Generation lengths of the world's birds and their implications for extinction risk.

Birds have been comprehensively assessed on the International Union for Conservation of Nature (IUCN) Red List more times than any other taxonomic group. However, to date, generation lengths have not been systematically estimated to scale population trends when undertaking assessments, as required by the criteria of the IUCN Red List. We compiled information from major databases of published life-history and trait data for all birds and imputed missing life-history data as a function of species traits with generalized linear mixed models. Generation lengths were derived for all species, based on our modeled values of age at first breeding, maximum longevity, and annual adult survival. The resulting generation lengths varied from 1.42 to 27.87 years (median 2.99). Most species (61%) had generation lengths <3.33 years, meaning that the period of 3 generations-over which population declines are assessed under criterion A-was <10 years, which is the value used for IUCN Red List assessments of species with short generation times. For these species, our trait-informed estimates of generation length suggested that 10 years is a robust precautionary value for threat assessment. In other cases, however, for whole families, genera, or individual species, generation length had a substantial impact on their estimated extinction risk, resulting in higher extinction risk in long-lived species than in short-lived species. Although our approach effectively addressed data gaps, generation lengths for some species may have been underestimated due to a paucity of life-history data. Overall, our results will strengthen future extinction-risk assessments and augment key databases of avian life-history and trait data.

Duraciones Generacionales de las Aves del Mundo y sus Implicaciones para el Riesgo de Extinción Resumen Las aves han sido valoradas integralmente en la Lista Roja de la Unión Internacional para la Conservación de la Naturaleza (UICN) más veces que cualquier otro grupo taxonómico. Sin embargo, a la fecha, las duraciones generacionales no han sido estimadas sistemáticamente para escalar las tendencias poblacionales cuando se realizan las valoraciones, como lo requieren los criterios de la Lista Roja de la UICN. Compilamos información a partir de las principales bases de datos de historias de vida y datos de características publicadas para todas las aves e imputamos los datos faltantes de historias de vida como una función de las características de especies con modelos lineales mixtos generalizados. La duración por generación estuvo derivada para todas las especies con base en nuestros valores modelados de edad durante la primera reproducción, la longevidad máxima y la supervivencia anual de adultos. La duración por generación resultante varió de 1.42 a 27.87 años (mediana: 2.99). La mayoría de las especies (61%) tuvo una duración generacional <3.33 años, lo que significa que el periodo de tres generaciones - durante el cual se valoran las declinaciones poblacionales bajo el Criterio A - es <10 años, el cual es el valor usado por la Lista Roja de la UICN para la valoración de especies con tiempos generacionales cortos. Para estas especies, nuestras estimaciones de duración por generación informados por características sugieren que diez años es un valor preventivo sólido para la valoración de amenazas. Para otros casos, sin embargo, como familias o géneros enteros o especies individuales, la duración generacional tuvo un impacto sustancial sobre su riesgo de extinción estimado, resultando así en un riesgo de extinción más elevado para las especies con mayor longevidad que aquellas especies con una menor longevidad. Aunque nuestra estrategia lidió efectivamente con los vacíos en los datos, la duración generacional para algunas especies podría estar subestimada debido a la escasez de datos de historia de vida. En general, nuestros resultados fortalecerán las futuras valoraciones de extinción de riesgo y aumentarán las bases de datos importantes de la historia de vida de las aves y los datos de características.

New insights into the architecture of the islet of Langerhans: a focused cross-species assessment.

The human genome project and its search for factors underlying human diseases has fostered a major human research effort. Therefore, unsurprisingly, in recent years we have observed an increasing number of studies on human islet cells, including disease approaches focusing on type 1 and type 2 diabetes. Yet, the field of islet and diabetes research relies on the legacy of rodent-based investigations, which have proven difficult to translate to humans, particularly in type 1 diabetes. Whole islet physiology and pathology may differ between rodents and humans, and thus a comprehensive cross-species as well as species-specific view on islet research is much needed. In this review we summarise the current knowledge of interspecies islet cytoarchitecture, and discuss its potential impact on islet function and future perspectives in islet pathophysiology research.

A data accuracy evaluation strategy to improve the representation of potential pesticide use areas for endangered species assessments.

