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Forecasting the spread of emerging pests is widely requested by pest management agencies in order to prioritise and target efforts. Two widely used approaches are statistical Species Distribution Models (SDMs) and CLIMEX, which uses ecophysiological parameters. Each have strengths and weaknesses. SDMs can incorporate almost any environmental condition and their accuracy can be formally evaluated to inform managers. However, accuracy is affected by data availability and can be limited for emerging pests, and SDMs usually predict year-round distributions, not seasonal outbreaks. CLIMEX can formally incorporate expert ecophysiological knowledge and predicts seasonal outbreaks. However, the methods for formal evaluation are limited and rarely applied. We argue that both approaches can be informative and complementary, but we need tools to integrate and evaluate their accuracy. Here we develop such an approach, and test it by forecasting the potential global range of the tomato pest Tuta absoluta.The accuracy of previously developed CLIMEX and new statistical SDMs were comparable on average, but the best statistical SDM techniques and environmental data substantially outperformed CLIMEX. The ensembled approach changes expectations of T. absoluta's spread. The pest's environmental tolerances and potential range in Africa, the Arabian Peninsula, Central Asia and Australia will be larger than previous estimates.We recommend that CLIMEX be considered one of a suite of SDM techniques and thus evaluated formally. CLIMEX and statistical SDMs should be compared and ensembled if possible. We provide code that can be used to do so when employing the biomod suite of SDM techniques. © 2021 The Authors. Pest Management Science published by John WileySons Ltd on behalf of Society of Chemical Industry.

1. Movement is the primary means by which animals obtain resources and avoid hazards. Most movement exhibits directional bias that is related to environmental features (taxis), such as the location of food patches, predators, ocean currents, or wind. Numerous behaviours with directional bias can be characterized by maintaining orientation at an angle relative to the environmental stimuli (menotaxis), including navigation relative to sunlight or magnetic fields and energy-conserving flight across wind. However, no statistical methods exist to flexibly classify and characterise such directional bias. 2. We propose a biased correlated random walk model that can identify menotactic behaviours by predicting turning angle as a trade-off between directional persistence and directional bias relative to environmental stimuli without making a priori assumptions about the angle of bias. We apply the model within the framework of a multi-state hidden Markov model (HMM) and describe methods to remedy information loss associated with coarse environmental data to improve the classification and parameterization of directional bias. 3. Using simulation studies, we illustrate how our method more accurately classifies behavioural states compared to conventional correlated random walk HMMs that do not incorporate directional bias. We illustrate the application of these methods by identifying cross wind olfactory foraging and drifting behaviour mediated by wind-driven sea ice drift in polar bears (Ursus maritimus) from movement data collected by satellite telemetry. 4. The extensions we propose can be readily applied to movement data to identify and characterize behaviours with directional bias toward any angle, and open up new avenues to investigate more mechanistic relationships between animal movement and the environment.

Biological invasions, the establishment and spread of non‐native species in new regions, can have extensive economic and environmental consequences. Increased global connectivity accelerates introduction rates, while climate and land‐cover changes may decrease the barriers to invasive populations spread. A detailed knowledge of the invasion history, including assessing source populations, routes of spread, number of independent introductions, and the effects of genetic bottlenecks and admixture on the establishment success, adaptive potential, and further spread, is crucial from an applied perspective to mitigate socioeconomic impacts of invasive species, as well as for addressing fundamental questions on the evolutionary dynamics of the invasion process. Recent advances in genomics together with the development of geographic information systems provide unprecedented large genetic and environmental datasets at global and local scales to link population genomics, landscape ecology, and species distribution modeling into a common framework to study the invasion process. Although the factors underlying population invasiveness have been extensively reviewed, analytical methods currently available to optimally combine molecular and environmental data for inferring invasive population demographic parameters and predicting further spreading are still under development. In this review, we focus on the few recent insect invasion studies that combine different datasets and approaches to show how integrating genetic, observational, ecological, and environmental data pave the way to a more integrative biological invasion science. We provide guidelines to study the evolutionary dynamics of invasions at each step of the invasion process, and conclude on the benefits of including all types of information and up‐to‐date analytical tools from different research areas into a single framework.

Characterization of microbial assemblages via environmental DNA metabarcoding is increasingly being used in routine monitoring programs due to its sensitivity and cost-effectiveness. Several programs have been developed recently which infer functional profiles from 16S rRNA gene data using hidden-state prediction (HSP) algorithms. These might offer an economic and scalable alter-native to shotgun metagenomics. To date, HSP-based methods have seen limited use for benthic marine surveys and their performance in these environments remains unevaluated. In this study, 16S rRNA metabarcoding was applied to sediment samples collected at 0 and ≥ 1200 m from Norwegian salmon farms, and three metabolic inference approaches (PAPRICA, PICRUSt2 and TAX4FUN2) evaluated against metagenomics and environmental data. While metabarcoding and metagenomics recovered a comparable functional diversity, the taxonomic composition differed be-tween approaches, with genera richness up to 20× higher for metabarcoding. Comparisons between the sensitivity (highest true positive rates) and specificity (lowest true negative rates) of HSP-based programs in detecting functions found in metagenomics data ranged, respectively, from 0.52 and 0.60 to 0.76 and 0.79. However, little correlation was observed between the relative abundance of their specific functions. Functional beta-diversity of HSP-based data was strongly associated with that of metagenomics (r ≥ 0.86 for PAPRICA and TAX4FUN2) and responded similarly to the impact of fish farm activities. Our results demonstrate that although HSP-based metabarcoding approaches provide a slightly different functional profile than metagenomics, partly due to recovering a distinct community, they represent a cost-effective and valuable tool for characterizing and assessing the effects of fish farming on benthic ecosystems.

As most ecosystems around the world are threatened by anthropogenic degradation and climate change, there is an increasing urgency to implement restoration strategies aiming at ensuring ecosystem self-sustainability and resilience. An initial step towards that goal relies on selecting the most suitable seed sources for a successful revegetation, which can be extremely challenging in highly degraded landscapes. The most common seed sourcing strategy is to select local seeds because it is assumed that plants experience strong adaptations to their natal sites. An alternative strategy is the selection of climate-adapted genotypes to future conditions. While considering future climatic projections is important to account for spatial shifts in climate to inform assisted gene flow and translocations, to restore highly degraded landscapes we need a comprehensive approach that first accounts for species adaptations to current at-site environmental conditions. In this issue of Molecular Ecology Resources, Carvalho et al. present a novel landscape genomics framework to identify the most appropriate seed sourcing strategy for moderately and highly degraded sites by integrating genotype, phenotype and environmental data in a spatially explicit context for two native plant species with potential to help restore iron-rich Amazonian savannas. This framework is amenable to be applicable and adapted to a broad range of restoration initiatives, as the dichotomy between focusing on the current or future climatic conditions should depend on the goals and environmental circumstances of each restoration site.

