increasing sediments in the watersheds. This study demonstrated the potentials of the proposed method in tracking DIN pollution and provided new insights into nutrient management in the watersheds under intensifying human-nature interactions.

Crown development is closely related to the biomass and growth rate of the tree and its width (CW) is an important covariable in growth and yield models and in forest management. To date, various CW models have been proposed. However, limited studies have explicitly focused on additive and inherent correlation of crown components and total CW as well as the influence of competition on crown radius from the corresponding direction. In this study, two model systems were used, i.e., aggregation method system (AMS) and disaggregation method system (DMS), to develop crown width additive model systems. For calculating spatially explicit competition index (CI), four neighbor tree selection methods were evaluated. CI was decomposed into four cardinal directions and added into the model systems. Results show that the power model form was more proper for our data to fit CW growth. For each crown radius and total CW, height to the diameter at breast height (HDR) and basal area of trees larger than the subject tree (BAL) significantly contributed to the increase of prediction accuracy. The 3-m fixed radius was optimal among the four neighborhoods selection ways. After adding decomposed competition Hegyi index into model systems AMS and DMS, the prediction accuracy improved. Of the model systems evaluated, AMS based on decomposed CI provided the best performance as well as the inherent correlation and additivity properties. Our study highlighted the importance of decomposed CI in tree CW modelling for additive model systems. This study focused on methodology and could be applied to other species or stands. [ABSTRACT FROM AUTHOR]

Exploring the complex relationship between ecological restoration and economic development is valuable for decision makers to formulate policy for sustainable development. The large-scale environmental restoration program—Grain for Green—was mainly implemented in the Loess Plateau of China to improve the soil retention service. However, whether this world-famous program affects local economic development has not been fully explored. In this study, using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and spatializing the gross domestic product (GDP) based on the remotely sensed nightlight data, we explored the tradeoff between environment (i.e., soil retention service) and economy (i.e., GDP) for the Loess Plateau in a spatially explicit way. We found that the soil retention service increased prominently over the past 40 years, especially after implementing the Grain for Green project. Meanwhile, the GDP increased about nine-fold over the past four decades from 4.52 to 40.29 × 107 USD. A win–win situation of soil retention and economic development was achieved in the Loess Plateau of China, particularly in the loess gully and loess hilly gully regions of the Loess Plateau. The win–win situation of soil retention and economic development was as a result of the Grain for Green program, the optimization of industrial structure, and the increase in non-agriculture employment. Compared with previous studies, more spatial information was available for the Loess Plateau in this study, which is more valuable to policymakers. [ABSTRACT FROM AUTHOR]

Tree to tree interactions are important structuring mechanisms for forest community dynamics. Forest management takes advantage of competition effects on tree growth by removing or retaining trees to achieve management goals. Both competition and silviculture have, thus, a strong effect on density and distribution of tree related microhabitats which are key features for forest taxa at the stand scale. In particular, spatially-explicit data to understand patterns and mechanisms of tree-related microhabitats formation in forest stands are rare. To train and eventually improve decision-making capacities related to the integration of biodiversity aspects into forest management plot of one hectare, so called marteloscopes were established in the frame of the ‘European Integrate Network’. In each plot, a set of data is collected at the individual tree level and stored in a database, the ‘I+ repository’. The 'I+ repository' is a centralised online database which serves for maintaining the data of all marteloscope plots. A subset of this repository was made publicly available via the Global Biodiversity Information Facility, based on a data-sharing policy. Data included are tree location in plot, tree species, forest mensuration data (diameter at breast height [cm], tree height [m]), tree status (living or standing dead) and tree-related microhabitats. Further, a visual assessment of timber quality classes is performed in order to provide an estimate of the economic value (market price) for each tree. This information is not part of the GBIF dataset.Currently 42,078 individual tree observations from 111 plots are made available via the Global Biodiversity Information Facility (GBIF). As the network of plots continues to expand, so does the database of tree-related microhabitats. Therefore, the database will undergo a regular update. The current version has a temporal coverage from March 2014 to December 2020. The innovation of this unique dataset is that it is based on a commonly agreed catalogue of tree microhabitats as a field reference list when assessing assessment protocol. The reference list is available in 17 languages and, thus, helps to guarantee compatibility of tree-related microhabitat assessments across countries and plots.

Abstract With rising demand for wood pellets from the US Southeast (US SE), the environmental limits to additional biomass demand are increasingly questioned. This study analyses the impact of increased pellet production in the US SE until 2030 on feedstock allocation, carbon flux in forest areas and costs of pre‐treatment and transport of feedstock and pellets. This by linking locations of forest biomass supply and demand through supply‐side logistics, allocating feedstock based on lowest costs of pre‐treatment, transport of feedstock and pellets, for the entire wood products sector. The impact is analysed for different scenarios with varied pellet production levels, additional inclusion of logging residues and optimization either on costs or on maintaining total carbon stock in sourcing areas of new pellet mills. In a scenario of 20 Mt pellet production, the roundwood share increases from 0% in 2020 to 37% pulplogs and 11% sawlogs in 2030. Costs increase with 57% towards 2030 compared to 2020, largely because of higher costs for pulplogs and sawlogs. In a scenario without pellet production, forest carbon removal in the US SE is 3 Mt CO2/year lower than in 2020. In the Reference scenario, additional carbon removal of 6, 21 and 38 Mt CO2/year is observed for 10, 20 and 30 Mt pellet production, respectively. In all cases, the forests of the US SE remain a net sink until 2030. The impact of a selection criteria for new pellet mill locations based on keeping local growth/drain ratios above 1 in sourcing areas is small since this mostly results in displacement of impacts and does not affect the total feedstock availability. Additional mobilization of logging residues is a key strategy to reduce carbon impacts, resulting in a smaller additional flux of 2, 11 and 29 Mt CO2/year for 10–30 Mt pellet production.

