Disentangling the effects of heterogeneity, stochastic dynamics and sampling in a community of aquatic insects

A dynamic and heterogeneous species abundance model generating the lognormal species abundance distribution is fitted to time series of species data from an assemblage of stoneflies and mayflies (Plecoptera and Ephemeroptera) of an aquatic insect community collected over a period of 15 years. In each year except one, we analyze 5 parallel samples taken at the same time of the season giving information about the over-dispersion in the sampling relative to the Poisson distribution. Results are derived from a correlation analysis, where the correlation in the bivariate normal distribution of log abundance is used as measurement of similarity between communities. The analysis enables decomposition of the variance of the lognormal species abundance distribution into three components due to heterogeneity among species, stochastic dynamics driven by environmental noise, and over-dispersion in sampling, accounting for 62.9, 30.6 and 6.5% of the total variance, respectively. Corrected for sampling the heterogeneity and stochastic components accordingly account for 67.3 and 32.7% of the among species variance in log abundance. By using this method, it is possible to disentangle the effect of heterogeneity and stochastic dynamics by quantifying these components and correctly remove sampling effects on the observed species abundance distribution.     

Lifting a veil on diversity: a Bayesian approach to fitting relative-abundance models.

Bayesian methods incorporate prior knowledge into a statistical analysis. This prior knowledge is usually restricted to assumptions regarding the form of probability distributions of the parameters of interest, leaving their values to be determined mainly through the data. Here we show how a Bayesian approach can be applied to the problem of drawing inference regarding species abundance distributions and comparing diversity indices between sites. The classic log series and the lognormal models of relative- abundance distribution are apparently quite different in form. The first is a sampling distribution while the other is a model of abundance of the underlying population. Bayesian methods help unite these two models in a common framework. Markov chain Monte Carlo simulation can be used to fit both distributions as small hierarchical models with shared common assumptions. Sampling error can be assumed to follow a Poisson distribution. Species not found in a sample, but suspected to be present in the region or community of interest, can be given zero abundance. This not only simplifies the process of model fitting, but also provides a convenient way of calculating confidence intervals for diversity indices. The method is especially useful when a comparison of species diversity between sites with different sample sizes is the key motivation behind the research. We illustrate the potential of the approach using data on fruit-feeding butterflies in southern Mexico. We conclude that, once all assumptions have been made transparent, a single data set may provide support for the belief that diversity is negatively affected by anthropogenic forest disturbance. Bayesian methods help to apply theory regarding the distribution of abundance in ecological communities to applied conservation.     

Empirical evaluation of neutral theory

We describe a general framework for testing neutral theory. We summarize similarities and differences between ten different versions of neutral theory. Two central predictions of neutral theory are that species abundance distributions will follow a zero-sum multinomial distribution and that community composition will change over space due to dispersal limitation. We review all published empirical tests of neutral theory. With the exception of one type of test, all tests fail to support neutral theory. We identify and perform several new tests. Specifically, we develop a set of best practices for testing the fit of the zero-sum multinomial (ZSM) vs. a lognormal null hypothesis and apply this to a data set, concluding that the lognormal outperforms neutral theory on robust tests. We explore whether a priori parameterization of neutral theory is possible, and we conclude that it is not. We show that non-curve-fitting predictions readily derived from neutral theory are easily falsifiable. In toto, there is a current overwhelming weight of evidence against neutral theory. We suggest some next steps for neutral theory.     

Zum Stichprobenfehler im Human-Biomonitoring

Goal and Scope

Human biomonitoring determines the concentration of xenobiotics in populations by means of smaller samples, thus necessarily arising sampling errors. These are determined.

Methods

For a fictitious population of 200,000 persons, differently broad xenobiotic concentration distributions were simulated. Samples of varying size were randomly drawn and the sampling error, defined as the proportional difference between the geometric means of sample and population, was determined.

Results and Conclusions

The sampling error depends on the sample size and the width of the concentration distribution; its estimation is possible for any xenobiotic, given it has lognormal distribution, and the sample size is between 10 and 50,000. For its estimation an equation was derived.

Outlook

When presenting and interpreting results of human biomonitoring, the sampling error must be considered, together with the uncertainty of the measurement.     