Areas that contain ecologically distinct biological content, called bioregions, are a central component to spatial and ecosystem‐based management. We review and describe a variety of commonly used and newly developed statistical approaches for quantitatively determining bioregions. Statistical approaches to bioregionalization can broadly be classified as two‐stage approaches that either ‘Group First, then Predict’ or ‘Predict First, then Group’, or a newer class of one‐stage approaches that simultaneously analyse biological data with reference to environmental data to generate bioregions. We demonstrate these approaches using a selection of methods applied to simulated data and real data on demersal fish. The methods are assessed against their ability to answer several common scientific or management questions. The true number of simulated bioregions was only identified by both of the one‐stage methods and one two‐stage method. When the number of bioregions was known, many of the methods, but not all, could adequately infer the species, environmental and spatial characteristics of bioregions. One‐stage approaches, however, do so directly via a single model without the need for separate post‐hoc analyses and additionally provide an appropriate characterization of uncertainty. One‐stage approaches provide a comprehensive and consistent method for objectively identifying and characterizing bioregions using both biological and environmental data. Potential avenues of future development in one‐stage methods include incorporating presence‐only and multiple data types as well as considering functional aspects of bioregions.

Digital soil mapping (DSM) is an effective mapping technique that supports the increased need for quantitative soil data. In DSM, soil properties are correlated with environmental characteristics using statistical models such as regression. However, many of these relationships are explicitly described in mechanistic simulation models. Therefore, the mechanistic relationships can, in theory, replace the statistical relationships in DSM. This study aims to develop a mechanistic model to predict soil organic matter (SOM) stocks in Natura2000 areas of the Cantabria region (Spain). The mechanistic model is established in four steps: (a) identify major processes that influence SOM stocks, (b) review existing models describing the major processes and the respective environmental data that they require, (c) establish a database with the required input data, and (d) calibrate the model with field observations. The SOM stocks map resulting from the mechanistic model had a mean error (ME) of -2 t SOM ha−1 and a root mean square error (RMSE) of 66t SOM ha-1. The Lin's concordance correlation coefficient was 0.47 and the amount of variance explained (AVE) was 0.21. The results of the mechanistic model were compared to the results of a statistical model. It turned out that the correlation coefficient between the two SOM stock maps was 0.8. This study illustrated that mechanistic soil models can be used for DSM, which brings new opportunities. Mechanistic models for DSM should be considered for mapping soil characteristics that are difficult to predict by statistical models, and for extrapolation purposes. This research was financially supported by the Environmental Hydraulics Institute ‘IH Cantabria of Universidad de Cantabria’ and the CGIAR Research Programme on Climate Change, Agriculture and Food Security (CCAFS). The CCAFS project is carried out with support from CGIAR Fund Donors and through bilateral funding agreements. Besides the financial support, we would like to thank Sara Alcalde Aparicio for collaboration in the collection and analyses of soil samples.

Summary Despite the importance of climate-adjusted provenancing to mitigate the effects of climate change, climatic considerations alone are insufficient when restoring highly degraded sites. Here we employ a comprehensive landscape genomic approach to assist the restoration of moderately disturbed and highly degraded sites. Relying on genotyping-by-sequencing we identified thousands of single nucleotide polymorphisms in two plant species of special interest for the restoration of iron-rich Amazon Savannas from the Eastern Amazon. We first used a subset of neutral loci to assess genetic structure and determine the genetic neighborhood size, and then combined uni-variate and multi-variate methods to identify genotype-environment and genotype-phenotype associations, representing local adaptations. We then mapped adaptive genetic variation and predicted adaptive genotypes for both restoration sites. Whereas local provenances where found optimal to restore the moderately disturbed site, a mixture of genotypes was the most promising strategy to recover a highly degraded mining site, to which local provenances were not adapted. We discuss how our results can help define site-adjusted provenancing strategies and argue that our methods can be more broadly applied to assist other restoration and rehabilitation initiatives.

Designing an effective conservation strategy requires understanding where rare species are located. Because rare species can be difficult to find, ecologists often identify other species called conservation surrogates that can help inform the distribution of rare species. Species distribution models typically rely on environmental data when predicting the occurrence of species, neglecting the effect of species' co‐occurrences and biotic interactions. Here, we present a new approach that uses Bayesian networks to improve predictions by modeling environmental co‐responses among species. For species from a European peat bog community, our approach consistently performs better than single‐species models and better than conventional multi‐species approaches that include the presence of nontarget species as additional independent variables in regression models. Our approach performs particularly well with rare species and when calibration data are limited. Furthermore, we identify a group of “predictor species” that are relatively common, insensitive to the presence of other species, and can be used to improve occurrence predictions of rare species. Predictor species are distinct from other categories of conservation surrogates such as umbrella or indicator species, which motivates focused data collection of predictor species to enhance conservation practices., It is difficult to protect rare species from extinction because of their sparse distributions and specialized habitat preferences. Here, we use Bayesian networks to accurately predict the occurrence of rare species, providing a solution to an ever‐important conservation problem.

Despite studies on range shifts being abundant, the problem of dispersal barriers limiting climate migrants' movement is yet to be fully included into any modeling framework. For this reason, we introduce a novel concept whereby the interplay of range shifts and dispersal barriers of a particular spatial configuration can threaten the persistence of populations under a climate change scenario. We named this concept "C-trap," based on the topographic shape of such barriers. After elaborating on the theoretical features of C-traps, we provide a simple method that combines environmental data and future climate projections to locate them spatially. We use this method to determine where high C-trap densities have the potential to further threaten the conservation of endangered, endemic animals across the world's terrestrial realm, in a climate change scenario. Our methodology detected potential C-traps for the study system, with areas of high density mostly located in east Europe, south Asia and North America. However, finer-scale analyses are required to assess the magnitude of the threat locally. Dispersal barriers add an additional dimension to range shift studies and can ultimately prevent otherwise successful climate migrants from tracking their climatic niche. The methodology presented here is simple and flexible enough to be adapted to a wide range of taxa and locations as well as the fast development of range shift modeling. Therefore, we encourage researchers to include the effects of anthropogenic dispersal barriers in range shifts models and in the planning of effective conservation strategies with reference to climate change.

