Identifying socioeconomic and biophysical factors driving forest loss in protected areas.

Protected areas (PAs) are a commonly used strategy to confront forest conversion and biodiversity loss. Although determining drivers of forest loss is central to conservation success, understanding of them is limited by conventional modeling assumptions. We used random forest regression to evaluate potential drivers of deforestation in PAs in Mexico, while accounting for nonlinear relationships and higher order interactions underlying deforestation processes. Socioeconomic drivers (e.g., road density, human population density) and underlying biophysical conditions (e.g., precipitation, distance to water, elevation, slope) were stronger predictors of forest loss than PA characteristics, such as age, type, and management effectiveness. Within PA characteristics, variables reflecting collaborative and equitable management and PA size were the strongest predictors of forest loss, albeit with less explanatory power than socioeconomic and biophysical variables. In contrast to previously used methods, which typically have been based on the assumption of linear relationships, we found that the associations between most predictors and forest loss are nonlinear. Our results can inform decisions on the allocation of PA resources by strengthening management in PAs with the highest risk of deforestation and help preemptively protect key biodiversity areas that may be vulnerable to deforestation in the future.

Identificación de los factores biofísicos y socioeconómicos que impulsan la pérdida de bosques en las áreas protegidas Resumen Las áreas protegidas son una estrategia de uso común para hacer frente a la conversión forestal y la pérdida de biodiversidad. Aunque determinar los factores que impulsan la pérdida de bosques es fundamental para el éxito de la conservación, su comprensión se ve limitada por los supuestos de modelación convencionales. Utilizamos la regresión de bosques aleatorios para evaluar los posibles impulsores de la deforestación en las áreas protegidas de México, considerando las relaciones no lineales y las interacciones de orden superior que subyacen a los procesos de deforestación. Los impulsores socioeconómicos (densidad de carreteras, densidad de población humana) y las condiciones biofísicas subyacentes (precipitaciones, distancia al agua, elevación, pendiente) fueron predictores más fuertes de la pérdida de bosques que las características de las áreas protegidas, como la edad, el tipo y la efectividad de la gestión. Dentro de las características de las áreas protegidas, las variables que reflejan una gestión colaborativa y equitativa y el tamaño del área protegida fueron los predictores más potentes de la pérdida de bosques, aunque con menor poder explicativo que las variables socioeconómicas y biofísicas. A diferencia de los métodos utilizados anteriormente, que suelen basarse en el supuesto de relaciones lineales, observamos que las asociaciones entre la mayoría de los predictores y la pérdida de bosques no son lineales. Nuestros resultados pueden servir de base para la toma de decisiones sobre la asignación de los recursos para las áreas protegidas, reforzando la gestión en las zonas protegidas con mayor riesgo de deforestación y ayudando a proteger de forma preventiva zonas clave para la biodiversidad que pueden ser vulnerables a la deforestación en el futuro.

Predicting landscape-scale biodiversity recovery by natural tropical forest regrowth.

Natural forest regrowth is a cost-effective, nature-based solution for biodiversity recovery, yet different socioenvironmental factors can lead to variable outcomes. A critical knowledge gap in forest restoration planning is how to predict where natural forest regrowth is likely to lead to high levels of biodiversity recovery, which is an indicator of conservation value and the potential provisioning of diverse ecosystem services. We sought to predict and map landscape-scale recovery of species richness and total abundance of vertebrates, invertebrates, and plants in tropical and subtropical second-growth forests to inform spatial restoration planning. First, we conducted a global meta-analysis to quantify the extent to which recovery of species richness and total abundance in second-growth forests deviated from biodiversity values in reference old-growth forests in the same landscape. Second, we employed a machine-learning algorithm and a comprehensive set of socioenvironmental factors to spatially predict landscape-scale deviation and map it. Models explained on average 34% of observed variance in recovery (range 9-51%). Landscape-scale biodiversity recovery in second-growth forests was spatially predicted based on socioenvironmental landscape factors (human demography, land use and cover, anthropogenic and natural disturbance, ecosystem productivity, and topography and soil chemistry); was significantly higher for species richness than for total abundance for vertebrates (median range-adjusted predicted deviation 0.09 vs. 0.34) and invertebrates (0.2 vs. 0.35) but not for plants (which showed a similar recovery for both metrics [0.24 vs. 0.25]); and was positively correlated for total abundance of plant and vertebrate species (Pearson r = 0.45, p = 0.001). Our approach can help identify tropical and subtropical forest landscapes with high potential for biodiversity recovery through natural forest regrowth.