Null model analysis of species associations using abundance data

The influence of negative species interactions has dominated much of the literature on community assembly rules. Patterns of negative covariation among species are typically documented through null model analyses of binary presence/absence matrices in which rows designate species, columns designate sites, and the matrix entries indicate the presence (1) or absence (0) of a particular species in a particular site. However, the outcome of species interactions ultimately depends on population-level processes. Therefore, patterns of species segregation and aggregation might be more clearly expressed in abundance matrices, in which the matrix entries indicate the abundance or density of a species in a particular site. We conducted a series of benchmark tests to evaluate the performance of 14 candidate null model algorithms and six covariation metrics that can be used with abundance matrices. We first created a series of random test matrices by sampling a metacommunity from a lognormal species abundance distribution. We also created a series of structured matrices by altering the random matrices to incorporate patterns of pairwise species segregation and aggregation. We next screened each algorithm-index combination with the random and structured matrices to determine which tests had low Type I error rates and good power for detecting segregated and aggregated species distributions. In our benchmark tests, the best-performing null model does not constrain species richness, but assigns individuals to matrix cells proportional to the observed row and column marginal distributions until, for each row and column, total abundances are reached. Using this null model algorithm with a set of four covariance metrics, we tested for patterns of species segregation and aggregation in a collection of 149 empirical abundance matrices and 36 interaction matrices collated from published papers and posted data sets. More than 80% of the matrices were significantly segregated, which reinforces a previous meta-analysis of presence/absence matrices. However, using two of the metrics we detected a significant pattern of aggregation for plants and for the interaction matrices (which include plant-pollinator data sets). These results suggest that abundance matrices, analyzed with an appropriate null model, may be a powerful tool for quantifying patterns of species segregation and aggregation.     

Use of Community‐Composition Data to Predict the Fecundity and Abundance of Species

Abstract: Species distribution models are critical tools for the prediction of invasive species spread and conservation of biodiversity. The majority of species distribution models have been built with environmental data. Community ecology theory suggests that species co‐occurrence data could also be used to predict current and potential distributions of species. Species assemblages are the products of biotic and environmental constraints on the distribution of individual species and as a result may contain valuable information for niche modeling. We compared the predictive ability of distribution models of annual grassland plants derived from either environmental or community‐composition data. Composition‐based models were built with the presence or absence of species at a site as predictors of site quality, whereas environment‐based models were built with soil chemistry, moisture content, above‐ground biomass, and solar radiation as predictors. The reproductive output of experimentally seeded individuals of 4 species and the abundance of 100 species were used to evaluate the resulting models. Community‐composition data were the best predictors of both the site‐specific reproductive output of sown individuals and the site‐specific abundance of existing populations. Successful community‐based models were robust to omission of data on the occurrence of rare species, which suggests that even very basic survey data on the occurrence of common species may be adequate for generating such models. Our results highlight the need for increased public availability of ecological survey data to facilitate community‐based modeling at scales relevant to conservation.     

Characterizing species abundance distributions across taxa and ecosystems using a simple maximum entropy model

The species abundance distribution (SAD) is one of themost studied patterns in ecology due to its potential insights into commonness and rarity, community assembly, and patterns of biodiversity. It is well established that communities are composed of a few common and many rare species, and numerous theoretical models have been proposed to explain this pattern. However, no attempt has been made to determine how well these theoretical characterizations capture observed taxonomic and global-scale spatial variation in the general form of the distribution. Here, using data of a scope unprecedented in community ecology, we show that a simple maximum entropy model produces a truncated log-series distribution that can predict between 83% and 93% of the observed variation in the rank abundance of species across 15 848 globally distributed communities including birds, mammals, plants, and butterflies. This model requires knowledge of only the species richness and total abundance of the community to predict the full abundance distribution, which suggests that these factors are sufficient to understand the distribution for most purposes. Since geographic patterns in richness and abundance can often be successfully modeled, this approach should allow the distribution of commonness and rarity to be characterized, even in locations where empirical data are unavailable.     