Species occurrences inherently include positional error. Such error can be problematic for species distribution models (SDMs), especially those based on fine-resolution envi- ronmental data. It has been suggested that there could be a link between the influence of positional error and the width of the species ecological niche. Although positional errors in species occurrence data may imply serious limitations, especially for model- ling species with narrow ecological niche, it has never been thoroughly explored. We used a virtual species approach to assess the effects of the positional error on fine-scale SDMs for species with environmental niches of different widths. We simulated three virtual species with varying niche breadth, from specialist to generalist. The true dis- tribution of these virtual species was then altered by introducing different levels of positional error (from 5 to 500 m). We built generalized linear models and MaxEnt models using the distribution of the three virtual species (unaltered and altered) and a combination of environmental data at 5 m resolution. The models’ performance and niche overlap were compared to assess the effect of positional error with varying niche breadth in the geographical and environmental space. The positional error negatively impacted performance and niche overlap metrics. The amplitude of the influence of positional error depended on the species niche, with models for specialist species being more affected than those for generalist species. The positional error had the same effect on both modelling techniques. Finally, increasing sample size did not mitigate the negative influence of positional error. We showed that fine-scale SDMs are consider- ably affected by positional error, even when such error is low. Therefore, where new surveys are undertaken, we recommend paying attention to data collection techniques to minimize the positional error in occurrence data and thus to avoid its negative effect on SDMs, especially when studying specialist species.

Animal populations have developed multiple strategies to deal with environmental change. Among them, the demographic buffering strategy consists in constraining the temporal variation of the vital rate(s) that most affect(s) the overall performance of the population. Tortoises are known to buffer their temporal variation in adult survival, which typically has the highest contribution to the population growth rate λ, at the expense of a high variability on reproductive rates, which contribute far less to λ. To identify the effects of projected increases in droughts in its natural habitat, we use field data collected across 15 locations of Testudo graeca in southeast Spain over a decade. We analyse the effects of environmental variables on reproduction rates. In addition, we couple the demographic and environmental data to parameterise an integral projection model to simulate the effects of different scenarios of drought recurrence on λ under different degrees of intensity in the survival–reproduction tradeoff. We find that droughts negatively affect the probability of laying eggs; however, the overall effects on λ under the current drought recurrence (one/decade) are negligible when survival is constant (independent of the reduction of reproduction by drought events) and when survival increased as a tradeoff with the reduction of reproduction rates, with a threshold to population viability at three or more droughts/decade. Additionally, we show that, although some species may buffer current environmental regimes by carefully orchestrating how their vital rates vary through time, a demographic buffering strategy is insufficient to ensure population viability in extreme regimes. Our findings support the hypothesis that the demographic buffering strategy has a limit of effectiveness when adverse conditions occur frequently. Our methodological approach provides a framework for ecologists to determine how effective the management of environmental drivers can be for demographically buffering populations, and which scenarios may not provide long-term population persistence.

Biological data collection is entering a new era. Community science, satellite remote sensing (SRS), and local forms of remote sensing (e.g., camera traps and acoustic recordings) have enabled biological data to be collected at unprecedented spatial and temporal scales and resolution. There is growing interest in developing observation networks to collect and synthesize data to improve broad-scale ecological monitoring, but no examples of such networks have emerged to inform decision-making by agencies. Here, we present the implementation of one such jurisdictional observation network (JON), Snapshot Wisconsin, which links synoptic environmental data derived from SRS to biodiversity observations collected continuously from a trail camera network to support management decision-making. We use several examples to illustrate that Snapshot Wisconsin improves the spatial, temporal, and biological resolution and extent of information available to support management, filling gaps associated with traditional monitoring and enabling consideration of new management strategies. JONs like Snapshot Wisconsin further strengthen monitoring inference by contributing novel lines of evidence useful for corroboration or integration. SRS provides environmental context that facilitates inference, prediction, and forecasting, and ultimately helps managers formulate, test, and refine conceptual models for the monitored systems. Although these approaches pose challenges, Snapshot Wisconsin demonstrates that expansive observation networks can be tractably managed by agencies to support decision making, providing a powerful new tool for agencies to better achieve their missions and reshape the nature of environmental decision-making.

The Quasi-biennial Oscillation (QBO) dominates the interannual variability of the tropical stratosphere and influences other regions of the atmosphere. The high predictability of the QBO implies that its teleconnections could lead to increased skill of seasonal and decadal forecasts provided the relevant mechanisms are accurately represented in models. Here modelling and sampling uncertainties of QBO teleconnections are examined using a multi-model ensemble of QBO-resolving atmospheric general circulation models that have carried out a set of coordinated experiments as part of the Stratosphere-troposphere Processes And their Role in Climate (SPARC) QBO initiative (QBOi). During Northern Hemisphere winter, the stratospheric polar vortex in most of these models strengthens when the QBO near 50 hPa is westerly and weakens when it is easterly, consistent with, but weaker than, the observed response. These weak responses are likely due to model errors, such as systematically weak QBO amplitudes near 50 hPa, affecting the teleconnection. The teleconnection to the North Atlantic Oscillation is less well captured overall, but of similar strength to the observed signal in the few models that do show it. The models do not show clear evidence of a QBO teleconnection to the Northern Hemisphere Pacific-sector subtropical jet. We acknowledge the UK's Centre for Environmental Data Analysis (CEDA) for hosting the QBOi data archive. This work was supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation under Cooperative Agreement no. 1852977. Portions of this study were supported by the Regional and Global Model Analysis (RGMA) component of the Earth and Environmental System Modeling Program of the U.S. Department of Energy's Office of Biological & Environmental Research (BER) via National Science Foundation IA 1844590. NB was supported by the Met Office Hadley Centre Programme funded by BEIS and Defra. SO and LG were supported by the National Centre for Atmospheric Science and the Natural Environment Research Council (NE/P006779/1, NE/N018001/1). FL and SO were supported by the JPI-Climate/Belmont Forum 423 project GOTHAM (ANR-15-JCLI-0004-01, NERC NE/P006779/1). CC and FS were supported by the Copernicus Climate Change Service, funded by the EU and implemented by ECMWF. HN was supported by the Environment Research and Technology Development Fund (S-12) of the Environmental Restoration and Conservation Agency of Japan and by Grant-in-Aid (JP18K03748, JP20H05171) for Science Research (KAKENHI) from the Ministry of Education, Culture, Sports, Science, and Technology of Japan. For the EMAC simulations, PB, TK, and SV acknowledge support by the state of Baden-Württemberg through bwHPC. YK was supported by the Japan Society for Promotion of Science (JSPS) KAKENHI (grant nos. JP15KK0178, JP17K18816, JP18H01286 and 19H05702) and by the Environment Research and Technology Development Fund (JPMEERF20192004) of the Environmental Restoration and Conservation Agency of Japan. YK and KH were supported by the Japan Agency for Marine-Earth Science and Technology (JAMSTEC) through its sponsorship of research at the International Pacific Research Center. SW and YK were partly supported by the “Integrated Research Program for Advancing Climate Models (TOUGOU program)” from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan. The Earth Simulator was used for MIROC-ESM and MIROC-AGCM-LL simulations. We acknowledge the scientific guidance of the World Climate Research Programme (WCRP) for helping motivate this work, coordinated under the framework of the Stratosphere-troposphere Processes And their Role in Climate (SPARC) QBO initiative (QBOi) led by JA, NB, KH and SO. Peer Reviewed "Article signat per 29 autors/es: James A. Anstey, Isla R. Simpson, Jadwiga H. Richter, Hiroaki Naoe, Masakazu Taguchi, Federico Serva, Lesley J. Gray, Neal Butchart, Kevin Hamilton, Scott Osprey, Omar Bellprat, Peter Braesicke, Andrew C. Bushell, Chiara Cagnazzo, Chih-Chieh Chen, Hye-Yeong Chun, Rolando R. Garcia, Laura Holt, Yoshio Kawatani, Tobias Kerzenmacher, Young-Ha Kim, Francois Lott, Charles McLandress, John Scinocca, Timothy N. Stockdale, Stefan Versick, Shingo Watanabe, Kohei Yoshida, Seiji Yukimoto"