Predicción de la Recuperación de la Biodiversidad a Escala de Paisaje según la Regeneración Natural del Bosque Tropical Resumen La regeneración natural del bosque es una solución rentable para la recuperación de la biodiversidad basada en la naturaleza, sin embargo, los diferentes factores socioambientales pueden derivar en resultados variables. Cómo predecir la ubicación en donde la regeneración natural del bosque recuperará los niveles de biodiversidad, los cuales son un indicador del valor de la conservación y un suministro potencial de diferentes servicios ambientales, es un vacío de conocimiento importante en la planeación de la restauración forestal. Buscamos predecir y mapear la recuperación a escala de paisaje de la riqueza de especies y la abundancia total de vertebrados, invertebrados y plantas en bosques tropicales y subtropicales de segundo crecimiento para guiar la planeación de la restauración. Primero, realizamos un metaanálisis mundial para cuantificar la medida a la que se desvió la recuperación de la riqueza y la abundancia total de especies en los bosques de segundo crecimiento de los valores de biodiversidad en los bosques antiguos referenciales en el mismo paisaje. Después, utilizamos un algoritmo de aprendizaje automático y un conjunto integral de factores socioambientales para predecir espacialmente la desviación a escala de paisaje para después mapearla. Los modelos explicaron en promedio el 34% de la varianza observada en la recuperación (rango de 9-51%). La recuperación de la biodiversidad a escala de paisaje en los bosques de segundo crecimiento pudo predecirse espacialmente con base en los factores socioambientales del paisaje (demografía humana, uso y cobertura del suelo, alteraciones naturales y antropogénicas, productividad del ecosistema, tipo de topografía y de suelo); fue significativamente más alta para la riqueza de especies que para la abundancia total de vertebrados (desviación media pronosticada ajustada al rango de 0.09 versus 0.34) e invertebrados (0.2 versus 0.35) pero no para las plantas (las cuales mostraron una recuperación similar para ambas medidas [0.24 versus 0.25]); y tuvo una correlación positiva para la abundancia de especies de plantas y vertebrados (Pearson r =0.45, p=0.001). Nuestra estrategia puede ayudar a identificar los paisajes de bosques tropicales y subtropicales con un potencial alto para la recuperación de la biodiversidad por medio de la regeneración natural del bosque.

Generalization of threats attributed to large carnivores in areas of high human-wildlife conflict.

Fear-induced generalization of threats to noninimical stimuli is a behavioral tendency of humans to minimize exposure to potential threats. In human-carnivore conflict zones, people often generalize their fear of predation by obligate carnivores to nonobligate carnivores despite differences in species' predation rates. We investigated the effect of a perceived threat of large obligate carnivores to livestock on tolerance and perception of striped hyena (Hyaena hyaena) in an area of high human-carnivore conflict. We surveyed 197 households through asemistructured questionnaire to determine people's perception and tolerance of striped hyenas in Sathyamangalam and Mudumalai Tiger Reserves after identification of the current distribution range of hyena determined through camera trap and sign surveys. Through the random forest algorithm, we modeled the level of tolerance of striped hyena as a function of loss of livestock to predation and from disease, the perceived threat of predation by hyena, and other socioeconomic attributes. Animal husbandry was a major source of income but was severely affected by livestock loss due to predation and disease. Sixty-nine percent of people were uncertain about predatory behavior of hyena; out of that, 23% reported a negative conservation attitude. Only 6 instances of hyena depredation on livestock and 2 on dogs were reported. Our model confirmed that economic instability associated with increased loss to predation and disease, livestock dependency, and a decrease in family annual income negatively affected people's tolerance of hyena. Perceptual uncertainty related to predatory behavior of hyena also negatively affected people's tolerance. In our study area, economic instability and perceptual uncertainty led to generalization of fear of large carnivores to a nonobligate predator. Such generalization may affect the attitude of people toward many other species. Understanding the role of economic instability and perceptual uncertainty should facilitate conservation of species, such as the hyena, that are vulnerable to false generalization.

Generalización de las Amenazas Atribuidas a los Grandes Carnívoros en las Áreas con un Elevado Conflicto Humano-Fauna Resumen La generalización de las amenazas inducidas por el miedo a los estímulos no desfavorables es una tendencia del comportamiento humano para minimizar la exposición ante las amenazas potenciales. En las zonas de conflicto humano-carnívoros, las personas transfieren con frecuencia su miedo a los carnívoros obligados hacia los no obligados a pesar de las diferencias en las tasas de depredación de las espcies. Investigamos el efecto de la amenaza percibida que representan los grandes carnívoros obligados para el ganado sobre la tolerancia a y la percepción de la hiena rayada (Hyaena hyaena) en un área con un conflicto elevado entre humanos y carnívoros. Encuestamos a 197 hogares con un cuestionario semiestructurado para determinar la percepción y tolerancia que tienen las personas hacia la hiena rayada en las reservas de tigres de Satayamangalam y Mudumalai después de identificar la distribución actual de la hiena mediante cámaras trampa y censos de indicios. Modelamos el nivel de tolerancia a la hiena rayada con el algoritmo de bosque aleatorio como función de la pérdida de ganado por depredación o enfermedad, la amenaza percibida de la hiena como depredador y otros atributos socioeconómicos. La crianza de animales fue una gran fuente de ingresos aunque con severas afectaciones por la pérdida de ganado por depredación o enfermedad. El 69% de las personas tenían incertidumbre acerca del comportamiento de depredación de la hiena; de ese porcentaje, el 23% reportó una actitud negativa de conservación. En cuanto a las hienas, sólo se reportaron seis instancias de depredación de ganado y dos de perros. Nuestro modelo confirmó que la inestabilidad económica asociada con el incremento en las pérdidas por depredación y enfermedad, la dependencia por el ganado y una reducción en el ingreso anual familiar afectó negativamente la tolerancia que tienen las personas hacia las hienas. La incertidumbre percibida relacionada con el comportamiento de depredación de las hienas también afectó negativamente la tolerancia de las personas. La inestabilidad económica y la incertidumbre percibida derivaron en una transferencia del miedo a los grandes carnívoros hacia los depredadores no obligados en nuestra área de estudio. Dicha transferencia puede afectar la actitud de las personas hacia muchas otras especies. El conocimiento sobre el papel que tiene la inestabilidad económica y la incertidumbre percibida debería facilitar la conservación de especies que son vulnerables a las falsas generalizaciones, como es el caso de la hiena.