Predicting species distributions from samples collected along roadsides

Predictive models of species distributions are typically developed with data collected along roads. Roadside sampling may provide a biased (nonrandom) sample; however, it is currently unknown whether roadside sampling limits the accuracy of predictions generated by species distribution models. We tested whether roadside sampling affects the accuracy of predictions generated by species distribution models by using a prospective sampling strategy designed specifically to address this issue. We built models from roadside data and validated model predictions at paired locations on unpaved roads and 200 m away from roads (off road), spatially and temporally independent from the data used for model building. We predicted species distributions of 15 bird species on the basis of point-count data from a landbird monitoring program in Montana and Idaho (U.S.A.). We used hierarchical occupancy models to account for imperfect detection. We expected predictions of species distributions derived from roadside-sampling data would be less accurate when validated with data from off-road sampling than when it was validated with data from roadside sampling and that model accuracy would be differentially affected by whether species were generalists, associated with edges, or associated with interior forest. Model performance measures (kappa, area under the curve of a receiver operating characteristic plot, and true skill statistic) did not differ between model predictions of roadside and off-road distributions of species. Furthermore, performance measures did not differ among edge, generalist, and interior species, despite a difference in vegetation structure along roadsides and off road and that 2 of the 15 species were more likely to occur along roadsides. If the range of environmental gradients is surveyed in roadside-sampling efforts, our results suggest that surveys along unpaved roads can be a valuable, unbiased source of information for species distribution models.     

A nearly neutral model of biodiversity

S. P. Hubbell's unified neutral theory of biodiversity has stimulated much new thinking about biodiversity. However, empirical support for the neutral theory is limited, and several observations are inconsistent with the predictions of the theory, including positive correlations between traits associated with competitive ability and species abundance and correlations between species diversity and ecosystem functioning. The neutral theory can be extended to explain these observations by allowing species to differ slightly in their competitive ability (fitness). Here, we show that even slight differences in fecundity can greatly reduce the time to extinction of competitors even when the community size is large and dispersal is spatially limited. In this case, species richness is dramatically reduced, and a markedly different species abundance distribution is predicted than under pure neutrality. In the nearly neutral model, species co-occur in the same community not because of, but in spite of, ecological differences. The more competitive species with higher fecundity tend to have higher abundance both in the metacommunity and in local communities. The nearly neutral perspective provides a theoretical framework that unites the sampling model of the neutral theory with theory of biodiversity affecting ecosystem function.     

Size-mediated non-trophic interactions and stochastic predation drive assembly and dynamics in a seabird community

Theoretical and empirical evidence suggests that body size is a major life-history trait impacting on the structure and functioning of complex food webs. However, long-term analyses of size-dependent interactions within simpler network modules, for instance, competitive guilds, are scant. Here, we model the assembly dynamics of the largest breeding seabird community in the Mediterranean basin during the last 30 years. This unique data set allowed us to test, through a "natural experiment," whether body size drove the assembly and dynamics of an ecological guild growing from very low numbers after habitat protection. Although environmental stochasticity accounted for most of community variability, the population variance explained by interspecific interactions, albeit small, decreased sharply with increasing body size. Since we found a demographic gradient along a body size continuum, in which population density and stability increase with increasing body size, the numerical effects of interspecific interactions were proportionally higher on smaller species than on larger ones. Moreover, we found that the per capita interaction coefficients were larger the higher the size ratio among competing species, but only for the set of interactions in which the species exerting the effect was greater. This provides empirical evidence for long-term asymmetric interspecific competition, which ultimately prompted the local extinction of two small species during the study period. During the assembly process stochastic predation by generalist carnivores further triggered community reorganizations and global decays in population synchrony, which disrupted the pattern of interspecific interactions. These results suggest that the major patterns detected in complex food webs can hold as well for simpler sub-modules of these networks involving non-trophic interactions, and highlight the shifting ecological processes impacting on assembling vs. asymptotic communities.     

Estimating species occurrence, abundance, and detection probability using zero-inflated distributions

Researchers have developed methods to account for imperfect detection of species with either occupancy (presence absence) or count data using replicated sampling. We show how these approaches can be combined to simultaneously estimate occurrence, abundance, and detection probability by specifying a zero-inflated distribution for abundance. This approach may be particularly appropriate when patterns of occurrence and abundance arise from distinct processes operating at differing spatial or temporal scales. We apply the model to two data sets: (1) previously published data for a species of duck, Anas platyrhynchos, and (2) data for a stream fish species, Etheostoma scotti. We show that in these cases, an incomplete-detection zero-inflated modeling approach yields a superior fit to the data than other models. We propose that zero-inflated abundance models accounting for incomplete detection be considered when replicate count data are available.     