Hybrid zones have long been of interest to biologists as natural laboratories where we can gain insight into the processes of adaptation and speciation. Repeated sampling of individual hybrid zones has been particularly useful in elucidating the dynamic balance between selection and dispersal that maintains most hybrid zones. Here, we revisit a hybrid zone between Heliconius erato butterflies in Panamá for a third time over more than 30 years. We combine a novel Bayesian extension of stepped-cline hybrid zone models with environmental data to understand the genetic and environmental causes of cline dynamics in this species. The cline has continued to move west, likely due to dominance drive, but has slowed and broadened. Environmental analyses suggest that widespread deforestation in Panamá could be leading to decreased avian predation and relaxed selection, causing the observed changes in cline dynamics.

"1.Remote sensing data is a valuable tool in movement ecology. However, the way it's combined with animal movement data is far from optimal as most studies overlook differences in the spatial, temporal and thematic resolutions between both data types. 2.rsMove uses a pixel‐based approach to link animal tracking and remote sensing data that bridge the gap between these two disciplines while respecting the limitations of the latest. Additionally, if offers standardize methods to pre‐analyze the connection between animal movement and environmental change. 3.The package guides the choice of study sites, satellite data and environmental predictors though data mining and visualization tools. rsMove offers a simple methodology to analyze animal‐environment interactions that helps avoid unnecessary, time‐consuming satellite data processing through an informed decision process. 4.The package offers a basis for a better communication between ecologists and remote sensing experts contributing to the definition of clear remote sensing data requirements. Additionally, it provides future studies on animal movement tools for a more critical approach on the selection of environmental data supporting their reproducibility."

This case study deals with The Dow Chemical Company’s (Dow) decision on how to restore a greenbelt area with historical issue that borders a brownfield property owned by the city of Midland, Michigan. Dow has a stated goal to apply a “business-decision process that values nature” and to deliver $1 billion in “value through projects that are good for business and good for ecosystems.” In line with this goal, Dow wanted to restore the greenbelt area by enhancing habitat and ecosystem services to Dow and Midland in a way that was also beneficial to the company’s bottom line. This case study presents three alternative restoration designs along with detailed financial cost and environmental data for each design. Students perform cost-benefit analysis, highlighting potential differences between how costs are calculated in a public setting relative to a private setting. In addition, students assess how the inclusion of important non-financial environmental data may be used to inform decision making.

The research data repository of the Environmental Data Initiative (EDI) is a signatory of the FAIR Data Principles. Building on over 30 years of data curation research and experience in the NSF-fun...

A conceptual framework for evaluation of climate effects on tree diseases is presented. Climate can exacerbate tree diseases by favouring pathogen biology, including reproduction and infection processes. Climatic conditions can also cause abiotic disease—direct stress or mortality when trees’ physiological limits are exceeded. When stress is sublethal, weakened trees may subsequently be killed by secondary organisms. To demonstrate climate's involvement in disease, associations between climatic conditions and disease expression provide the primary evidence of atmospheric involvement because experimentation is often impractical for mature trees. This framework tests spatial and temporal relationships of climate and disease at several scales to document climate effects, if any. The presence and absence of the disease can be contrasted with climate data and models at geographic scales: stand, regional and species range. Temporal variation in weather, climate and climate change is examined during onset, development and remission of the disease. Predisposing factors such as site and stand conditions can modify the climate effects of some diseases, especially at finer spatial scales. Spatially explicit climate models that display temperature and precipitation or derivative models such as snow and drought stress provide useful data, and however, information on disease extent at different spatial scales and monitoring through time are often incomplete. The framework can be used to overcome limitations in other disease causality approaches, such as Koch's postulates, and allow for the integration of vegetation, pathogen and environmental data into causality determinations.

Statistical spatiotemporal environmental data analysis is rarely straightforward, with one having to face challenges relating to big data, non‐Gaussianity, nonstationarity, multiple scales of behavior, deterministic (numerical) model output, and more. One often has to rely heavily on good statistical parallel computing skills and sound knowledge of the application domain. The work of Tagle et al. (2020) overcomes all of these challenges, and is an excellent example of the tangible contributions spatiotemporal modeling and distribution theory can make to the environmental sciences at the policy level. In this discussion piece I focus on a few high‐level concepts in the paper of Tagle et al. (2020) that are relevant to related application domains. I also provide some technical suggestions that could be used to facilitate inference.

Location can be described and used to spatially integrate and enable data in a number of different ways. In 2018, the Location Index (LOC-I) project was initiated to bring together a number of gove...

