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The Anthropocene is characterized by complex, primarily human‐generated, disturbance regimes that include combinations of long‐term press (e.g. climate change, pollution) and episodic pulse (e.g. cyclonic storms, floods, wildfires, land use change) disturbances. Within any regime, disturbances occur at multiple spatial and temporal scales, creating complex and varied interactions that influence spatiotemporal dynamics in the abundance, distribution and biodiversity of organisms. Moreover, responses to disturbance are context dependent, with the legacies of previous disturbances affecting responses to ensuing perturbations. We use three decades of annual data to evaluate the effects of repeated pulse disturbances and global warming on gastropod populations and communities in Puerto Rico at multiple spatial scales. More specifically, we quantify 1) the relative importance of large‐scale and small‐scale aspects of disturbance on variation in abundance, biodiversity and species composition; and 2) the spatial scales at which populations and communities integrate information in the spatially heterogenous environments created by disturbances. Gastropods do not exhibit consistent decreases in abundance or biodiversity in association with global warming: abundance for many species has increased over time and species richness does not evince a temporal trend. Nonetheless, gastropods are sensitive to hurricane severity, spatial environmental variation and successional trajectories of the flora. In addition, they exhibit context dependent (i.e. legacy effects) responses that are scale dependent. The Puerto Rican biota has evolved in a disturbance‐mediated system. This historical exposure to repeated, severe hurricane‐induced disturbances has imbued the biota with high resistance and resilience to the current disturbance regime, resulting in an ability to persist or thrive under current environmental conditions. Nonetheless, these ecosystems may yet be threatened by worsening direct and indirect effects of climate change. In particular, more frequent and severe hurricanes may prevent the establishment of closed canopy forests, negatively impacting populations and communities that rely on these habitats. 

Evidence suggests that bats are important hosts of filoviruses, yet the specific species involved remain largely unidentified. Niemann-Pick C1 (NPC1) is an essential entry receptor, with amino acid variations influencing viral susceptibility and species-specific tropism. Herein, we conducted combinatorial binding studies with seven filovirus glycoproteins (GPs) and NPC1 orthologs from 81 bat species. We found that GP-NPC1 binding correlated poorly with phylogeny. By integrating binding assays with machine learning, we identified genetic factors influencing virus-receptor-binding and predicted GP-NPC1-binding avidity for additional filoviruses and bats. Moreover, combining receptor-binding avidities with bat geographic distribution and the locations of previous Ebola outbreaks allowed us to rank bats by their potential as Ebola virus hosts. This study represents a comprehensive investigation of filovirus-receptor binding in bats (1,484 GP-NPC1 pairs, 11 filoviruses, and 135 bats) and describes a multidisciplinary approach to predict susceptible species and guide filovirus host surveillance. 

Abstract Quantification of phenological patterns (e.g. migration, hibernation or reproduction) should involve statistical assessments of non‐uniform temporal patterns. Circular statistics (e.g. Rayleigh test or Hermans‐Rasson test) provide useful approaches for doing so based on the number of individuals that exhibit particular activities during a number of time intervals.This study used monthly reproductive activity as an example to illustrate problems in applying circular statistics to data when marginal totals characterize experimental designs (e.g. the number of reproductively active individuals per time interval depends on sampling effort or sampling success). We illustrate the nature of this problem by crafting four exemplar data sets and developing a bootstrapping simulation procedure to overcome complications that arise from the existence of marginal totals. In addition, we apply circular statistics and our bootstrapping simulation to empirical data on the reproductive phenology of six species of Neotropical bats from the Amazon.Because sampling effort or success can differ among time intervals, circular statistics can produce misleading results of two types: those suggesting uniform phenologies when empirical patterns are markedly modal, and those suggesting non‐uniform phenologies when empirical patterns are uniform. The bootstrapping simulation overcomes these limitations: the exemplar phenology in which the percentage of reproductively active individuals is modal is appropriately identified as non‐uniform based on the bootstrapping approach, and the exemplar phenology in which the percentage of reproductively active individuals is invariant is appropriately identified as uniform based on the bootstrapping approach. The reproductive phenology of each of the six empirical examples is non‐uniform based on the bootstrapping approach, and this is true for bats species with unimodal peaks or bimodal peaks.In addition to problems with marginal totals, a review of analyses of phenological patterns in ecology identified two other frequent issues in the application of circular statistics: sampling bias and pseudoreplication. Each of these issues and potential solutions are also discussed. By providing source code for the execution of the Rayleigh test and Hermans‐Rasson test, along with the code for the bootstrapping simulation, we offer a useful tool for assessing non‐random phenologies when marginal totals characterize experimental designs.