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  1. Abstract

    Interspecific competition, environmental filtering, or spatial variation in productivity can contribute to positive or negative spatial covariance in the abundances of species across ensembles (i.e., groups of interacting species defined by geography, resource use, and taxonomy). In contrast, density compensation should give rise to a negative relationship between ecomorphological similarity and abundance of species within ensembles. We evaluated (1) whether positive or negative covariances characterized the pairwise relationships of 21 species of Congolese shrew, and (2) whether density compensation characterized the structure of each of 36 Congolese shrew ensembles, and did so based on the abundances or biomasses of species. In general, positive covariance is more common than negative covariance based on considerations of abundance or biomass, suggesting dominant roles for environmental filtering and productivity. Nonetheless, negative covariance is more common for ecomorphologically similar species, suggesting a dominant role for competition within functional groups. Effects of abundance or biomass compensation, via pairwise or diffuse competitive interactions, were detected less often than expected by chance, suggesting that interspecific competition is not the dominant mechanism structuring these ensembles. Effects of competition may be balanced by responses to variation in resource abundance among sites in a landscape or among niche spaces within sites. Future studies of compensatory effects should incorporate considerations of heterogeneity in the abundance and distribution of resources in ecological space to better isolate the effects of competition and resource abundance, which can have opposing effects on community structure.

     
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  2. 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.

     
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  3. 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.

     
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