Greaves, Alan/0000-0002-8004-4702; Salihoglu, Rana/0000-0003-3277-2446; Sakinc, Mehmet/0000-0001-5955-0099; Akcer On, Sena/0000-0002-0708-8629 WOS: 000527372800001 The ancient Gulf of Latmos is an iconic example of a dynamic landscape and humankind's historical relationship with it. Using extensive new primary data and original models for calibrating radiocarbon dates in transitional lagoon environments, we demonstrate that Lake Bafa (or Bafa Golu, in Turkish) formed at a much earlier date than previously thought. In questioning the logical process by which previous dates were achieved, we re-examine the relationship between sedimentological data, archaeology and written history. We reassert the need to establish independently dated environmental data sets as the foundation of regional studies as distinct from archaeological and historical interpretive processes. We conclude that Lake Bafa slowly transitioned to become an isolated lagoon sometime between the end of the second millennium B.C. and end of the first millennium B.C.; becoming a fully closed brackish lake during the second millennium A.D. This marks a major shift in our understanding of the nature of human occupation and activity here during the last four millennia but also in the way we date ancient lagoons and integrate historical and environmental data in general. School of Histories, Languages and Cultures at the University of Liverpool; TUBITAKTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [110Y021] School of Histories, Languages and Cultures at the University of Liverpool; TUBITAK, Grant/Award Number: 110Y021

Weather- and climate-related hazards are responsible for monetary losses, material damages, and societal consequences. Quantifying related risks is, therefore, an important societal task, particularly in view of future climate change. For this task, climate risk assessment increasingly uses model chains, which mainly build on data from the last few decades. The past record of events could play a role in this context. New numerical techniques can make use of historical weather data to simulate impacts quantitatively. However, using historical data for model applications differs from using recent products. Here, we provide an overview of climate risk assessment methodologies and of the properties of historical instrumental and documentary data. Using three examples, we then outline how historical environmental data can be used today in climate risk assessment by (1) developing and validating numerical model chains, (2) providing a large statistical sample which can be directly exploited to estimate hazards and to model present risks, and (3) establishing "worst-case" events which are relevant references in the present or future. The examples show that, in order to be successful, different sources (reanalyses, digitized instrumental data, and documentary data) and methods (dynamical downscaling and analog methods) need to be combined on a case-by-case basis.

QUESTIONS: The community patterns in kwongan, a mediterranean‐type scrub on nutrient‐poor soils occurring in Western Australia, are poorly understood due to only few, focused studies using disparate sampling designs. We aimed to determine whether (a) classification of the kwongan vegetation of the Eneabba Sandplains leads to an ecologically informative classification scheme, and (b) we could identify environmental drivers underpinning the plant community patterns. LOCATION: Township of Eneabba (29°82′S, 115°27′E), approximately 250 km north of Perth, Western Australia, covering 1,210 km². METHODS: We used a data set consisting of 512 releves, collected following the standard field methodology of the Braun‐Blanquet approach, and accompanied by an extensive set of environmental data consisting of 94 variables representing climate, fire, soil and terrain properties across 189 releves. The data were classified and ordinated by a series of multivariate analyses. OptimClass assisted in the selection of the most robust classification procedure. Distance‐based redundancy analysis (db‐RDA) inferred the major ecological drivers of the vegetation patterns. RESULTS: Numerical classification, nonmetric multidimensional scaling, and syntaxonomic tabular analysis revealed two major community groups (MCG A and B), eight community groups, and 17 communities in the kwongan vegetation of the study area. All vegetation units are characterised in terms of floristic composition and position along major ecological gradients in the studied area. The MCGs separated along a composite gradient of soil‐texture and exchangeable cations. The first two db‐RDA axes explained 21% of the total variance which is very low, considering the high number of environmental variables used. CONCLUSIONS: Our study provides a comprehensive insight into the high variability of vegetation types in hyper‐diverse kwongan scrub at a landscape spatial scale; it is the first syntaxonomic account of the Western Australian kwongan vegetation, presenting a complete tabular comparative analysis. The studied major community groups segregate along a soil‐texture and an exchangeable‐cation content gradient. At a community scale, environmental filtering explained a small fraction of the vegetation–environment relationship. We suggest that the unexplained portion of the vegetation‐environment relationship might be a product of slow‐acting neutral processes in this hyper‐diverse system; this assertion is amenable to rigorous future testing.

Data repositories and research networks worldwide are publishing a diverse array of long-term and experimental data for meaningful reuse, repurpose, and integration. However, in synthesis research ...

Objective. To map at a fine spatial scale, the risk of malaria incidence for the important endemic region is Uraba-Bajo Cauca and Alto Sinu, NW Colombia, using a new modelling framework based on GIS and remotely sensed environmental data. Methods. The association between environmental and topographic variables obtained from remote sensors and the annual parasite incidence (API) for the years 2013–2015 was calculated using multiple regression analysis; subsequently, a model was constructed to estimate the API and to project it to the entire endemic region in order to design the risk map. The model was validated by relating the obtained API values with the presence of the three main Colombian malaria vectors, Anopheles darlingi, Anopheles albimanus and Anopheles nuneztovari. Results. Temperature and Normalized Difference Water Index (NDWI) showed a significant correlation with the observed API. The risk map of malaria incidence showed that the zones at higher risk in the Uraba-Bajo Cauca and Alto Sinu region were located south-east of the region, while the northern area presented the lowest malaria risk. A method was generated to estimate the API for small urban centres, instead of the used reports at the municipality level. Conclusions. These results provide evidence of the utility of risk maps to identify environmentally vulnerable areas at a fine spatial resolution in the Uraba-Bajo Cauca and Alto Sinu region. This information contributes to the implementation of vector control interventions at the icrogeographic scale at areas of high malaria risk. COL0013746

AIM: To assess if geographical and/or environmental factors are driving skull morphology and genetic structure in the cosmopolitan bat species, Tadarida brasiliensis. LOCATION: North, Central and South America, Greater and Lesser Antilles. METHODS: We analysed morphometric, genetic and environmental data from the entire distribution of T. brasiliensis. Individuals were genotyped at nine nuclear loci, and clustering analyses were used to detect genetic groups and test correlations between genetic, geographical, and environmental distances. Museum specimens were photographed and 111 two‐dimensional landmarks were digitized per individual. Multivariate analyses were applied to explore whether factors such as sex, genetic group, subspecies, biogeographical regions, latitude, longitude and/or environment contribute to the morphometric variation of this species. We also applied a novel approach based on trend surface analyses and model selection to compare models of spatial and environmental dependence and to predict size, shape, and allometry‐free variation across the Americas. RESULTS: Genetic and morphometric differences among populations coincide with major biogeographical and climatic breaks in Nearctic, Neotropical and Antillean regions. Trend surface analyses showed that skull size variation can be explained by spatial factors (latitude and longitude), whereas skull shape can be explained by the interaction of spatial and environmental factors. However, allometry‐free shape variation might respond to complex interactions not captured by our models. MAIN CONCLUSION: Our explicit testing of spatial and environmental dependence using geostatistical model selection approaches allowed us to identify extrinsic factors that shape genetic and phenotypic responses in a cosmopolitan bat species. Our findings suggest that skull size is primarily driven by geographical factors (genetic drift), whereas shape variation is driven by adaptive responses to environmental conditions.

