Natural population variability may be masking the more-individuals hypothesis

Species richness and productivity are correlated at global and regional scales, but the mechanisms linking them are inconclusive. The most commonly invoked mechanism, the more-individuals hypothesis (MIH), hypothesizes that increased productivity leads to increased food resource availability, which leads to an increased number of individuals supporting more species. Empirical evidence for the MIH remains mixed despite a substantial literature. Here we used simulations to determine whether interannual population variability could be masking a "true" MIH relationship. In each simulation, fixed linear relationships between productivity, richness, and 50-yr average abundance mimicked the MIH mechanism. Abundance was allowed to vary annually and sampled for 1-40 yr. Linear regressions of richness on sampled abundance assessed the probability of detecting the fixed MIH relationship. Medium to high population variability with short-term sampling (1-3 yr) led to poor detection of the fixed MIH relationship. Notably, this level of sampling and population variability describes nearly all MIH studies to date. Long-term sampling (5+ yr) led to improved detection of the fixed relationship; thus it is necessary to detect support for the MIH reliably. Such sampling duration is nonexistent in the MIH literature. Robust future studies of the MIH necessitate consideration of interannual population variability.

Quantifying the ecological success of a community-based wildlife conservation area in Tanzania

In Tanzania, community-based natural resource management of wildlife occurs through the creation of Wildlife Management Areas (WMAs). WMAs consist of multiple villages designating land for wildlife conservation, and sharing a portion of subsequent tourism revenues. Nineteen WMAs are currently operating, encompassing 7% of Tanzania’s land area, with 19 more WMAs planned. The ecological success or failure of WMAs for wildlife conservation has yet to be quantified. We defined ecological success in this case as significantly greater densities of wildlife and significantly lower densities of livestock in the WMA relative to the control site, after the WMA was established. We used 4 years of distance sampling surveys conducted 6 times per year for wild and domestic ungulates to quantify wildlife and livestock densities before and after the establishment and implementation of management efforts at Randilen WMA, relative to a control site on adjacent land of similar vegetation and habitat types. We documented similarity between the sites before WMA establishment, when both sites were managed by the same authority. After WMA establishment, we documented significantly higher densities of resident wildlife (giraffes and dik-diks) and lower densities of cattle in the WMA, relative to the control site, indicating short-term ecological success. Continued monitoring is necessary to determine longer-term effects, and to evaluate management decisions.

Accounting for sampling error when inferring population synchrony from time-series data: a Bayesian state-space modelling approach with applications

Background

Data collected to inform time variations in natural population size are tainted by sampling error. Ignoring sampling error in population dynamics models induces bias in parameter estimators, e.g., density-dependence. In particular, when sampling errors are independent among populations, the classical estimator of the synchrony strength (zero-lag correlation) is biased downward. However, this bias is rarely taken into account in synchrony studies although it may lead to overemphasizing the role of intrinsic factors (e.g., dispersal) with respect to extrinsic factors (the Moran effect) in generating population synchrony as well as to underestimating the extinction risk of a metapopulation.

Methodology/Principal findings

The aim of this paper was first to illustrate the extent of the bias that can be encountered in empirical studies when sampling error is neglected. Second, we presented a space-state modelling approach that explicitly accounts for sampling error when quantifying population synchrony. Third, we exemplify our approach with datasets for which sampling variance (i) has been previously estimated, and (ii) has to be jointly estimated with population synchrony. Finally, we compared our results to those of a standard approach neglecting sampling variance. We showed that ignoring sampling variance can mask a synchrony pattern whatever its true value and that the common practice of averaging few replicates of population size estimates poorly performed at decreasing the bias of the classical estimator of the synchrony strength.

Conclusion/Significance

The state-space model used in this study provides a flexible way of accurately quantifying the strength of synchrony patterns from most population size data encountered in field studies, including over-dispersed count data. We provided a user-friendly R-program and a tutorial example to encourage further studies aiming at quantifying the strength of population synchrony to account for uncertainty in population size estimates.