Capacity expansion optimisation is a widely used techno-economic analysis particularly on topics related to climate change mitigation and renewable energy transition. Using optimisation models to investigate capacity expansion in regions that potentially require significant grid infrastructure development requires incorporation of grid expansion problem within the optimisation. This study presents the development of SELARU, a spatially explicit optimisation model that incorporates the economies of scale of grid expansion using contextualized geographical feature to form the model's high-resolution spatial units. The model is used to investigate the case study of Indonesia using various spatial treatments to demonstrate the impact of detailed spatial depiction of grid expansion. Results reveal significant difference in renewable energy deployment trajectory (up to 2272 % increase in new generation capacity) between high-resolution spatial depiction of grid expansion vis-à-vis non spatially explicit energy system optimisation. Due to its high-resolution, SELARU also generates detailed information on the geographical extent of grid expansion requirement, which provides more realistic insights on governance challenges of renewable energy transition. Careful consideration of spatial representation is crucial when optimisation model is used to evaluate scenarios that concern technology selection such as renewable energy deployment or climate change mitigation. • We develop SELARU—a spatially explicit capacity expansion optimisation model. • SELARU incorporates economy of scale consideration in generating model results. • Grid expansion spatial representation changes technology selection in model output. • Findings from power sector example also apply to multiple energy carrier systems. • Computing demand requires trade-off between spatial and temporal resolution. [ABSTRACT FROM AUTHOR]

Invasive plant species alter community dynamics and ecosystem properties, potentially leading to regime shifts. Here, the invasion of a non-native tree species into a stand of native tree species is simulated using an agent-based model. The model describes an invasive tree with fast growth and high seed production that produces litter with a suppressive effect on native seedlings, based loosely on Melaleuca quinquenervia, invasive to southern Florida. The effect of a biocontrol agent, which reduces the invasive tree's growth and reproductive rates, is included to study how effective biocontrol is in facilitating the recovery of native trees. Even under biocontrol, the invader has some advantages over native tree species, such as the ability to tolerate higher stem densities than the invaded species and its litter's seedling suppression effect. We also include a standing dead component of both species, where light interception from dead canopy trees influences neighboring tree demographics. The model is applied to two questions. The first is how the mean seedling dispersal rate affects the spread of the invading species into a pure stand of natives, assuming the same mean dispersal distance for both species. For assumed litter seedling suppression that roughly balances the fitness levels of the two species, which species dominates depends on the mean dispersal distance. The invader dominates at both very high and very low mean seedling dispersal distances, while the native tree dominates for dispersal distances in the intermediate range. The second question is how standing dead trees affect either the rate of spread of the invader or the rate of recovery of the native species. The legacy of standing dead invasive trees may delay the recovery of native vegetation. The results here are novel and show that agent-based modeling is essential in illustrating how the fine-scale modeling of local interactions of trees leads to effects at the population level.

Background Tree to tree interactions are important structuring mechanisms for forest community dynamics. Forest management takes advantage of competition effects on tree growth by removing or retaining trees to achieve management goals. Both competition and silviculture have, thus, a strong effect on density and distribution of tree related microhabitats which are key features for forest taxa at the stand scale. In particular, spatially-explicit data to understand patterns and mechanisms of tree-related microhabitats formation in forest stands are rare. To train and eventually improve decision-making capacities related to the integration of biodiversity aspects into forest management plot of one hectare, so called marteloscopes were established in the frame of the 'European Integrate Network'. In each plot, a set of data is collected at the individual tree level and stored in a database, the 'I+ repository'. The 'I+ repository' is a centralised online database which serves for maintaining the data of all marteloscope plots. A subset of this repository was made publicly available via the Global Biodiversity Information Facility, based on a data-sharing policy. Data included are tree location in plot, tree species, forest mensuration data (diameter at breast height [cm], tree height [m]), tree status (living or standing dead) and tree-related microhabitats. Further, a visual assessment of timber quality classes is performed in order to provide an estimate of the economic value (market price) for each tree. This information is not part of the GBIF dataset. New information Currently 42,078 individual tree observations from 111 plots are made available via the Global Biodiversity Information Facility (GBIF). As the network of plots continues to expand, so does the database of tree-related microhabitats. Therefore, the database will undergo a regular update. The current version has a temporal coverage from March 2014 to December 2020. The innovation of this unique dataset is that it is based on a commonly agreed catalogue of tree microhabitats as a field reference list when assessing assessment protocol. The reference list is available in 17 languages and, thus, helps to guarantee compatibility of tree-related microhabitat assessments across countries and plots. [ABSTRACT FROM AUTHOR]

African swine fever virus (ASFv) is a virulent pathogen that threatens domestic swine industries globally and persists in wild boar populations in some countries. Persistence in wild boar can challenge elimination and prevent disease‐free status, making it necessary to address wild swine in proactive response plans. In the United States, invasive wild pigs are abundant and found across a wide range of ecological conditions that could drive different epidemiological dynamics among populations. Information on the size of the control areas required to rapidly eliminate the ASFv in wild pigs and how this area should change with management constraints and local ecology is needed to optimize response planning. We developed a spatially explicit disease transmission model contrasting wild pig movement and contact ecology in two ecosystems in Southeastern United States. We simulated ASFv spread and determined the optimal response area (reported as the radius of a circle) for eliminating ASFv rapidly over a range of detection times (when ASFv was detected relative to the true date of introduction), culling capacities (proportion of wild pigs in the culling zone removed weekly) and wild pig densities. Large radii for response areas (14 km) were needed under most conditions but could be shortened with early detection (≤ 8 weeks) and high culling capacities (≥ 15% weekly). Under most conditions, the ASFv was eliminated in less than 22 weeks using optimal control radii, although ecological conditions with high rates of wild pig movement required higher culling capacities (≥ 10% weekly) for elimination within 1 year. The results highlight the importance of adjusting response plans based on local ecology and show that wild pig movement is a better predictor of the optimal response area than the number of ASFv cases early in the outbreak trajectory. Our framework provides a tool for determining optimal control plans in different areas, guiding expectations of response impacts, and planning resources needed for rapid elimination. [ABSTRACT FROM AUTHOR]