Tsetse flies (genus Glossina) are the only vector for the parasitic trypanosomes responsible for sleeping sickness and nagana across sub-Saharan Africa. In Uganda, the tsetse fly Glossina fuscipes fuscipes is responsible for transmission of the parasite in 90% of sleeping sickness cases, and co-occurrence of both forms of human-infective trypanosomes makes vector control a priority. We use population genetic data from 38 samples from northern Uganda in a novel methodological pipeline that integrates genetic data, remotely sensed environmental data, and hundreds of field-survey observations. This methodological pipeline identifies isolated habitat by first identifying environmental parameters correlated with genetic differentiation, second, predicting spatial connectivity using field-survey observations and the most predictive environmental parameter(s), and third, overlaying the connectivity surface onto a habitat suitability map. Results from this pipeline indicated that net photosynthesis was the strongest predictor of genetic differentiation in G. f. fuscipes in northern Uganda. The resulting connectivity surface identified a large area of well-connected habitat in northwestern Uganda, and twenty-four isolated patches on the northeastern margin of the G. f. fuscipes distribution. We tested this novel methodological pipeline by completing an ad hoc sample and genetic screen of G. f. fuscipes samples from a model-predicted isolated patch, and evaluated whether the ad hoc sample was in fact as genetically isolated as predicted. Results indicated that genetic isolation of the ad hoc sample was as genetically isolated as predicted, with differentiation well above estimates made in samples from within well-connected habitat separated by similar geographic distances. This work has important practical implications for the control of tsetse and other disease vectors, because it provides a way to identify isolated populations where it will be safer and easier to implement vector control and that should be prioritized as study sites during the development and improvement of vector control methods.

Identifying adaptive loci can provide insight into the mechanisms underlying local adaptation. Genotype-environment association (GEA) methods, which identify these loci based on correlations between genetic and environmental data, are particularly promising. Univariate methods have dominated GEA, despite the high dimensional nature of genotype and environment. Multivariate methods, which analyze many loci simultaneously, may be better suited to these data since they consider how sets of markers covary in response to environment. These methods may also be more effective at detecting adaptive processes that result in weak, multilocus signatures. Here, we evaluate four multivariate methods, and five univariate and differentiation-based approaches, using published simulations of multilocus selection. We found that Random Forest performed poorly for GEA. Univariate GEAs performed better, but had low detection rates for loci under weak selection. Constrained ordinations showed a superior combination of low false positive and high true positive rates across all levels of selection. These results were robust across the demographic histories, sampling designs, sample sizes, and levels of population structure tested. The value of combining detections from different methods was variable, and depended on study goals and knowledge of the drivers of selection. Reanalysis of genomic data from gray wolves highlighted the unique, covarying sets of adaptive loci that could be identified using redundancy analysis, a constrained ordination. Although additional testing is needed, this study indicates that constrained ordinations are an effective means of detecting adaptation, including signatures of weak, multilocus selection, providing a powerful tool for investigating the genetic basis of local adaptation.

Global multiregional input-output databases (GMRIOs) became the standard tool for track ing environmental impacts through global supply chains. To date, several GMRIOs are available, but the numerical results differ. This paper considers how GMRIOs can be made more robust and authoritative. We show that GMRIOs need detail in environmentally relevant sectors. On the basis of a review of earlier work, we conclude that the highest uncertainty in footprint analyses is caused by the environmental data used in a GMRIO, followed by the size of country measured in gross domestic product (GDP) as fraction of the global total, the structure of the national table, and only at the end the structure of trade. We suggest the following to enhance robustness of results. In the short term, we recommend using the Single country National Accounts Consistent footprint approach, that uses official data for extensions and the national table for the country in question, combined with embodiments in imports calculated using a GMRIO. In a time period of 2 to 3 years, we propose work on harmonized environmental data for water, carbon, materials, and land, and use the aggregated Organization for Economic Cooperation and Development (OECD) Inter-Country Input-Output GMRIO as default in combination with detailing procedures developed in, for example, the EXIOBASE and Eora projects. In the long term, solutions should be coordinated by the international organizations such as the United Nations (UN) Statistical Division, OECD, and Eurostat. This could ensure that when input-output tables and trade data of individual countries are combined, that the global totals are consistent and that bilateral trade asymmetries are resolved.

The purpose of this study was to determine whether limited occurrence data for highly threatened species can provide useful spatial information to inform conservation. The study was conducted across central and southern China. We developed a habitat suitability model for the Critically Endangered Chinese giant salamander (Andrias davidianus) based on one biotic and three abiotic parameters from single‐site locality records, which represent the only relevant environmental data available for this species. We then validated model quality by testing whether increased percentage of predicted suitable habitat at the county level correlated with independent data on giant salamander presence. We randomly selected 48 counties containing historical records which were distinct from, and independent of, the single‐site records used to develop the model, and 47 additional counties containing >50% predicted suitable habitat. We interviewed 2,812 respondents near potential giant salamander habitat across these counties and tested for differences in respondent giant salamander reports between counties selected using each method. Our model predicts that suitable giant salamander habitat is found widely across central and southern China, with counties containing ≥50% predicted suitable habitat distributed in 13 provinces. Counties with historical records contain significantly more predicted suitable habitat than counties without historical records. There are no statistical differences in any patterns of respondent giant salamander reports in surveyed counties selected from our model compared with the areas of known historical giant salamander distribution. A Chinese giant salamander habitat suitability model with strong predictive power can be derived from the restricted range of environmental variables associated with limited available presence‐only occurrence records, constituting a cost‐effective strategy to guide spatial allocation of conservation planning. Few reported sightings were recent, however, with most being over 20 years old, so that identification of areas of suitable habitat does not necessarily indicate continued survival of the species at these locations.