Rapid,recurring, structured survey versus bioblitz for generating biodiversity data and analysis with a multispecies abundance model

A bioblitz inexpensively and quickly generates biodiversity data, but bioblitzes are often conducted with haphazard, unreplicated sampling. Results tend to be taxonomically, geographically, or temporally biased, lack metadata, and consist of lists of observed taxa that do not enable further analyses or correction for imperfect detection. A rapid, recurring, structured survey (RRSS) uses a structured sampling design and temporal and spatial replication to survey randomly selected sites on a conservation property. We participated in a loosely structured bioblitz and a subsequent RRSS at Big Canoe Creek Nature Preserve in Springville (St. Clair County), Alabama (USA) to compare observed richness derived from the 2 survey approaches. The RRSS data structure enabled us to fit models that accounted for imperfect detection to estimate abundances, occupancy probabilities, and habitat associations. The loosely structured bioblitz data could not be used in such models. We present a new integrated multispecies abundance model that we applied to avian RRSS data. Our model extension enables estimation for the community, employs data augmentation to estimate the number of undetected species, and incorporates covariates. The RRSS generated a more comprehensive and less biased list of observed taxonomic richness than the loosely structured bioblitz (e.g., 73 vs. 45 bird species and 104 vs. 63 insect families from the RRSS vs. loosely structured bioblitz, respectively). Models fit to the RRSS data identified seasonal patterns in avian community composition and allowed for estimation of habitat–occupancy relationships for insect taxa. The RRSS protocol has potential for broad transferability as a standardized, quick, and inexpensive way to inventory biodiversity and estimate ecological parameters while providing an outreach opportunity.     

Partitioning the factors of spatial variation in regeneration density of shade-tolerant tree species

Understanding coexistence of highly shade-tolerant tree species is a longstanding challenge for forest ecologists. A conceptual model for the coexistence of sugar maple (Acer saccharum) and American beech (Fagus grandibfolia) has been proposed, based on a low-light survival/high-light growth trade-off, which interacts with soil fertility and small-scale spatiotemporal variation in the environment. In this study, we first tested whether the spatial distribution of seedlings and saplings can be predicted by the spatiotemporal variability of light availability and soil fertility, and second, the manner in which the process of environmental filtering changes with regeneration size. We evaluate the support for this hypothesis relative to the one for a neutral model, i.e., for seed rain density predicted from the distribution of adult trees. To do so, we performed intensive sampling over 86 quadrats (5 x 5 m) in a 0.24-ha plot in a mature maple-beech community in Quebec, Canada. Maple and beech abundance, soil characteristics, light availability, and growth history (used as a proxy for spatiotemporal variation in light availability) were finely measured to model variation in sapling composition across different size classes. Results indicate that the variables selected to model species distribution do effectively change with size, but not as predicted by the conceptual model. Our results show that variability in the environment is not sufficient to differentiate these species' distributions in space. Although species differ in their spatial distribution in the small size classes, they tend to correlate at the larger size class in which recruitment occurs. Overall, the results are not supportive of a model of coexistence based on small-scale variations in the environment. We propose that, at the scale of a local stand, the lack of fit of the model could result from the high similarity of species in the range of environmental conditions encountered, and we suggest that coexistence would be stable only at larger spatial scales at which variability in the environment is greater.     

Spatial structure, sampling design and abundance estimates in sandy beach macroinfauna: some warnings and new perspectives