With rising demand for wood pellets from the US Southeast (US SE), the environmental limits to additional biomass demand are increasingly questioned. This study analyses the impact of increased pellet production in the US SE until 2030 on feedstock allocation, carbon flux in forest areas and costs of pre‐treatment and transport of feedstock and pellets. This by linking locations of forest biomass supply and demand through supply‐side logistics, allocating feedstock based on lowest costs of pre‐treatment, transport of feedstock and pellets, for the entire wood products sector. The impact is analysed for different scenarios with varied pellet production levels, additional inclusion of logging residues and optimization either on costs or on maintaining total carbon stock in sourcing areas of new pellet mills. In a scenario of 20 Mt pellet production, the roundwood share increases from 0% in 2020 to 37% pulplogs and 11% sawlogs in 2030. Costs increase with 57% towards 2030 compared to 2020, largely because of higher costs for pulplogs and sawlogs. In a scenario without pellet production, forest carbon removal in the US SE is 3 Mt CO2/year lower than in 2020. In the Reference scenario, additional carbon removal of 6, 21 and 38 Mt CO2/year is observed for 10, 20 and 30 Mt pellet production, respectively. In all cases, the forests of the US SE remain a net sink until 2030. The impact of a selection criteria for new pellet mill locations based on keeping local growth/drain ratios above 1 in sourcing areas is small since this mostly results in displacement of impacts and does not affect the total feedstock availability. Additional mobilization of logging residues is a key strategy to reduce carbon impacts, resulting in a smaller additional flux of 2, 11 and 29 Mt CO2/year for 10–30 Mt pellet production. [ABSTRACT FROM AUTHOR]

Water temperature is an abiotic master variable for the survival of aquatic organisms. Global warming alters the thermal regimes of rivers and, thus, poses a threat to freshwater biodiversity. To address the impacts of water temperature changes related to global warming on freshwater fish species in life cycle assessment (LCA), we developed spatially explicit characterization factors (CFs) for 207 greenhouse gases under four representative concentration pathways. We calculated fate factors by using the output of a global hydrological model fully coupled with a dynamic water temperature model. We developed six species sensitivity distribution curves for two thermal effects (i.e., lethal and sub-lethal) to derive effect factors, which take the differences in sensitivity between climate regions into account. The regional CFs for CO2 ranged from 2.91 × 10−22 to 6.53 × 10−18 PAF·yr/kg for sub-lethal effects and from 1.98 × 10−22 to 4.58 × 10−18 PDF·yr/kg for lethal effects, depending on the river watersheds and future climate scenarios. To identify the contribution of regional impacts on freshwater fish to their potential global extinction, the regional CFs were converted into global CFs. The largest CFs always occur in the tropical watersheds. The regional impacts in the Amazon watershed contribute the most to the global freshwater fish species extinction. This study contributes to assessing the potential impacts on freshwater biodiversity from global warming from a new cause-effect pathway in LCA.

A modeling framework was created for the development of spatially explicit aquatic exposure models for any region or country of interest for chemicals disposed of down the drain. The framework relies on globally available data sets for river flow and population, and locally available data sets for wastewater treatment infrastructure and domestic water use, and leverages the iSTREEM® chemical routing algorithm. The framework was applied to China and Japan as case study countries. Spatially explicit population data were obtained from WorldPop. River flows covering the spatial extent of the two countries were derived from a high‐resolution surface runoff gridded data set that was based on the Curve Number approach and combined with the hydrology network for catchments and rivers from HydroBASINS and HydroSHEDS data sets. Publicly available data from government sources were used for estimating per capita water use and wastewater treatment infrastructure. To demonstrate the framework, the China model was used to predict the levels of the antifungal agent climbazole in rivers across the country, and the Japan model was used to predict river concentrations of linear alkylbenzene sulfonate. For both chemicals, the comparison of measured to modeled values showed good agreement, using linear regression analysis (R2 ≥ 0.96). The framework presented in this study provides a systematic and robust approach to develop spatially resolved exposure models that can be extrapolated to any country or region, allowing more accurate risk assessment of chemicals disposed down the drain by leveraging concentration distributions generated by the model. Integr Environ Assess Manag 2022;18:722–733. © 2021 SETAC Key Points: Modeling framework created for the development of spatially explicit aquatic exposure models for any region or country of interest for chemicals disposed of down the drain.Framework relies on the use of a high‐resolution global flow dataset developed as part of this research.Framework also relies on globally available datasets for population and locally available datasets for wastewater treatment infrastructure and domestic water use.Model framework was successfully applied to two case study countries, China and Japan, which differ significantly in population density, river flow (due to climate and geography), and wastewater treatment infrastructure. [ABSTRACT FROM AUTHOR]

