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Emerald ash borer (EAB; Agrilus planipennis Fairmaire, 1888) has been a persistent disturbance for ash forests in the United States since 2002. Of particular concern is the impact that EAB will have on the ecosystem functioning of wetlands dominated by black ash (Fraxinus nigra Marsh.). In preparation, forest managers need reliable and complete maps of black ash dominated stands. Traditionally, forest survey data from the United States Forest Inventory and Analysis (FIA) Program have provided rigorous measures of tree species at large spatial extents but are limited when providing estimates for smaller management units (e.g., stands). Fortunately, geospatial data can extend forest survey information by generating predictions of forest attributes at scales finer than those of the FIA sampling grid. In this study, geospatial data were integrated with FIA data in a randomForest model to estimate and map black ash dominated stands in northern Minnesota in the United States. The model produced low error rates (overall error = 14.5%; area under the curve (AUC) = 0.92) and was strongly informed by predictors from soil saturation and phenology. These results improve upon FIA-based spatial estimates at national extents by providing forest managers with accurate, fine-scale maps (30 m spatial resolution) of black ash stand dominance that could ultimately support landscape-level EAB risk and vulnerability assessments. Key words: compound topographic index (CTI), remote sensing, black ash, emerald ash borer, forest inventory. L'agrile du frene (AF; Agrilus planipennis Fairmaire, 1888) est une source de perturbation persistante dans les forets de frene depuis 2002 aux Etats-Unis. L'impact que l'AF aura sur le fonctionnement de l'ecosysteme des milieux humides domines par le frene noir (Fraxinus nigra Marsh.) est particulierement preoccupant. Pour se preparer, les gestionnaires forestiers ont besoin de cartes exhaustives et fiables des peuplements domines par le frene noir. Traditionnellement, les donnees d'inventaire forestier du programme d'analyse et d'inventaire forestier (AIF) des Etats-Unis ont fourni des mesures rigoureuses concernant les especes arborescentes pour de vastes etendues mais elles sont limitees lorsqu'il s'agit de fournir des estimations pour de plus petites unites d'amenagement (p. ex des peuplements). Heureusement, les donnees geospatiales peuvent etendre la portee des informations de l'inventaire forestier en generant des predictions des attributs forestiers a des echelles plus fines que la grille d'echantillonnage du programme d'AIF. Dans cette etude, des donnees geospatiales ont ete integrees avec des donnees du programme d'AIF dans une foret d'arbres decisionnels pour estimer et cartographier les peuplements domines par le frene noir dans le nord du Minnesota, aux Etats-Unis. Le modele a produit de faibles taux d'erreur (erreur globale = 14,5 %; surface sous la courbe (AUC) = 0,92) et etait fortement informe par des predicteurs ayant trait a la saturation du sol et a la phenologie. Ces resultats ameliorent les estimations spatiales fondees sur le programme d'AIF a l'echelle nationale en fournissant aux gestionnaires forestiers des cartes precises et a petite echelle (resolution spatiale de 30 m) des peuplements domines par le frene noir qui pourraient en fin de compte servir de base a l'evaluation des risques et de la vulnerabilite a l'AL. [Traduit par la Redaction] Mots-cles : indice topographique compose (CTI), teledetection, frene noir, agrile du frene, inventaire forestier., 1. Introduction A variety of exotic flora, fauna, and pathogens induce ecological changes to forest ecosystems worldwide, ultimately disrupting ecosystem processes and functions, including nutrient cycling, productivity, and wildlife habitat [...]

Aquatic invasive species (AIS) present major ecological and economic challenges globally, endangering ecosystems and human livelihoods. Managers and policy makers thus need tools to predict invasion risk and prioritize species and areas of concern, and they often use native range climate matching to determine whether a species could persist in a new location. However, climate matching for AIS often relies on air temperature rather than water temperature due to a lack of global water temperature data layers, and predictive power of models is seldom evaluated. We developed 12 global lake (water) temperature‐derived "BioLake" bioclimatic layers for distribution modeling of aquatic species and compared "climatch" climate matching predictions (from climatchR package) from BioLake with those based on BioClim temperature layers and with a null model. We did this for 73 established AIS in the United States, training the models on their ranges outside of the United States and Canada. Models using either set of climate layers outperformed the null expectation by a similar (but modest) amount on average, but some species were occasionally found in locations with low climatch scores. Mean US climatch scores were higher for most species when using air temperature. Including additional climate layers in models reduced mean climatch scores, indicating that commonly used climatch score thresholds are not absolute but can be context specific and may require calibration based upon climate data used. Although finer resolution global lake temperature data would likely improve predictions, our BioLake layers provide a starting point for aquatic species distribution modeling. Climate matching was most effective for some species that originated at low latitudes or had small ranges. Climatch scores remain useful but limited for predicting AIS risk, perhaps because current ranges seldom fully reflect climatic tolerances (fundamental niches). Managers could consider climate matching as one of a suite of tools that can be used in AIS prioritization. [ABSTRACT FROM AUTHOR]

Less than 7000 cheetahs (Acinonyx jubatus) persist in Africa. Although human–wildlife conflict, habitat degradation, and loss of prey are major threats to cheetah populations, illegal trade in live cubs for pets may have the most significant impact on populations in the Horn of Africa. We developed a novel, stepwise decision support tool to predict probable trafficking routes by leveraging the power of distinct modeling approaches. First, we created a cheetah habitat suitability index (HSI) to determine where source cheetah populations may occur. We then created a trafficking network model linking known and predicted cheetah populations with documented destinations in the Arabian Peninsula. A significant area in Eastern Ethiopia and Northern Somalia was estimated to harbor undocumented cheetahs. When these predicted populations were used as a supply source, the trafficking network model showed multiple routes passing through Somaliland and across the Gulf of Aden to Yemen, supporting the notion that undocumented cheetahs may be supplying pet market demands. Though we demonstrate how our decision support tool can inform law enforcement, conservation strategies, and community engagement, we caution that our results are not fully validated due to limited accessibility, alternative trafficking routes, and the cryptic nature of illegal wildlife trade. [ABSTRACT FROM AUTHOR]