In this study we document the diet, determine diet selection, and evaluate the seed-dispersal role of lowland tapirs (Tapirus terrestris L.) in the Tabaro River valley of southern Venezuela. The diet was assessed by checking treefall gaps and closed-canopy areas of equal size for browsing signs, examining droppings for seeds and fruit remains, and casually asking experienced Ye'kwana Indian hunters. Plants browsed by tapirs were identified and counted. The abundance of each plant species at the study site was determined using 25-m2 quadrats and compared with its abundance in the diet to determine selectivity. Because tapirs defecate in water, their role as seed dispersers was examined by analyzing the distribution of diet species using a data base of the locations of trees at the study site. Information from the 25-m2 quadrats was used for lianas and shrubs. Results show that tapirs selectively browse on 88 out of at least 256 plant species, consistently avoiding more species in closed-canopy areas. Some species occur significantly more frequently in the diet than their relative abundance in the forest. Tapirs eat fruits of 33 species; 2 of these are mainly found near the water and 9 away from the water.

Systematic conservation planning aims to design networks of protected areas that meet conservation goals across large landscapes. The optimal design of these conservation networks is most frequently based on the modeled habitat suitability or probability of occurrence of species, despite evidence that model predictions may not be highly correlated with species density. We hypothesized that conservation networks designed using species density distributions more efficiently conserve populations of all species considered than networks designed using probability of occurrence models. To test this hypothesis, we used the Zonation conservation prioritization algorithm to evaluate conservation network designs based on probability of occurrence versus density models for 26 land bird species in the U.S. Pacific Northwest. We assessed the efficacy of each conservation network based on predicted species densities and predicted species diversity. High-density model Zonation rankings protected more individuals per species when networks protected the highest priority 10-40% of the landscape. Compared with density-based models, the occurrence-based models protected more individuals in the lowest 50% priority areas of the landscape. The 2 approaches conserved species diversity in similar ways: predicted diversity was higher in higher priority locations in both conservation networks. We conclude that both density and probability of occurrence models can be useful for setting conservation priorities but that density-based models are best suited for identifying the highest priority areas. Developing methods to aggregate species count data from unrelated monitoring efforts and making these data widely available through ecoinformatics portals such as the Avian Knowledge Network will enable species count data to be more widely incorporated into systematic conservation planning efforts.

Abstract. The large uncertainty surrounding the future effects of sea-level rise and other aspects of climate change on tidal marsh ecosystems exacerbates the difficulty in planning effective conservation and restoration actions. We addressed these difficulties in the context of large-scale wetland restoration activities underway in the San Francisco Estuary (Suisun, San Pablo and San Francisco Bays). We used a boosted regression tree approach to project the future distribution and abundance of five marsh bird species (through 2110) in response to changes in habitat availability and suitability as a result of projected sea-level rise, salinity, and sediment availability in the Estuary. To bracket the uncertainty, we considered four future scenarios based on two sediment availability scenarios (high or low), which varied regionally, and two rates of sea-level rise (0.52 or 1.65 m/100 yr). We evaluated three approaches for using model results to inform the selection of potential restoration projects: (1) Use current conditions only to prioritize restoration. (2) Use a single future scenario (among the four referred to above) in combination with current conditions to select priority restoration projects. (3) Combine current conditions with all four future scenarios, while incorporating uncertainty among future scenarios into the selection of restoration projects. We found that simply using current conditions resulted in the poorest performing restoration projects selected in terms of providing habitat for tidal marsh birds in light of possible future scenarios. The most robust method for selecting restoration projects, the ''combined'' strategy, used projections from all future scenarios with a discounting of areas with high levels of variability among future scenarios. We show that uncertainty about future conditions can be incorporated in site prioritization algorithms and should motivate the selection of adaptation measures that are robust to uncertain future conditions. These results and data have been made available via an interactive decision support tool at www.prbo.org/sfbayslr.