Wild bee species are important pollinators in agricultural landscapes. However, population decline was reported over the last decades and is still ongoing. While agricultural intensification is a major driver of the rapid loss of pollinating species, transition zones between arable fields and forest or grassland patches, i.e., agricultural buffer zones, are frequently mentioned as suitable mitigation measures to support wild bee populations and other pollinator species. Despite the reported general positive effect, it remains unclear which amount of buffer zones is needed to ensure a sustainable and permanent impact for enhancing bee diversity and abundance. To address this question at a pollinator community level, we implemented a process‐based, spatially explicit simulation model of functional bee diversity dynamics in an agricultural landscape. More specifically, we introduced a variable amount of agricultural buffer zones (ABZs) at the transition of arable to grassland, or arable to forest patches to analyze the impact on bee functional diversity and functional richness. We focused our study on solitary bees in a typical agricultural area in the Northeast of Germany. Our results showed positive effects with at least 25% of virtually implemented agricultural buffer zones. However, higher amounts of ABZs of at least 75% should be considered to ensure a sufficient increase in Shannon diversity and decrease in quasi‐extinction risks. These high amounts of ABZs represent effective conservation measures to safeguard the stability of pollination services provided by solitary bee species. As the model structure can be easily adapted to other mobile species in agricultural landscapes, our community approach offers the chance to compare the effectiveness of conservation measures also for other pollinator communities in future. [ABSTRACT FROM AUTHOR]

Advanced biofuels produced from lignocellulosic crops grown on marginal lands can become an important part of the European Union (EU) climate change mitigation strategy to reduce CO2 emissions and meet biofuel demand. This study quantifies spatially explicit the availability of marginal land in the EU, its production biomass potentials for eight different crops, and the greenhouse gas (GHG) performance of advanced biofuel supply chains. Available land is mapped based on land marginality and Renewable Energy Directive Recast (REDII) land‐related sustainability criteria. Biomass potentials are assessed with a water‐use‐to‐biomass‐production approach while considering the available land, location‐specific biophysical conditions and crop‐specific phenological characteristics. The GHG balance of advanced biofuels from energy crops produced on marginal lands is assessed considering both land‐related carbon stock changes and supply chain emissions with the carbon footprint approach from the REDII. Available marginal land that meets REDII criteria is projected at 20.5–21 Mha 2030 and 2050, respectively. Due to biophysical limitations, not all available land is suitable for energy crop production. The maximum biomass potential of lignocellulosic energy crops (optimal crop choice with maximum yield for each available location) varies between 1951 PJ year−1 in 2030 and 2265 PJ year−1 in 2050. The GHG emission performance (net emissions) of different advanced biofuel supply chains varies on average between −32 g CO2‐eq MJfuel−1 for poplar/willow diesel to 38 g CO2‐eq MJfuel‐1 for reed canary grass renewable jet fuel. The large variability in GHG performance is strongly determined by the spatial heterogeneity, which dictates the type of feedstock produced under specific local biophysical conditions, the crop characteristics, and the best conversion pathway. Negative GHG emissions are related to increased carbon stocks for the biomass and soil organic carbon pools compared to the land prior to conversion. When for each location, the advanced biofuel supply chain with the highest GHG performance (lowest net GHG emissions) is selected, 618 PJ year−1 of advanced biofuels can be produced by 2030. Under REDII GHG emission criteria, slightly less (552 PJ year−1) is viable. Smart choices on location, crop type and supply chain design are paramount to achieve maximum benefits of bioenergy systems. [ABSTRACT FROM AUTHOR]

Ecological models are constrained by the availability of high‐quality data at biologically appropriate resolutions and extents. Modeling a species' affinity or aversion with a particular land cover class requires data detailing that class across the full study area. Data sets with detailed legends (i.e., high thematic resolution) and/or high accuracy often sacrifice geographic extent, while large‐area data sets often compromise on the number of classes and local accuracy. Consequently, ecologists must often restrict their study extent to match that of the more precise data set, or ignore potentially key land cover associations to study a larger area. We introduce a hierarchical Bayesian model to capitalize on the thematic resolution and accuracy of a regional land cover data set, and on the geographic breadth of a large area land cover data set. For the full extent (i.e., beyond the regional data set), the model predicts systematic discrepancies of the large‐area data set with the regional data set, and divides an aggregated class into two more specific classes detailed by the regional data set. We illustrate the application of our model for mapping eastern white pine (Pinus strobus) forests, an important timber species that also provides habitat for an invasive shrub in the northeastern United States. We use the National Land Cover Database (NLCD), which covers the full study area but includes only generalized forest classes, and the NH GRANIT land cover data set, which maps White Pine Forest and has high accuracy, but only exists within New Hampshire. We evaluate the model at coarse (20 km2) and fine (2 km2) resolutions, with and without spatial random effects. The hierarchical model produced improved maps of compositional land cover for the full extent, reducing inaccuracy relative to NLCD while partitioning a White Pine Forest class out of the Evergreen Forest class. Accuracy was higher with spatial random effects and at the coarse resolution. All models improved upon simply partitioning Evergreen Forest in NLCD based on the predicted distribution of white pine. This flexible statistical method helps ecologists leverage localized mapping efforts to expand models of species distributions, population dynamics, and management strategies beyond the political boundaries that frequently delineate land cover data sets. [ABSTRACT FROM AUTHOR]