The use of "best available data" is a fundamental requirement for all scientific forms of analysis. This paper discusses ways to improve the accuracy of data used to evaluate the potential impacts of pesticides on species that are listed as threatened or endangered under the Endangered Species Act (ESA) by ensuring the best available spatial data representing pesticide use sites are applied correctly. A decision matrix is presented that uses accuracy information from metadata contained in the US Department of Agriculture's (USDA's) Cropland Data Layer (CDL) and the Census of Agriculture (CoA) to improve how labeled pesticide use sites are spatially delineated. We suggest recommendations for the current pesticide evaluation process used by the US Environmental Protection Agency (USEPA) and subsequently by the US Fish and Wildlife Services and National Marine Fisheries Service (collectively known as the Services) in Section 7 consultation activities. The decision matrix is applied to each cultivated land layer in the USDA's CDL with recommendations for how best to use each layer in the evaluation process. Application of this decision matrix will lead to improved representation of labeled uses and more accurate overlap calculations used in the assessment of potential impacts of pesticides on endangered species. Integr Environ Assess Manag 2022;18:1655-1666. © 2022 SETAC.

Potential distributional shifts in North America of allelopathic invasive plant species under climate change models.

Predictive studies play a crucial role in the study of biological invasions of terrestrial plants under possible climate change scenarios. Invasive species are recognized for their ability to modify soil microbial communities and influence ecosystem dynamics. Here, we focused on six species of allelopathic flowering plants-Ailanthus altissima, Casuarina equisetifolia, Centaurea stoebe ssp. micranthos, Dioscorea bulbifera, Lantana camara, and Schinus terebinthifolia-that are invasive in North America and examined their potential to spread further during projected climate change. We used Species Distribution Models (SDMs) to predict future suitable areas for these species in North America under several proposed future climate models. ENMEval and Maxent were used to develop SDMs, estimate current distributions, and predict future areas of suitable climate for each species. Areas with the greatest predicted suitable climate in the future include the northeastern and the coastal northwestern regions of North America. Range size estimations demonstrate the possibility of extreme range loss for these invasives in the southeastern United States, while new areas may become suitable in the northeastern United States and southeastern Canada. These findings show an overall northward shift of suitable climate during the next few decades, given projected changes in temperature and precipitation. Our results can be utilized to analyze potential shifts in the distribution of these invasive species and may aid in the development of conservation and management plans to target and control dissemination in areas at higher risk for potential future invasion by these allelopathic species.

A probabilistic risk assessment for the Kirtland's warbler potentially exposed to chlorpyrifos and malathion during the breeding season and migration.

Two organophosphate pesticides, chlorpyrifos and malathion, are currently undergoing reregistration in the United States and were recently used by the US Environmental Protection Agency (USEPA) as case studies to develop a national procedure for evaluating risks to endangered species. One of the endangered bird species considered by the USEPA was the Kirtland's warbler (Setophaga kirtlandii). The Kirtland's warbler is an endangered migratory species that nests exclusively in young jack pine stands in Michigan and Wisconsin, and winters in the Bahamas. We developed probabilistic models to assess the risks of chlorpyrifos and malathion to Kirtland's warblers during the breeding season and the spring and fall migrations. The breeding area model simulates acute and chronic exposure and risk to each of 10 000 birds over a 60-d period following initial pesticide application. The model is highly species specific with regard to the foraging behavior of Kirtland's warblers during the breeding season. We simulated the maximum application rate and number of applications allowed on the labels for representative use patterns that could be found within 3 km of the breeding areas of Kirtland's warbler. The migration model simulates 10 000 birds during the course of their 12- to 23-d migration between their breeding area and the Bahamas. The model takes advantage of more than a century of observations of when, where, and for how long Kirtland's warblers forage in different habitats during the course of their migration. The data indicate that warblers only infrequently stop over in habitats that could be treated with chlorpyrifos and malathion. The breeding area and migration models resulted in predictions of very low acute and chronic risk for both pesticides to Kirtland's warblers. These results were expected, given that field observations indicate that the Kirtland's warbler has dramatically increased in abundance in recent decades. Integr Environ Assess Manag 2018;14:252-269. © 2017 SETAC.

Overcoming the Data Crisis in Biodiversity Conservation.