Motivation: The availability of user-friendly, high-resolution global environmental datasets is crucial for bioclimatic modelling. For terrestrial environments, WorldClim has served this purpose since 2005, but equivalent marine data only became available in 2012, with pioneer initiatives like Bio-ORACLE providing data layers for several ecologically relevant variables. Currently, the available marine data packages have not yet been updated to the most recent Intergovernmental Panel on Climate Change (IPCC) predictions nor to present times, and are mostly restricted to the top surface layer of the oceans, precluding the modelling of a large fraction of the benthic diversity that inhabits deeper habitats. To address this gap, we present a significant update of Bio-ORACLE for new future climate scenarios, present-day conditions and benthic layers (near sea bottom). The reliability of data layers was assessed using a cross-validation framework against in situ quality-controlled data. This test showed a generally good agreement between our data layers and the global climatic patterns. We also provide a package of functions in the R software environment (sdmpredictors) to facilitate listing, extraction and management of data layers and allow easy integration with the available pipelines for bioclimatic modelling. Main types of variable contained: Surface and benthic layers for water temperature, salinity, nutrients, chlorophyll, sea ice, current velocity, phytoplankton, primary productivity, iron and light at bottom. Spatial location and grain: Global at 5 arcmin (c.0.08 degrees or 9.2 km at the equator). Time period and grain: Present (2000-2014) and future (2040-2050 and 2090-2100) environmental conditions based on monthly averages. Major taxa and level of measurement: Marine biodiversity associated with sea surface and epibenthic habitats. Software format: ASCII and TIFF grid formats for geographical information systems and a package of functions developed for R software.

Changes in body size and breeding phenology have been identified as two major ecological consequences of climate change, yet it remains unclear whether climate acts directly or indirectly on these variables. To better understand the relationship between climate and ecological changes, it is necessary to determine environmental predictors of both size and phenology using data from prior to the onset of rapid climate warming, and then to examine spatially explicit changes in climate, size, and phenology, not just general spatial and temporal trends. We used 100 years of natural history collection data for the wood frog, Lithobates sylvaticus with a range >9 million km2, and spatially explicit environmental data to determine the best predictors of size and phenology prior to rapid climate warming (1901–1960). We then tested how closely size and phenology changes predicted by those environmental variables reflected actual changes from 1961 to 2000. Size, phenology, and climate all changed as expected (smaller, earlier, and warmer, respectively) at broad spatial scales across the entire study range. However, while spatially explicit changes in climate variables accurately predicted changes in phenology, they did not accurately predict size changes during recent climate change (1961–2000), contrary to expectations from numerous recent studies. Our results suggest that changes in climate are directly linked to observed phenological shifts. However, the mechanisms driving observed body size changes are yet to be determined, given the less straightforward relationship between size and climate factors examined in this study. We recommend that caution be used in “space‐for‐time” studies where measures of a species’ traits at lower latitudes or elevations are considered representative of those under future projected climate conditions. Future studies should aim to determine mechanisms driving trends in phenology and body size, as well as the impact of climate on population density, which may influence body size.

Robots such as autonomous underwater vehicles (AUVs) and autonomous surface vehicles (ASVs) have been used for sensing and monitoring aquatic environments such as oceans and lakes. Environmental sampling is a challenging task because the environmental attributes to be observed can vary both spatially and temporally, and the target environment is usually a large and continuous domain whereas the sampling data is typically sparse and limited. The challenges require that the sampling method must be informative and efficient enough to catch up with the environmental dynamics. In this paper we present a planning and learning method that enables a sampling robot to perform persistent monitoring tasks by learning and refining a dynamic "data map" that models a spatiotemporal environment attribute such as ocean salinity content. Our environmental sampling framework consists of two components: to maximize the information collected, we propose an informative planning component that efficiently generates sampling waypoints that contain the maximal information; To alleviate the computational bottleneck caused by large-scale data accumulated, we develop a component based on a sparse Gaussian Process whose hyperparameters are learned online by taking advantage of only a subset of data that provides the greatest contribution. We validate our method with both simulations running on real ocean data and field trials with an ASV in a lake environment. Our experiments show that the proposed framework is both accurate in learning the environmental data map and efficient in catching up with the dynamic environmental changes.

Aim: Ideally, datasets for species distribution modelling (SDM) contain evenly sampled records covering the entire distribution of the species, confirmed absences and auxiliary ecophysiological data allowing informed decisions on relevant predictors. Unfortunately, these criteria are rarely met for marine organisms for which distributions are too often only scantly characterized and absences generally not recorded. Here, we investigate predictor relevance as a function of modelling algorithms and settings for a global dataset of marine species. Location: Global marine. Methods: We selected well-studied and identifiable species from all major marine taxonomic groups. Distribution records were compiled from public sources (e.g., OBIS, GBIF, Reef Life Survey) and linked to environmental data from Bio-ORACLE and MARSPEC. Using this dataset, predictor relevance was analysed under different variations of modelling algorithms, numbers of predictor variables, cross-validation strategies, sampling bias mitigation methods, evaluation methods and ranking methods. SDMs for all combinations of predictors from eight correlation groups were fitted and ranked, from which the top five predictors were selected as the most relevant. Results: We collected two million distribution records from 514 species across 18 phyla. Mean sea surface temperature and calcite are, respectively, the most relevant and irrelevant predictors. A less clear pattern was derived from the other predictors. The biggest differences in predictor relevance were induced by varying the number of predictors, the modelling algorithm and the sample selection bias correction. The distribution data and associated environmental data are made available through the R package marinespeed and at http://marinespeed.org. Main conclusions: While temperature is a relevant predictor of global marine species distributions, considerable variation in predictor relevance is linked to the SDM set-up. We promote the usage of a standardized benchmark dataset (MarineSPEED) for methodological SDM studies.