Sustainable hunting, the extraction of game without reducing its density, is a desirable approach to the use of wildlife. Assessment of sustainable extraction in many parts of the world is difficult; it has recently been done by a method proposed by Robinson and Redford (1991): a maximum number of animals that can be extracted per unit area is calculated based on life-history parameters and density estimates. If extraction is higher than that maximum number, it is deemed unsustainable. We extended the method by adding spatial and stochastic components through an individual-based model of a population of female tapirs ( Tapirus sp.) and conducted a sensitivity analysis to evaluate the importance of spatial and life-history parameters. Our analysis suggests that spatial factors, such as the shape of the hunted area and the size of the surrounding population, may be important in determining the sustainability of extraction. For long-lived, slow-reproducing mammals such as tapirs, survival to age of last reproduction is the most critical parameter, but the shape of the hunting zone and population density can be critical, especially in unsustainable hunting scenarios. We advocate long-term studies of tapirs to collect information on spatial movements and survival rates that could then be used for development of proper management plans. Factores Espaciales y Estocasticidad en la Evaluación de la Cacería Sustentable de TapiresResumen: La cacería sustentable, extracción de animales de caza sin reducción de su densidad, es un enfoque deseable para el uso de vida silvestre. La evaluación de la extracción sustentable es difícil en muchas partes del mundo y se ha hecho utilizando un método propuesto por Robinson y Redford (1991): el número máximo de animales que se puede extraer por unidad de área se calcula con base en parámetros de la historia de vida y estimaciones de la densidad. Si la extracción es mayor que ese número máximo, se considera no sustentable. Extendimos el método agregando componentes espaciales y estocásticos por medio del modelo basado en individuos de una población de tapires ( Tapirus sp.) hembras y realizamos un análisis de sensibilidad para evaluar la importancia de los parámetros espaciales y de la historia de vida. Nuestro análisis sugiere que factores espaciales, tal como la forma del área de cacería y el tamaño de la población circundante, pueden ser importantes en la determinación de la sustentabilidad de la extracción. Para especies longevas de reproducción lenta como el tapir, la supervivencia hasta la última edad reproductiva es el parámetro más crítico; pero la forma del área de cacería y la densidad pueden ser críticas, especialmente en poblaciones bajo caza no sustentable. Recomendamos estudios de largo plazo de tapires para obtener información de movimientos espaciales y tasas de supervivencia que pueda utilizarse para el desarrollo de planes de manejo adecuados.

The Global Ecosystem Dynamics Investigation (GEDI) lidar began data acquisition from the International Space Station in March 2019 and is expected to make over 10 billion measurements of canopy structure and topography over two years. Previously, airborne lidar data with limited spatial coverage have been used to examine relationships between forest canopy structure and faunal diversity, most commonly bird species. GEDI’s latitudinal coverage will permit these types of analyses at larger spatial extents, over the majority of the Earth’s forests, and most importantly in areas where canopy structure is complex and/or poorly understood. In this regional study, we examined the impact that GEDI-derived Canopy Structure variables have on the performance of bird species distribution models (SDMs) in Sonoma County, California. We simulated GEDI waveforms for a two-year period and then interpolated derived Canopy Structure variables to three grid sizes of analysis. In addition to these variables, we also included Phenology, Climate, and other Auxiliary variables to predict the probability of occurrence of 25 common bird species. We used a weighted average ensemble of seven individual machine learning models to make predictions for each species and calculated variable importance. We found that Canopy Structure variables were, on average at our finest resolution of 250 m, the second most important group (32.5%) of predictor variables after Climate variables (35.3%). Canopy Structure variables were most important for predicting probability of occurrence of birds associated with Conifer forest habitat. Regarding spatial analysis scale, we found that finer-scale models more frequently performed better than coarser-scale models, and the importance of Canopy Structure variables was greater at finer spatial resolutions. Overall, GEDI Canopy Structure variables improved SDM performance for at least one spatial resolution for 19 of 25 species and thus show promise for improving models of bird species occurrence and mapping potential habitat.