Predicting biological invasions remains a challenge to applied ecologists and limits pre‐emptive management of biosecurity threats. In the last decade, spotted‐wing drosophila Drosophila suzukii has emerged as an internationally significant agricultural pest as it rapidly spread across Europe and the Americas. However, the underlying drivers of its global invasion remain unstudied, while countries like Australia, presently free from D. suzukii, require robust estimates of spread and establishment potential to aid development of effective preparedness strategies.Here, we analysed the ecoclimatic and human‐mediated drivers of the global invasion of D. suzukii to understand historical spread patterns and improve forecasts of future spread potential. Using a modular approach, climate‐driven population dynamics were linked in space via dispersal processes to simulate spread at continental scales. Combined with biological parameters measured in laboratory studies, the spread model was parameterized and validated on international spread data.Model accuracy was high and was able to predict 83% of regional presence–absence through time in the United States and, without further model fitting, 78% of the variation in the Europe incursion. Omitting human‐assisted spread from the model reduced predictability by over 20%, highlighting the large anthropogenic influence in this modern biological invasion. Economic activity (GDP) rather than human population density was more strongly associated with human‐mediated spread. Simulations predicted that eastern Australian coastal regions, particularly those near major cities with high economic activity, will result in the fastest spread of D. suzukii.Synthesis and applications. Incursions of Drosophila suzukii into Australia will have significant consequences for horticultural industries with the predicted speed of spread making eradication programs extremely difficult. However, the identified areas of significant fruit production, and high environmental suitability and economic activity will form a logical means for prioritizing industry preparedness. In light of our findings, a key component of preparedness strategies will be the ability of fruit producers to rapidly transition to effective management of D. suzukii. [ABSTRACT FROM AUTHOR]

Wildlife viewing within protected areas is an increasingly popular recreational activity. Management agencies are often tasked with providing these opportunities, yet quantitative analyses of factors influencing wildlife sightings are lacking. We analyzed locations of GPS-collared wolves and wolf sightings from 2945 trips in Denali National Park and Preserve, Alaska, USA, to provide a mechanistic understanding of how viewing opportunities are influenced by attributes of wolves and physical, biological, and harvest characteristics. We found that the presence of masking vegetation, den site proximity to the road, pack size, and presence of a wolf harvest closure adjacent to the park affected wolf sightings, and the influence of den proximity on sightings depended on harvest management. Wolf sightings increased with den site proximity to the road in years with a harvest closure adjacent to the park but not in the absence of the closure. The effect of the harvest closure on sightings was similar in magnitude to an increase in pack size by two wolves or a more than a two-fold decrease in masking vegetation. These findings were consistent across a 10-fold change in spatial resolution. Quantitative analysis of the factors influencing wildlife sightings provides valuable insight for agencies tasked with managing viewing opportunities. [ABSTRACT FROM AUTHOR]

The Perdido Key beach mouse (Peromyscus polionotus trissyllepsis), Choctawhatchee beach mouse (P. p. allophrys), and St. Andrew beach mouse (P. p. peninsularis) are 3 federally endangered subspecies that inhabit coastal dunes of Alabama and Florida, USA. Conservation opportunities for these subspecies are limited and costly. Consequently, well‐targeted efforts are required to achieve their downlisting criteria. To aid the development of targeted management scenarios that are designed to achieve downlisting criteria, we developed a Bayesian network model that uses habitat characteristics to predict the probability of beach mouse presence at a 30‐m resolution across a portion of the Florida Panhandle. We then designed alternative management scenarios for a variety of habitat conditions for coastal dunes. Finally, we estimated how much area is needed to achieve the established downlisting criterion (i.e., habitat objective) and the amount of effort needed to achieve the habitat objective (i.e., management efficiency). The results suggest that after 7 years of post‐storm recolonization, habitat objectives were met for Perdido Key (within its Florida critical habitat) and Choctawhatchee beach mice. The St. Andrew beach mouse required 5.14 km2 of additional critical habitat to be protected and occupied. The St. Andrew beach mouse habitat objective might be achieved by first restoring protected critical habitat to good dune conditions and then protecting or restoring the unprotected critical habitat with the highest predicted probability of beach mouse presence. This scenario provided a 28% increase in management efficiency compared to a scenario that randomly protected or restored undeveloped unprotected critical habitat. In total, when coupled with established downlisting criteria, these quantitative and spatial decision support tools could provide insight into how much habitat is available, how much more is needed, and targeted conservation or restoration efforts that might efficiently achieve habitat objectives. © 2020 The Wildlife Society. This study aids coastal habitat managers by providing quantitative, spatially explicit decision support tools that estimate how much beach mouse habitat is available and how much more is needed to achieve established downlisting criteria related to habitat. These results could also guide selection of specific management actions, where those actions might be taken, and over how much critical habitat they must be applied. [ABSTRACT FROM AUTHOR]

Inland waters (rivers, lakes, and reservoirs) and wetlands (marshes and coastal wetlands) represent large and continuous sources of nitrous oxide (N 2 O) emissions, in view of adequate biomass and anaerobic conditions. Considerable uncertainties remain in quantifying spatially explicit N 2 O emissions from aquatic systems, attributable to the limitations of models and a lack of comprehensive data sets. Herein, we conducted a synthesis of 1659 observations of N 2 O emission rates to determine the major environmental drivers across five aquatic systems. A framework for spatially explicit estimates of N 2 O emissions in China was established, employing a data-driven approach that upscaled from site-specific N 2 O fluxes to robust multiple-regression models. Results revealed the effectiveness of models incorporating soil organic carbon and water content for marshes and coastal wetlands, as well as water nitrate concentration and dissolved organic carbon for lakes, rivers, and reservoirs for predicting emissions. Total national N 2 O emissions from inland waters and wetlands were 1.02 × 10 5 t N 2 O yr -1 , with contributions from marshes (36.33%), rivers (27.77%), lakes (25.27%), reservoirs (6.47%), and coastal wetlands (4.16%). Spatially, larger emissions occurred in the Songliao River Basin and Continental River Basin, primarily due to their substantial terrestrial biomass. This study offers a vital national inventory of N 2 O emissions from inland waters and wetlands in China, providing paradigms for the inventorying work in other countries and insights to formulate effective mitigation strategies for climate change.