Hierarchical modeling of an invasive spread: the Eurasian Collared-Dove Streptopelia decaocto in the United States

Invasive species are regularly claimed as the second threat to biodiversity. To apply a relevant response to the potential consequences associated with invasions (e.g., emphasize management efforts to prevent new colonization or to eradicate the species in places where it has already settled), it is essential to understand invasion mechanisms and dynamics. Quantifying and understanding what influences rates of spatial spread is a key research area for invasion theory. In this paper, we develop a model to account for occupancy dynamics of an invasive species. Our model extends existing models to accommodate several elements of invasive processes; we chose the framework of hierarchical modeling to assess site occupancy status during an invasion. First, we explicitly accounted for spatial structure and how distance among sites and position relative to one another affect the invasion spread. In particular, we accounted for the possibility of directional propagation and provided a way of estimating the direction of this possible spread. Second, we considered the influence of local density on site occupancy. Third, we decided to split the colonization process into two subprocesses, initial colonization and recolonization, which may be ground-breaking because these subprocesses may exhibit different relationships with environmental variations (such as density variation) or colonization history (e.g., initial colonization might facilitate further colonization events). Finally, our model incorporates imperfection in detection, which might be a source of substantial bias in estimating population parameters. We focused on the case of the Eurasian Collared-Dove (Streptopelia decaocto) and its invasion of the United States since its introduction in the early 1980s, using data from the North American BBS (Breeding Bird Survey). The Eurasian Collared-Dove is one of the most successful invasive species, at least among terrestrial vertebrates. Our model provided estimation of the spread direction consistent with empirical observations. Site persistence probability exhibits a quadratic response to density. We also succeeded at detecting differences in the relationship between density and initial colonization vs. recolonization probabilities. We provide a map of sites that may be colonized in the future as an example of possible practical application of our work.

Replicated sampling increases efficiency in monitoring biological populations

Observation or sampling error in population monitoring can cause serious degradation of the inferences, such as estimates of trend or risk, that ecologists and managers frequently seek to make with time-series observations of population abundances. We show that replicating the sampling process can considerably improve the information obtained from population monitoring. At each sampling time the sampling method would be repeated, either simultaneously or within a short time. In this study we examine the potential value of replicated sampling to population monitoring using a density-dependent population model. We modify an existing population time-series model, the Gompertz state-space model, to incorporate replicated sampling, and we develop maximum-likelihood and restricted maximum-likelihood estimates of model parameters. Depending on sampling protocols, replication may or may not entail substantial extra cost. Some sampling programs already have replicated samples, but the samples are aggregated or pooled into one estimate of population abundance; such practice of aggregating samples, according to our model, loses considerable information about model parameters. The gains from replicated sampling are realized in substantially improved statistical inferences about model parameters, especially inferences for sorting out the contributions of process noise and observation error to observed population variability.

Response of soil invertebrates to disturbance across three resource regions in North Carolina

We evaluated the potential of soil microarthropods and enchytraeid worms to be useful as bioindicators of soil condition in forest, wetland, and agricultural ecosystems over a range of ecoregions. Selected mesofauna and soil characteristics in soil and litter in relatively undisturbed and disturbed examples of each of three ecosystems within each of three land resource regions were monitored over two years. Optimal times of year to sample these organisms as indicators of disturbance were April, May, July and September. No single measure reflected disturbance across all three ecosystems. Among forest sites, Simpson's diversity index, evenness, abundance of ants, and proportion of enchytraeids in the mesofauna differed between soils of different disturbance levels. Among agricultural sites, richness, evenness, abundance of mites, and proportions of collembolans and of enchytraeids in the mesofauna differed between disturbance levels. Among wetland sites, Shannon's and Simpson's diversity indices, richness based on the total mesofauna, and abundances of mites, diplurans, ants, and isotomid and onychiurid collembolans differed between disturbance levels. Covariates most frequently associated with abundance and diversity of the measured mesofauna were soil electrical conductivity, available N, organic matter, and pH. Canonical correspondence analysis provided information somewhat different to bivariate analysis. Using both approaches to examine soil and litter taxa that have distinctive responses to disturbance may help to identify candidate groups applicable for use in large-scale environmental monitoring programs.