We discuss methodological aspects directed to quantify the across-shore population structure and abundance of sandy beach macroinfauna. The reliability of estimates derived from design-based (stratified random sampling) and model-based (geostatistics, kriging) approaches is discussed. Our analysis also addresses potential biases arising from environmentally driven designs that consider a priori fixed strata for sampling macroinfauna, as opposed to species-driven sampling designs, in which the entire range of across-shore distribution is covered. Model-based approaches showed, spatially, highly autocorrelated and persistent structures in two intertidal populations of the Uruguayan coast: the isopod Excirolana armata and the yellow clam Mesodesma mactroides. Both populations presented zonation patterns that ranged from the base of the dunes to upper levels of the subtidal. The Gaussian model consistently explained the spatial distribution of species and population components (clam recruits and adults), with a minor contribution (Е%) of unresolved, small-scale variability. The consistent structure of spatial dependence in annual data strongly suggests an across-shore-structured process covering close to 35 m. Kriging predictions through cross-validation corroborated the appropriateness of the models fitted through variographic analysis, and the derived abundance estimates were very similar (maximum difference=7%) to those obtained from linear interpolation. Monthly analysis of E. armata data showed marked variations in its zonation and an unstable spatial structure according to the Gaussian model. The clear spatial structure resulting from species-driven sampling was not observed when data was truncated to simulate an environmentally driven sampling design. In this case, the linear semivariogram indicated a spatial gradient, suggesting that sampling was not performed at the appropriate spatial scale. Further, the cross-validation procedure was not significant, and both density and total abundance were underestimated. We conclude that: (1) geostatistics provides useful additional information about population structure and aids in direct abundance estimation; thus we suggest it as a powerful tool for further applications in the study of sandy beach macroinfauna; and that (2) environmentally driven sampling strategies fail to provide conclusive results about population structure and abundance, and should be avoided in studies of sandy beach populations. This is especially true for microtidal beaches, where unpredictable swash strength precludes a priori stratification through environmental reference points. The need to use adaptive sampling designs and avoid snapshot sampling is also stressed. Methodological implications for the detection of macroecological patterns in sandy beach macroinfauna are also discussed.     

Using citizen science butterfly counts to predict species population trends

Citizen scientists are increasingly engaged in gathering biodiversity information, but trade‐offs are often required between public engagement goals and reliable data collection. We compared population estimates for 18 widespread butterfly species derived from the first 4 years (2011–2014) of a short‐duration citizen science project (Big Butterfly Count [BBC]) with those from long‐running, standardized monitoring data collected by experienced observers (U.K. Butterfly Monitoring Scheme [UKBMS]). BBC data are gathered during an annual 3‐week period, whereas UKBMS sampling takes place over 6 months each year. An initial comparison with UKBMS data restricted to the 3‐week BBC period revealed that species population changes were significantly correlated between the 2 sources. The short‐duration sampling season rendered BBC counts susceptible to bias caused by interannual phenological variation in the timing of species’ flight periods. The BBC counts were positively related to butterfly phenology and sampling effort. Annual estimates of species abundance and population trends predicted from models including BBC data and weather covariates as a proxy for phenology correlated significantly with those derived from UKBMS data. Overall, citizen science data obtained using a simple sampling protocol produced comparable estimates of butterfly species abundance to data collected through standardized monitoring methods. Although caution is urged in extrapolating from this U.K. study of a small number of common, conspicuous insects, we found that mass‐participation citizen science can simultaneously contribute to public engagement and biodiversity monitoring. Mass‐participation citizen science is not an adequate replacement for standardized biodiversity monitoring but may extend and complement it (e.g., through sampling different land‐use types), as well as serving to reconnect an increasingly urban human population with nature.     

Will Observation Error and Biases Ruin the Use of Simple Extinction Models?

Abstract: Estimating the risk of extinction for populations of endangered species is an important component of conservation biology. These estimates must be made from data that contain both environmental noise in the year-to-year transitions in population size (so-called "process error"), random errors in sampling, and possible biases in sampling ( both forms of observation errors). To determine how much faith to place in estimated extinction rates, it is important to know how sensitive they are to observation error. We used three simple, commonly employed models of population dynamics to generate simulated population time series. We then combined random observation error or systematic biases with those data, fit models to the time series data, and observed how close the extinction dynamics of the fitted models compared with the dynamics of the underlying models. We found that systematic biases in sampling rarely affected estimates of extinction risk. We also found that even moderate levels of random observation error do not significantly affect extinction estimates except over a small range of process errors, corresponding to the region where extinction risk is most uncertain. With more substantial sampling error, estimates of extinction risk degraded rapidly. Field census techniques for a variety of taxa often involve observation errors within ±32% of actual population sizes. For typical time series used in conservation, therefore, we often may not need to be overly concerned about observation errors as an extra source of imperfection in our estimated extinction rates.     