Abstract Background We determine the potential of forests and the forest sector to mitigate greenhouse gas (GHG) emissions by changes in management practices and wood use for two regions within Canada’s managed forest from 2018 to 2050. Our modeling frameworks include the Carbon Budget Model of the Canadian Forest Sector, a framework for harvested wood products that estimates emissions based on product half-life decay times, and an account of marginal emission substitution benefits from the changes in use of wood products and bioenergy. Using a spatially explicit forest inventory with 16 ha pixels, we examine mitigation scenarios relating to forest management and wood use: increased harvesting efficiency; residue management for bioenergy; reduced harvest; reduced slashburning, and more longer-lived wood products. The primary reason for the spatially explicit approach at this coarse resolution was to estimate transportation distances associated with delivering harvest residues for heat and/or electricity production for local communities. Results Results demonstrated large differences among alternative scenarios, and from alternative assumptions about substitution benefits for fossil fuel-based energy and products which changed scenario rankings. Combining forest management activities with a wood-use scenario that generated more longer-lived products had the highest mitigation potential. Conclusions The use of harvest residues to meet local energy demands in place of burning fossil fuels was found to be an effective scenario to reduce GHG emissions, along with scenarios that increased the utilization level for harvest, and increased the longevity of wood products. Substitution benefits from avoiding fossil fuels or emissions-intensive products were dependent on local circumstances for energy demand and fuel mix, and the assumed wood use for products. As projected future demand for biomass use in national GHG mitigation strategies could exceed sustainable biomass supply, analyses such as this can help identify biomass sources that achieve the greatest mitigation benefits.

Transition from conventional energy into a system based on renewable energies was decided in Germany in 2002. Its implementation was accompanied by a controversial discussion about food safety, biodiversity, and landscape change. This study assesses potential changes in skylark occurrence caused by a spatial expansion of bioenergy crops in the administrative district of Schwäbisch Hall, Germany, using a Generalised Linear Habitat Model approach combined with remotely sensed land use information. Predictions for the occurrence of skylarks were developed for the land use distribution in 2011, and a close to reality bioenergy scenario with reduced crops. Prediction of skylark territories based on the land use classification of 2011 resulted in 46 269 territories. Skylark territories for the bioenergy scenario resulted in 36 472 territories, which as 8797 fewer skylark territories, represents a decline of nearly 20%. Our study helps elucidate and quantify the effects of spatial extension of bioenergy crops on skylark. [ABSTRACT FROM AUTHOR]

Anticipating and preparing for the effect of environmental changes on biodiversity requires to understand and predict both the ecological and evolutionary responses of populations. Tools and methods to efficiently integrate these complex processes are lacking.We present the genetically and spatially explicit individual‐based simulation software Nemo‐age combining ecological and evolutionary processes. Nemo‐age has a strong emphasis on modelling complex life histories. We here provide a methodology to predict changes in species distribution for given climate projections using Nemo‐age.Modelling complex life histories, spatial distribution and evolutionary processes unravel possible eco‐evolutionary mechanisms that have been previously overlooked when populations endure rapid environmental changes.The interface of Nemo‐age is designed to integrate species' data from different fields, from demography to genetic architecture and spatial distributions, thus representing a versatile tool to model a variety of applied and theoretical scenarios. [ABSTRACT FROM AUTHOR]

Neutral theory proposes that some macroscopic biodiversity patterns can be explained in terms of drift, speciation and immigration, without invoking niches. There are many different varieties of neutral model, all assuming that the fitness of an individual is unrelated to its species identity. Variants that are spatially explicit provide a means for making quantitative predictions about spatial biodiversity patterns.We present software packages that make spatially explicit neutral simulations straightforward and efficient. The packages allow the user to customize both dispersal and landscape structure in a wide variety of ways. We provide a Python package pycoalescence and a functionally equivalent R package rcoalescence. In both packages, the core routines are written in C++ and make use of coalescence methods to optimize performance.We explain the technical details of the packages and give examples for their application, with a particular focus on two scenarios of ecological and evolutionary interest—a landscape with habitat fragmentation, and an archipelago of islands.Spatially explicit neutral models represent an important tool in ecology for understanding the processes of biodiversity generation and predicting outcomes at large scales. The effort required to implement these complex spatially explicit simulations efficiently has thus far been a barrier to entry. Our packages increase the accessibility of these models and encourage further investigation of the primary mechanisms underpinning biodiversity. [ABSTRACT FROM AUTHOR]

The mid-domain effect states that in a spatially bounded domain species richness tends to decrease from the center towards the boundary, thus producing a peak or plateau of species richness in the middle of the domain even in the absence of any environmental gradient. This effect has been frequently used to describe geographic richness gradients of trophically similar species, but how it scales across different trophic levels is poorly understood. Here, we study the role of geometric constraints for the formation of spatial gradients in trophically structured metacommunities. We model colonization–extinction dynamics of a simple food chain on a network of habitat patches embedded in a one- or two-dimensional domain. In a spatially homogeneous or well-mixed system, we find that the food chain length increases with the square root of the ratio of colonization and extinction rates. In a spatially bounded domain, we find that the patch occupancy decreases towards the edge of the domain for all species of the food web, but this spatial gradient varies with the trophic level. As a consequence, the average food chain length peaks in the center and declines towards the boundaries of the domain, thereby extending the notion of a mid-domain effect from species richness to food chain length. This trophic mid-domain effect already arises in a one-dimensional domain, but it is most pronounced at the headlands in a two-dimensional domain. As the mid-domain effect for food chain length is caused solely by spatial boundaries and requires no other environmental heterogeneity, it can be considered a null expectation for geographic patterns in spatially extended food webs. [ABSTRACT FROM AUTHOR]