How can we track population trends when monitoring data are sparse? Population declines can go undetected, despite ongoing threats. For example, only one of every 200 harvested species are monitored. This gap leads to uncertainty about the seriousness of declines and hampers effective conservation. Collecting more data is important, but we can also make better use of existing information. Prior knowledge of physiology, life history, and community ecology can be used to inform population models. Additionally, in multispecies models, information can be shared among taxa based on phylogenetic, spatial, or temporal proximity. By exploiting generalities across species that share evolutionary or ecological characteristics within Bayesian hierarchical models, we can fill crucial gaps in the assessment of species' status with unparalleled quantitative rigor.

A probabilistic approach for estimating the spatial extent of pesticide agricultural use sites and potential co-occurrence with listed species for use in ecological risk assessments.

A crop footprint refers to the estimated spatial extent of growing areas for a specific crop, and is commonly used to represent the potential "use site" footprint for a pesticide labeled for use on that crop. A methodology for developing probabilistic crop footprints to estimate the likelihood of pesticide use and the potential co-occurrence of pesticide use and listed species locations was tested at the national scale and compared to alternative methods. The probabilistic aspect of the approach accounts for annual crop rotations and the uncertainty in remotely sensed crop and land cover data sets. The crop footprints used historically are derived exclusively from the National Land Cover Database (NLCD) Cultivated Crops and/or Pasture/Hay classes. This approach broadly aggregates agriculture into 2 classes, which grossly overestimates the spatial extent of individual crops that are labeled for pesticide use. The approach also does not use all the available crop data, represents a single point in time, and does not account for the uncertainty in land cover data set classifications. The probabilistic crop footprint approach described herein incorporates best available information at the time of analysis from the National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) for 5 y (2008-2012 at the time of analysis), the 2006 NLCD, the 2007 NASS Census of Agriculture, and 5 y of NASS Quick Stats (2008-2012). The approach accounts for misclassification of crop classes in the CDL by incorporating accuracy assessment information by state, year, and crop. The NLCD provides additional information to improve the CDL crop probability through an adjustment based on the NLCD accuracy assessment data using the principles of Bayes' Theorem. Finally, crop probabilities are scaled at the state level by comparing against NASS surveys (Census of Agriculture and Quick Stats) of reported planted acres by crop. In an example application of the new method, the probabilistic crop footprint for soybean resulted in national and statewide soybean acreages that are within the error bounds of the average reported NASS yearly soybean acreage over the same time period, whereas the method using only NLCD resulted in an acreage that is over 4 times the survey acreage. When the probabilistic crop footprint for soybean was used in a co-occurrence analysis with listed species locations, the number of potentially proximal species identified was half the number based on the standard NLCD crop footprint method (276 species with the probabilistic crop footprint vs 511 for the conventional method). The probabilistic crop footprint methodology allows for a more comprehensive and representative understanding of the potential pesticide use footprint co-occurrence with endangered species locations for use in effects determinations.

Effects of wind energy generation and white-nose syndrome on the viability of the Indiana bat.

Wind energy generation holds the potential to adversely affect wildlife populations. Species-wide effects are difficult to study and few, if any, studies examine effects of wind energy generation on any species across its entire range. One species that may be affected by wind energy generation is the endangered Indiana bat (Myotis sodalis), which is found in the eastern and midwestern United States. In addition to mortality from wind energy generation, the species also faces range-wide threats from the emerging infectious fungal disease, white-nose syndrome (WNS). White-nose syndrome, caused by Pseudogymnoascus destructans, disturbs hibernating bats leading to high levels of mortality. We used a spatially explicit full-annual-cycle model to investigate how wind turbine mortality and WNS may singly and then together affect population dynamics of this species. In the simulation, wind turbine mortality impacted the metapopulation dynamics of the species by causing extirpation of some of the smaller winter colonies. In general, effects of wind turbines were localized and focused on specific spatial subpopulations. Conversely, WNS had a depressive effect on the species across its range. Wind turbine mortality interacted with WNS and together these stressors had a larger impact than would be expected from either alone, principally because these stressors together act to reduce species abundance across the spectrum of population sizes. Our findings illustrate the importance of not only prioritizing the protection of large winter colonies as is currently done, but also of protecting metapopulation dynamics and migratory connectivity.