Hierarchical Spatiotemporal Matrix Models for Characterizing Invasions

The growth and dispersal of biotic organisms is an important subject in ecology. Ecologists are able to accurately describe survival and fecundity in plant and animal populations and have developed quantitative approaches to study the dynamics of dispersal and population size. Of particular interest are the dynamics of invasive species. Such nonindigenous animals and plants can levy significant impacts on native biotic communities. Effective models for relative abundance have been developed; however, a better understanding of the dynamics of actual population size (as opposed to relative abundance) in an invasion would be beneficial to all branches of ecology. In this article, we adopt a hierarchical Bayesian framework for modeling the invasion of such species while addressing the discrete nature of the data and uncertainty associated with the probability of detection. The nonlinear dynamics between discrete time points are intuitively modeled through an embedded deterministic population model with density-dependent growth and dispersal components. Additionally, we illustrate the importance of accommodating spatially varying dispersal rates. The method is applied to the specific case of the Eurasian Collared-Dove, an invasive species at mid-invasion in the United States at the time of this writing.

Monitoring of biodiversity indicators in boreal forests: a need for improved focus

The general principles of scale and coarse and fine filters have been widely accepted, but management agencies and industry are still grappling with the question of what to monitor to detect changes in forest biodiversity following forest management. Part of this problem can be attributed to the lack of focused questions for monitoring including absence of null models and predicted effects, a certain level of disconnect between research and management, and recognition that monitoring can be designed as a research question. Considerable research from the past decade has not been adequately synthesized to answer important questions, such as which species or forest attributes might be the best indicators of change. A disproportionate research emphasis has been placed on community ecology, and mostly on a few groups of organisms including arthropods, amphibians, migratory songbirds, and small mammals, while other species, including soil organisms, lichens, bats, raptors, some carnivores, and larger mammals remain less well-known. In most studies of community ecology, the question of what is the importance, if any, of the regularly observed subtle changes in community structures, and causes of observed changes is usually not answered. Hence, our ability to deal with questions of persistence is limited, and demographic research on regionally-defined key species (such as species linked to processes, species whose persistence may be affected, species with large home ranges, species already selected as indicators, and rare and threatened species) is urgently needed. Monitoring programs need to be designed to enable managers to respond to unexpected changes caused by forest management. To do this, management agencies need to articulate null models for monitoring that predict effects, focus fine-scale monitoring on key species (defined by local and regional research) in key habitats (rare, declining, important) across landscapes, and have a protocol in place to adapt management strategies to changes observed. Finally, agencies must have some way to determine and define when a significant change has occurred and to predict the persistence of species; this too should flow from a well-designed null model.

N ‐Mixture Models for Estimating Population Size from Spatially Replicated Counts

Spatial replication is a common theme in count surveys of animals. Such surveys often generate sparse count data from which it is difficult to estimate population size while formally accounting for detection probability. In this article, I describe a class of models (N-mixture models) which allow for estimation of population size from such data. The key idea is to view site-specific population sizes, N, as independent random variables distributed according to some mixing distribution (e.g., Poisson). Prior parameters are estimated from the marginal likelihood of the data, having integrated over the prior distribution for N. Carroll and Lombard (1985, Journal of American Statistical Association 80, 423-426) proposed a class of estimators based on mixing over a prior distribution for detection probability. Their estimator can be applied in limited settings, but is sensitive to prior parameter values that are fixed a priori. Spatial replication provides additional information regarding the parameters of the prior distribution on N that is exploited by the N-mixture models and which leads to reasonable estimates of abundance from sparse data. A simulation study demonstrates superior operating characteristics (bias, confidence interval coverage) of the N-mixture estimator compared to the Caroll and Lombard estimator. Both estimators are applied to point count data on six species of birds illustrating the sensitivity to choice of prior on p and substantially different estimates of abundance as a consequence.