A spatially explicit application has been developed for the conterminous United States to assist farmers and extension agents with selecting appropriate tree species for agroforestry applications. The application combines several spatially explicit databases of tree species, high-resolution soil data, and climate. On the front-end of the application, a simple graphical user interface (GUI) allows the user to indicate their location, the size of the area to be searched, and the functional use category for the trees. These parameters are used to query a PostGRESQL relational database management system on the back-end via a Python script. All tree species within the user-specified area and matching the user-specified objectives, are returned to the web page along with tree characteristics, and soil and climate data for the specified location. Expert feedback on the application was solicited and used to make improvements to the service. The accuracy of the application was tested at several locations in Missouri, USA, and found satisfactory. [ABSTRACT FROM AUTHOR]

The northern long-eared bat (Myotis septentrionalis) is currently listed as threatened under the U.S. Endangered Species Act largely due to population declines resulting from the spread of white-nose syndrome in North America. White-nose syndrome was confirmed in Iowa in 2015, emphasizing a need to closely monitor populations of M. septentrionalis statewide. We applied presence-only models to predict landscape-scale resource selection by M. septentrionalis using roost tree observations and mist net captures from various research and environmental assessment projects in Iowa (2003–2015). We used a simultaneous autoregressive (SAR) model to account for residual spatial autocorrelation in our compiled data set and estimate the proportional probability of use of summer habitats for M. septentrionalis. We estimated SAR models using four environmental predictor variables measured at two landscape scales (0.5- and 2.4-km) representative of M. septentrionalis home range sizes in North America. The SAR models resulted in high predictive fit with withheld test observations and an independent data set of acoustic detections of M. septentrionalis from recent surveys (2016–2018), indicating a significant positive relationship existed between habitat quality (as an index of selection) and distribution of M. septentrionalis at landscape scales. At both spatial scales, M. septentrionalis showed positive selection of closed canopy interior forest, bottomland hardwood forest, and total perennial stream length, whereas at the 0.5-km scale, M. septentrionalis also showed a positive association with open canopy forest. Our models indicated that up to 7.0% and 8.5% of the state was comprised of potentially suitable forested summer habitats for M. septentrionalis for 0.5- and 2.4-km scales, respectively. Our models also indicated the distribution of highly selected habitats at landscape scales in Iowa and accurately predicted independent observations of M. septentrionalis in areas of the state where no capture efforts have occurred. This study provides methods to predict landscape-scale resource selection and distribution for bats where multiple fine-scale data sources exist across broad geographic regions. [ABSTRACT FROM AUTHOR]

The northern long-eared bat (Myotis septentrionalis) is currently listed as threatened under the U.S. Endangered Species Act largely due to population declines resulting from the spread of white-nose syndrome in North America. White-nose syndrome was confirmed in Iowa in 2015, emphasizing a need to closely monitor populations of M. septentrionalis statewide. We applied presence-only models to predict landscape-scale resource selection by M. septentrionalis using roost tree observations and mist net captures from various research and environmental assessment projects in Iowa (2003–2015). We used a simultaneous autoregressive (SAR) model to account for residual spatial autocorrelation in our compiled data set and estimate the proportional probability of use of summer habitats for M. septentrionalis. We estimated SAR models using four environmental predictor variables measured at two landscape scales (0.5- and 2.4-km) representative of M. septentrionalis home range sizes in North America. The SAR models resulted in high predictive fit with withheld test observations and an independent data set of acoustic detections of M. septentrionalis from recent surveys (2016–2018), indicating a significant positive relationship existed between habitat quality (as an index of selection) and distribution of M. septentrionalis at landscape scales. At both spatial scales, M. septentrionalis showed positive selection of closed canopy interior forest, bottomland hardwood forest, and total perennial stream length, whereas at the 0.5-km scale, M. septentrionalis also showed a positive association with open canopy forest. Our models indicated that up to 7.0% and 8.5% of the state was comprised of potentially suitable forested summer habitats for M. septentrionalis for 0.5- and 2.4-km scales, respectively. Our models also indicated the distribution of highly selected habitats at landscape scales in Iowa and accurately predicted independent observations of M. septentrionalis in areas of the state where no capture efforts have occurred. This study provides methods to predict landscape-scale resource selection and distribution for bats where multiple fine-scale data sources exist across broad geographic regions. [ABSTRACT FROM AUTHOR]

Fisheries management has historically focused on the population elasticity of target fish based primarily on demographic modeling, with the key assumptions of stability in environmental conditions and static trophic relationships. The predictive capacity of this fisheries framework is poor, especially in closed systems where the benthic diversity and boundary effects are important and the stock levels are low. Here, we present a probabilistic model that couples key fish populations with a complex suite of trophic, environmental, and geomorphological factors. Using 41 years of observations we model the changes in eastern Baltic cod (Gadus morhua), herring (Clupea harengus), and Baltic sprat (Sprattus sprattus balticus) for the Baltic Sea within a Bayesian network. The model predictions are spatially explicit and show the changes of the central Baltic Sea from cod- to sprat-dominated ecology over the 41 years. This also highlights how the years 2004 to 2014 deviate in terms of the typical cod–environment relationship, with environmental factors such as salinity being less influential on cod population abundance than in previous periods. The role of macrozoobenthos abundance, biotopic rugosity, and flatfish biomass showed an increased influence in predicting cod biomass in the last decade of the study. Fisheries management that is able to accommodate shifting ecological and environmental conditions relevant to biotopic information will be more effective and realistic. Non-stationary modelling for all of the homogeneous biotope regions, while acknowledging that each has a specific ecology relevant to understanding the fish population dynamics, is essential for fisheries science and sustainable management of fish stocks.

