Quantifying the impact of vegetation-based metrics on species persistence when choosing offsets for habitat destruction.

Developers are often required by law to offset environmental impacts through targeted conservation actions. Most offset policies specify metrics for calculating offset requirements, usually by assessing vegetation condition. Despite widespread use, there is little evidence to support the effectiveness of vegetation-based metrics for ensuring biodiversity persistence. We compared long-term impacts of biodiversity offsetting based on area only; vegetation condition only; area × habitat suitability; and condition × habitat suitability in development and restoration simulations for the Hunter Region of New South Wales, Australia. We simulated development and subsequent offsetting through restoration within a virtual landscape, linking simulations to population viability models for 3 species. Habitat gains did not ensure species persistence. No net loss was achieved when performance of offsetting was assessed in terms of amount of habitat restored, but not when outcomes were assessed in terms of persistence. Maintenance of persistence occurred more often when impacts were avoided, giving further support to better enforce the avoidance stage of the mitigation hierarchy. When development affected areas of high habitat quality for species, persistence could not be guaranteed. Therefore, species must be more explicitly accounted for in offsets, rather than just vegetation or habitat alone. Declines due to a failure to account directly for species population dynamics and connectivity overshadowed the benefits delivered by producing large areas of high-quality habitat. Our modeling framework showed that the benefits delivered by offsets are species specific and that simple vegetation-based metrics can give misguided impressions on how well biodiversity offsets achieve no net loss.

Cuantificación del Impacto de las Medidas Basadas en la Vegetación sobre la Persistencia de las Especies cuando se Eligen las Compensaciones por la Destrucción del Hábitat Resumen Con frecuencia se requiere por ley que los desarrolladores compensen los impactos ambientales por medio de acciones de conservación. La mayoría de las políticas de compensación especifican medidas para calcular los requerimientos de cada compensación, generalmente mediante la evaluación de las condiciones de la vegetación. A pesar del uso extenso de estas medidas basadas en la vegetación, existe muy poca evidencia que respalde su efectividad para asegurar la persistencia de la biodiversidad. Comparamos los impactos a largo plazo de las compensaciones de biodiversidad basadas solamente en el área; solamente en la condición de la vegetación; la idoneidad del área x hábitat; y la idoneidad condición x hábitat en las simulaciones de desarrollo y restauración para la Región Hunter de Nueva Gales del Sur, Australia. Simulamos el desarrollo y las compensaciones subsecuentes mediante la restauración dentro de un paisaje virtual, conectando las simulaciones con los modelos de viabilidad poblacional para tres especies. Las ganancias del hábitat no aseguraron la persistencia de las especies. No hubo pérdida neta cuando el desempeño de las compensaciones se evaluó en relación con la persistencia. El mantenimiento de la persistencia ocurrió más seguido cuando se evitaron los impactos, lo que proporciona un mayor respaldo para mejorar la aplicación de la fase de prevención de la jerarquía de mitigación. Cuando el desarrollo afectó a las áreas con una alta calidad de hábitat para las especies, no se pudo garantizar la persistencia. Por lo tanto, las especies deben considerarse más explícitamente en las compensaciones, en lugar de sólo considerar a la vegetación o al hábitat. Las declinaciones causadas por la falta de consideración directa de las dinámicas poblacionales de las especies y de la conectividad opacaron los beneficios producidos por las grandes áreas de hábitat de alta calidad. Nuestro marco de trabajo para el modelado demostró que los beneficios producidos por las compensaciones son específicos para cada especie y que las medidas simples basadas en la vegetación pueden brindar impresiones mal informadas sobre qué tanto influyen las compensaciones de biodiversidad en la no pérdida neta.

Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping.

In this research, eight individual machine learning and statistical models are implemented and compared, and based on their results, seven ensemble models for flood susceptibility assessment are introduced. The individual models included artificial neural networks, classification and regression trees, flexible discriminant analysis, generalized linear model, generalized additive model, boosted regression trees, multivariate adaptive regression splines, and maximum entropy, and the ensemble models were Ensemble Model committee averaging (EMca), Ensemble Model confidence interval Inferior (EMciInf), Ensemble Model confidence interval Superior (EMciSup), Ensemble Model to estimate the coefficient of variation (EMcv), Ensemble Model to estimate the mean (EMmean), Ensemble Model to estimate the median (EMmedian), and Ensemble Model based on weighted mean (EMwmean). The data set covered 201 flood events in the Haraz watershed (Mazandaran province in Iran) and 10,000 randomly selected non-occurrence points. Among the individual models, the Area Under the Receiver Operating Characteristic (AUROC), which showed the highest value, belonged to boosted regression trees (0.975) and the lowest value was recorded for generalized linear model (0.642). On the other hand, the proposed EMmedian resulted in the highest accuracy (0.976) among all models. In spite of the outstanding performance of some models, nevertheless, variability among the prediction of individual models was considerable. Therefore, to reduce uncertainty, creating more generalizable, more stable, and less sensitive models, ensemble forecasting approaches and in particular the EMmedian is recommended for flood susceptibility assessment.

Predicting climate heating impacts on riverine fish species diversity in a biodiversity hotspot region.

Co-occurring biodiversity and global heating crises are systemic threats to life on Earth as we know it, especially in relatively rare freshwater ecosystems, such as in Iran. Future changes in the spatial distribution and richness of 131 riverine fish species were investigated at 1481 sites in Iran under optimistic and pessimistic climate heating scenarios for the 2050s and 2080s. We used maximum entropy modeling to predict species' potential distributions by hydrologic unit (HU) occupancy under current and future climate conditions through the use of nine environmental predictor variables. The most important variable determining fish occupancy was HU location, followed by elevation, climate variables, and slope. Thirty-seven species were predicted to decrease their potential habitat occupancy in all future scenarios. The southern Caspian HU faces the highest future species reductions followed by the western Zagros and northwestern Iran. These results can be used by managers to plan conservational strategies to ease the dispersal of species, especially those that are at the greatest risk of extinction or invasion and that are in rivers fragmented by dams.

Spatial Variation in Australian Neonicotinoid Usage and Priorities for Resistance Monitoring.

Australia is the third largest exporting country of cereals and a leader in other major commodity crops, yet little data exist on pesticide usage patterns in agriculture. This knowledge gap limits the management of off-target chemical impacts, such as the evolution of pesticide resistance. Here, for the first time, we quantify spatial patterns in neonicotinoid applications in Australia by coalescing land use data with sales and market research data contributed by agrichemical and agribusiness companies. An example application to resistance management is explored through the development of recommendations for the cosmopolitan crop pest, Myzus persicae (Sulzer) (Hemiptera: Aphididae), utilizing spatial statistical models. This novel dataset identified Australian neonicotinoid usage patterns, with most neonicotinoid products in Australia applied as cereal, canola, cotton and legume seed treatments and soil applications in sugarcane. Importantly, there were strong regional differences in pesticide applications, which will require regionally specific strategies to manage off-target impacts. Indeed, the estimated spatial grid of neonicotinoid usage demonstrated a statistically significant influence on the distribution of M. persicae neonicotinoid resistance, indicating off-target impacts are unevenly distributed in space. Future research on neonicotinoid usage will be supported by the spatial grids generated and made available through this study. Overall, neonicotinoid pesticides are widely relied upon throughout Australia's plant production systems but will face increasing pressure from resistance evolution, emerging research on off-target impacts, and stricter regulatory pressures.