Efficacy of extracting indices from large-scale acoustic recordings to monitor biodiversity.

Passive acoustic monitoring could be a powerful way to assess biodiversity across large spatial and temporal scales. However, extracting meaningful information from recordings can be prohibitively time consuming. Acoustic indices (i.e., a mathematical summary of acoustic energy) offer a relatively rapid method for processing acoustic data and are increasingly used to characterize biological communities. We examined the relationship between acoustic indices and the diversity and abundance of biological sounds in recordings. We reviewed the acoustic-index literature and found that over 60 indices have been applied to a range of objectives with varying success. We used 36 of the most indicative indices to develop a predictive model of the diversity of animal sounds in recordings. Acoustic data were collected at 43 sites in temperate terrestrial and tropical marine habitats across the continental United States. For terrestrial recordings, random-forest models with a suite of acoustic indices as covariates predicted Shannon diversity, richness, and total number of biological sounds with high accuracy (R2  ≥ 0.94, mean squared error [MSE] ≤170.2). Among the indices assessed, roughness, acoustic activity, and acoustic richness contributed most to the predictive ability of models. Performance of index models was negatively affected by insect, weather, and anthropogenic sounds. For marine recordings, random-forest models poorly predicted Shannon diversity, richness, and total number of biological sounds (R2 ≤ 0.40, MSE ≥ 195). Our results suggest that using a combination of relevant acoustic indices in a flexible model can accurately predict the diversity of biological sounds in temperate terrestrial acoustic recordings. Thus, acoustic approaches could be an important contribution to biodiversity monitoring in some habitats.

Meta-analysis of the effects of rice-field abandonment on biodiversity in Japan.

Farmland abandonment is increasing worldwide. Concurrently, some conservationists view this as an opportunity for biodiversity restoration (i.e., rewilding). Due to a lack of data, however, it remains unclear whether farmland abandonment increases biodiversity in different farmland types and surrounding environments. Information is particularly scarce for Asia, home to one-third of identified biodiversity hotspots and where dominant farmlands (i.e., rice fields) are often viewed as substitutes for natural wetlands. We conducted the first meta-analysis of the impacts of rice-field abandonment on biodiversity, in which we considered multiscale factors, such as taxon surveyed, landscape context, and climate. Species richness and abundance after abandonment decreased to 56-72%. This reduced biodiversity was unlikely to recover, at least for plant species richness, even after 10-15 years. These results suggest rewilding will not necessarily be achieved by rice-field abandonment. Moreover, there was a pronounced biodiversity decline under dry conditions (e.g., low precipitation), especially for organisms closely associated with aquatic environment, such as amphibians and fish. Biodiversity reduction was least pronounced for birds and mammals. Such context dependency may suggest that impacts of farmland abandonment can be predicted by considering biological features of organisms and their associations with human-modified environments.

Predicting and setting conservation priorities for Bolivian mammals based on biological correlates of the risk of decline.

The recognition that growing proportions of species worldwide are endangered has led to the development of comparative analyses to elucidate why some species are more prone to extinction than others. Understanding factors and patterns of species vulnerability might provide an opportunity to develop proactive conservation strategies. Such comparative analyses are of special concern at national scales because this is the scale at which most conservation initiatives take place. We applied powerful ensemble learning models to test for biological correlates of the risk of decline among the Bolivian mammals to understand species vulnerability at a national scale and to predict the population trend for poorly known species. Risk of decline was nonrandomly distributed: higher proportions of large-sized taxa were under decline, whereas small-sized taxa were less vulnerable. Body mass, mode of life (i.e., aquatic, terrestrial, volant), geographic range size, litter size, home range, niche specialization, and reproductive potential were strongly associated with species vulnerability. Moreover, we found interacting and nonlinear effects of key traits on the risk of decline of mammals at a national scale. Our model predicted 35 data-deficient species in decline on the basis of their biological vulnerability, which should receive more attention in order to prevent their decline. Our results highlight the relevance of comparative analysis at relatively narrow geographical scales, reveal previously unknown factors related to species vulnerability, and offer species-by-species outcomes that can be used to identify targets for conservation, especially for insufficiently known species.