Efficiency of composite sampling for estimating a lognormal distribution

In many environmental studies measuring the amount of a contaminant in a sampling unit is expensive. In such cases, composite sampling is often used to reduce data collection cost. However, composite sampling is known to be beneficial for estimating the mean of a population, but not necessarily for estimating the variance or other parameters. As some applications, for example, Monte Carlo risk assessment, require an estimate of the entire distribution, and as the lognormal model is commonly used in environmental risk assessment, in this paper we investigate efficiency of composite sampling for estimating a lognormal distribution. In particular, we examine the magnitude of savings in the number of measurements over simple random sampling, and the nature of its dependence on composite size and the parameters of the distribution utilizing simulation and asymptotic calculations.     

A test of reproductive power in snakes

Reproductive power is a contentious concept among ecologists, and the model has been criticized on theoretical and empirical grounds. Despite these criticisms, the model has successfully predicted the modal (optimal) size in three large taxonomic groups and the shape of the body size distribution in two of these groups. We tested the reproductive power model on snakes, a group that differs markedly in physiology, foraging ecology, and body shape from the endothermic groups upon which the model was derived. Using detailed field data from the published literature, snake-specific constants associated with reproductive power were determined using allometric relationships of energy invested annually in egg production and population productivity. The resultant model accurately predicted the mode and left side of the size distribution for snakes but failed to predict the right side of that distribution. If the model correctly describes what is possible in snakes, observed size diversity is limited, especially in the largest size classes.     

Using habitat suitability index and particle dispersion models for early detection of marine invaders.

Eradication and control of invasive species are often possible only if populations are detected when they are small and localized. To be efficient, detection surveys should be targeted at locations where there is the greatest risk of incursions. We examine the utility of habitat suitability index (HSI) and particle dispersion models for targeting sampling for marine pests. Habitat suitability index models are a simple way to identify suitable habitat when species distribution data are lacking. We compared the performance of HSI models with statistical models derived from independent data from New Zealand on the distribution of two nonindigenous bivalves: Theora lubrica and Musculista senhousia. Logistic regression models developed using the HSI scores as predictors of the presence/absence of Theora and Musculista explained 26.7% and 6.2% of the deviance in the data, respectively. Odds ratios for the HSI scores were greater than unity, indicating that they were genuine predictors of the presence/ absence of each species. The fit and predictive accuracy of each logistic model were improved when simulated patterns of dispersion from the nearest port were added as a predictor variable. Nevertheless, the combined model explained, at best, 46.5% of the deviance in the distribution of Theora and correctly predicted 56% of true presences and 50% of all cases. Omission errors were between 6% and 16%. Although statistical distribution models built directly from environmental predictors always outperformed the equivalent HSI models, the gain in model fit and accuracy was modest. High residual deviance in both types of model suggests that the distributions realized by Theora and Musculista in the field data were influenced by factors not explicitly modeled as explanatory variables and by error in the environmental data used to project suitable habitat for the species. Our results highlight the difficulty of accurately predicting the distribution of invasive marine species that exhibit low habitat occupancy and patchy distributions in time and space. Although the HSI and statistical models had utility as predictors of the likely distribution of nonindigenous marine species, the level of spatial accuracy achieved with them may be well below expectations for sensitive surveillance programs.     

Do plant–pollinator interaction networks result from stochastic processes?

Plant–pollinator interaction networks are characterized by several features that cannot be obtained from a totally random network (e.g. nestedness, power law distribution of degree specialization, temporal turnover). One reason is that both plants and pollinators are active for only a part of the year, and so a plant species flowering in spring cannot interact with a pollinator species that is active only in autumn. In this paper we build a stochastic model to simulate the plant–pollinator interaction network, taking into account the duration of activity of each species. To build the model we used an empirical plant–pollinator network from a Mediterranean scrub community surveyed over four years. In our simulated annual cycle we know which plant and pollinator species are active, and thus available to interact. We can obtain simulated plant–pollinator interaction networks with properties similar to the real ones in two different ways: (i) by assuming that the frequency distribution of both plant and pollinator duration of activity follow an exponential function, and that interaction among temporally coexisting species are totally random, and (ii) by assuming more realistic frequency distributions (exponential for pollinators, lognormal for plants) and that the interaction among coexisting species is occurring on a per capita basis. In the latter case we assume that there is a positive relationship between abundance and duration of activity. In our model the starting date of the species activity had little influence on the network structure. We conclude that the observed plant–pollinator network properties can be produced stochastically, and the mechanism shaping the network is not necessarily related to size constraints. Under such conditions co-evolutionary explanations should be given with caution.