Habitat loss leads to species extinctions, both immediately and over the long term as 'extinction debt' is repaid. The same quantity of habitat can be lost in different spatial patterns with varying habitat fragmentation. How this translates to species loss remains an open problem requiring an understanding of the interplay between community dynamics and habitat structure across temporal and spatial scales. Here we develop formulas that characterise extinction debt in a spatial neutral model after habitat loss and fragmentation. Central to our formulas are two new metrics, which depend on properties of the taxa and landscape: 'effective area', measuring the remaining number of individuals and 'effective connectivity', measuring individuals' ability to disperse through fragmented habitat. This formalises the conventional wisdom that habitat area and habitat connectivity are the two critical requirements for long‐term preservation of biodiversity. Our approach suggests that mechanistic fragmentation metrics help resolve debates about fragmentation and species loss. [ABSTRACT FROM AUTHOR]

Maize expressing the Bacillus thuringiensis Cry1Ab toxin (Bt maize) provides a more effective control of corn borers than the use of insecticides. Yet, the spatial expansion of Bt maize may offer ideal ecological conditions for the development and spread of secondary pests, i.e. pests not susceptible to the expressed toxin. This paper develops a bio-economic, spatially explicit population model to analyse the spread and economic consequences of a secondary pest outbreak. Results show that the present use and even an eventual expansion of Bt maize can be economically and environmentally advantageous as it would decrease insecticide usage intensity. However, we show that caution is required when considering its widespread use. If a pest outbreak is not identified and dealt with at an early stage, it could lead to severe economic impacts even if insecticides are used in combination with the Bt maize. We further discuss potential policy and subsequent management strategies to address this issue. • The development of a flexible bio-economic spatially explicit population model • Secondary pest spread patterns are estimated using spatial forecasting • Present use or expansion of Bt maize accrues economic benefits and reduce insecticide use • Inappropriate management and use of Bt can lead to ingression of secondary pests • Early detection and prompt control are vital to fully attain all the benefits of Bt maize. [ABSTRACT FROM AUTHOR]

• The physicochemistry of leaves is unique and is a major driver of leaf colonisation. • Competition and cooperation may be major drivers of bacterial colonisation. • Leaves respond to bacterial colonisation locally and systemically. • How leaf responses shape bacterial colonisation patterns is unclear. • Plant-microbe interaction should be studied at the micrometer resolution. Bacteria establish complex, compositionally consistent communities on healthy leaves. Ecological processes such as dispersal, diversification, ecological drift, and selection as well as leaf surface physicochemistry and topology impact community assembly. Since the leaf surface is an oligotrophic environment, species interactions such as competition and cooperation may be major contributors to shape community structure. Furthermore, the plant immune system impacts on microbial community composition, as plant cells respond to bacterial molecules and shape their responses according to the mixture of molecules present. Such tunability of the plant immune network likely enables the plant host to differentiate between pathogenic and non-pathogenic colonisers, avoiding costly immune responses to non-pathogenic colonisers. Plant immune responses are either systemically distributed or locally confined, which in turn affects the colonisation pattern of the associated microbiota. However, how each of these factors impacts the bacterial community is unclear. To better understand this impact, bacterial communities need to be studied at a micrometre resolution, which is the scale that is relevant to the members of the community. Here, current insights into the driving factors influencing the assembly of leaf surface-colonising bacterial communities are discussed, with a special focus on plant host immunity as an emerging factor contributing to bacterial leaf colonisation. [ABSTRACT FROM AUTHOR]

Wildlife density estimates are important to accurately formulate population management objectives and understand the relationship between habitat characteristics and a species' abundance. Despite advances in density and abundance estimation methods, management of common game species continues to be challenged by a lack of reliable population estimates. In Washington, USA, statewide American black bear (Ursus americanus) abundance estimates are predicated on density estimates derived from research in the 1970s and are hypothesized to be a function of precipitation and vegetation, with higher densities in western Washington. To evaluate current black bear density and landscape relationships in Washington, we conducted a 4‐year capture‐recapture study in 2 areas of the North Cascade Mountains using 2 detection methods, non‐invasive DNA collection and physical capture and deployment of global positioning system (GPS) collars. We integrated GPS telemetry from collared bears with spatial capture‐recapture (SCR) data and created a SCR‐resource selection model to estimate density as a function of spatial covariates and test the hypothesis that density is higher in areas with greater vegetative food resources. We captured and collared 118 bears 132 times and collected 7,863 hair samples at hair traps where we identified 537 bears from 1,237 detections via DNA. The most‐supported model in the western North Cascades depicted a negative relationship between black bear density and an index of human development. We estimated bear density at 20.1 bears/100 km2, but density varied from 13.5/100 km2 to 27.8 bears/100 km2 depending on degree of human development. The model best supported by the data in the eastern North Cascades estimated an average density of 19.2 bears/100 km2, which was positively correlated with primary productivity, with resulting density estimates ranging from 7.1/100 km2 to 33.6 bears/100 km2. The hypothesis that greater precipitation and associated vegetative production in western Washington supports greater bear density compared to eastern Washington was not supported by our data. In western Washington, empirically derived average density estimates (including cubs) were nearly 50% lower than managers expected prior to our research. In eastern Washington average black bear density was predominantly as expected, but localized areas of high primary productivity supported greater than anticipated bear densities. Our findings underscore the importance that black bear density is not likely uniform and management risk may be increased if an average density is applied at too large a scale. Disparities between expected and empirically derived bear density illustrate the need for more rigorous monitoring to understand processes that affect population numbers throughout the jurisdiction, and suggest that management plans may need to be reevaluated to determine if current harvest strategies are achieving population objectives. © 2019 The Wildlife Society. Black bear density is unlikely to be uniform within jurisdictions and can be associated with multiple natural and anthropogenic factors. Management risk may be increased if an average black bear density is applied at too large a scale because of spatial and temporal variability of natural food resources, land management practices, wildlife agency objectives, and human populations. [ABSTRACT FROM AUTHOR]