Aim Species distribution data play a pivotal role in the study of ecology, evolution, biogeography and biodiversity conservation. Although large amounts of location data are available and accessible from public databases, data quality remains problematic. Of the potential sources of error, positional errors are critical for spatial applications, particularly where these errors place observations beyond the environmental or geographical range of species. These outliers need to be identified, checked and removed to improve data quality and minimize the impact on subsequent analyses. Manually checking all species records within large multispecies datasets is prohibitively costly. This work investigates algorithms that may assist in the efficient vetting of outliers in such large datasets. Location We used real, spatially explicit environmental data derived from the western part of Victoria, Australia, and simulated species distributions within this same region. Methods By adapting species distribution modelling (SDM), we developed a pseudo-SDM approach for detecting outliers in species distribution data, which was implemented with random forest (RF) and support vector machine (SVM) resulting in two new methods: RF_pdSDM and SVM_pdSDM. Using virtual species, we compared eight existing multivariate outlier detection methods with these two new methods under various conditions. Results The two new methods based on the pseudo-SDM approach had higher true skill statistic (TSS) values than other approaches, with TSS values always exceeding 0. More than 70% of the true outliers in datasets for species with a low and intermediate prevalence can be identified by checking 10% of the data points with the highest outlier scores. Main conclusions Pseudo-SDM-based methods were more effective than other outlier detection methods. However, this outlier detection procedure can only be considered as a screening tool, and putative outliers must be examined by experts to determine whether they are actual errors or important records within an inherently biased set of data.

There is a significant convergence of interests in the research community efforts to advance the development and application of software resources (capable of handling the relevant mathematical algorithms to provide scalable information) for solving data science problems. Anaconda is one of the many open source platforms that facilitate the use of open source programming languages (R, Python) for large-scale data processing, predictive analytics, and scientific computing. The environmental research community may choose to adapt the use of either of the R or the Python programming languages for analyzing the data science problems on the Anaconda platform. This study demonstrated the applications of using Scikit-learn (a Python machine learning library package) on Anaconda platform for analyzing the in-bus carbon dioxide concentrations by (i) importing the data into Spyder (Python 3.6) in Anaconda, (ii) performing an exploratory data analysis, (iii) performing dimensionality reduction through RandomForestRegressor feature selection, (iv) developing statistical regression models, and (v) generating regression decision tree models with DecisionTreeRegressor feature. The readers may adopt the methods (inclusive of the Python coding) discussed in this article to successfully address their own data science problems. © 2017 American Institute of Chemical Engineers Environ Prog, 36: 1580–1586, 2017

Aim: Quantifying biological assemblages and their environment is a fundamental, yet statistically challenging task in conservation ecology. Here, we use a recently developed approach called Regions of Common Profile (RCP) to quantify and map the distribution of demersal fish assemblages in an ecologically significant region of the Southern Ocean to (1) gain ecological and management insights and (2) evaluate the utility of the new method for ecoregionalization. Location: Northern Kerguelen Plateau, Subantarctic Islands, Southern Ocean. Methods: The RCP approach is a multispecies, model‐based approach that can overcome many limitations of traditional distance‐based approaches. It simultaneously groups sites with a similar composition of species and describes the patterns of variation in assemblages using environmental data, allowing the prediction of assemblages across the study region. We apply RCP to a unique dataset of demersal fish occurrences across the northern Kerguelen Plateau to model and map the distribution of assemblages and examine the representativeness of the Heard Island and McDonald Island marine reserve. Results: We demonstrate that the RCP approach allows a direct and quantitative interpretation of the composition of assemblages as well as their environment. Further, the model reasonably predicts the occurrence of individual species across the plateau as well as the species composition of sites. We distinguish and map seven assemblages defined by depth, surface temperature and chlorophyll a. Shallow‐water assemblages contain a high proportion of endemic species, while deep‐water assemblages contain more cosmopolitan species. With the exception of one deep‐water assemblage, assemblages were well represented within the current Heard and McDonald Islands marine reserve. Main conclusions: The RCP is a valuable tool for classifying biological regions with a range of ecological and conservation management applications. Our results extend current ecological and biogeographic knowledge for the northern Kerguelen Plateau, and maps of the distribution of assemblages will be useful for ongoing spatial management.

This article presents a case study of the design of an autonomous wireless sensor network for environmental monitoring to provide data for understanding the relationships among food, energy, and water systems in suburban areas in Long Island New York. The study focuses on the existing water quality data for the two counties on Long Island, Nassau and Suffolk Counties, to understand the impact of energy and food production on the coastal waters of Long Island Sound and Peconic Bay. The analysis of existing data from the US Environmental Protection Agency database reveals the challenges in using such data to understand the relationships between water quality, weather events and identified point sources of pollutants. Maintaining on-going data collection programs is expensive, time-consuming and personnel intensive which is why this project examines new ways to obtain and utilize environmental data. The researchers propose the design of an autonomous wireless sensor network environmental monitoring system with an optimal sensor placement strategy to identify targeted locations for water quality monitoring. The goal is to provide environmental data that can facilitate a better understanding of the interactions of farming activities and energy production resulting in undesirable impacts on water quality. The proposed design can be used to provide real-time data to help stakeholders improve efficiency in resource management and advance early detection of potential hazardous events. © 2017 American Institute of Chemical Engineers Environ Prog, 2017

Ecological restoration has become an important strategy to conserve biodiversity and ecosystems services. To restore 15% of degraded ecosystems as stipulated by the Convention on Biological Diversity Aichi target 15, we developed a prioritization framework to identify potential priority sites for restoration in Mexico, a megadiverse country. We used the most current biological and environmental data on Mexico to assess areas of biological importance and restoration feasibility at national scale and engaged stakeholders and experts throughout the process. We integrated 8 criteria into 2 components (i.e., biological importance and restoration feasibility) in a spatial multicriteria analysis and generated 11 scenarios to test the effect of assigning different component weights. The priority restoration sites were distributed across all terrestrial ecosystems of Mexico; 64.1% were in degraded natural vegetation and 6% were in protected areas. Our results provide a spatial guide to where restoration could enhance the persistence of species of conservation concern and vulnerable ecosystems while maximizing the likelihood of restoration success. Such spatial prioritization is a first step in informing policy makers and restoration planners where to focus local and large-scale restoration efforts, which should additionally incorporate social and monetary cost-benefit considerations.

This software note announces a new open-source release of the Maxent software for modeling species distributions from occurrence records and environmental data, and describes a new R package for fitting such models. The new release (ver. 3.4.0) will be hosted online by the American Museum of Natural History, along with future versions. It contains small functional changes, most notably use of a complementary log-log (cloglog) transform to produce an estimate of occurrence probability. The cloglog transform derives from the recently-published interpretation of Maxent as an inhomogeneous Poisson process (IPP), giving it a stronger theoretical justification than the logistic transform which it replaces by default. In addition, the new R package, maxnet, fits Maxent models using the glmnet package for regularized generalized linear models. We discuss the implications of the IPP formulation in terms of model inputs and outputs, treating occurrence records as points rather than grid cells and interpreting the exponential Maxent model (raw output) as as an estimate of relative abundance. With these two open-source developments, we invite others to freely use and contribute to the software.