Assessing the impacts of imperfect detection on estimates of diversity and community structure through multispecies occupancy modeling

Ecologically informed priors improve Bayesian model estimates of species richness and occupancy for undetected species

Detection error can bias observations of ecological processes, especially when some species are never detected during sampling. In many communities, the probable identity of these missing species is known from previous research and natural history collections, but this information is rarely incorporated into subsequent models. Here, I present prior aggregation as a method for including information from external sources in Bayesian hierarchical detection models. Prior aggregation combines information from multiple prior distributions, in this case, an ecologically informative, species-level prior, and an uninformative community-level prior. This approach incorporates external information into the model without sacrificing the advantages of modeling species in the context of the community. Using simulated data supplied to a multispecies occupancy model, I demonstrated that prior aggregation improves estimates of (1) metacommunity richness and (2) environmental covariates were associated with species-specific occupancy probabilities. When applied to a dataset of small mammals in Vermont, prior aggregation allowed the model to estimate occupancy correlates of the Eastern cottontail Sylvilagus floridanus, a species observed at several sites in the region but never captured. Prior aggregation can be used to improve the analysis of several important metrics in population and community ecology, including abundance, survivorship, and diversity.

Bee species richness through time in an urbanizing landscape of the southeastern United States

Compared to non-urban environments, cities host ecological communities with altered taxonomic diversity and functional trait composition. However, we know little about how these urban changes take shape over time. Using historical bee (Apoidea: Anthophila) museum specimens supplemented with online repositories and researcher collections, we investigated whether bee species richness tracked urban and human population growth over the past 118 years. We also determined which species were no longer collected, whether those species shared certain traits, and if collector behavior changed over time. We focused on Wake County, North Carolina, United States where human population size has increased over 16 times over the last century along with the urban area within its largest city, Raleigh, which has increased over four times. We estimated bee species richness with occupancy models, and rarefaction and extrapolation curves to account for imperfect detection and sample coverage. To determine if bee traits correlated with when species were collected, we compiled information on native status, nesting habits, diet breadth, and sociality. We used non-metric multidimensional scaling to determine if individual collectors contributed different bee assemblages over time. In total, there were 328 species collected in Wake County. We found that although bee species richness varied, there was no clear trend in bee species richness over time. However, recent collections (since 2003) were missing 195 species, and there was a shift in trait composition, particularly lost species were below-ground nesters. The top collectors in the dataset differed in how often they collected bee species, but this was not consistent between historic and contemporary time periods; some contemporary collectors grouped closer together than others, potentially due to focusing on urban habitats. Use of historical collections and complimentary analyses can fill knowledge gaps to help understand temporal patterns of species richness in taxonomic groups that may not have planned long-term data.

Evaluating differences in density estimation for central Iowa butterflies using two methodologies

The Pollard-Yates transect is a widely used method for sampling butterflies. Data from these traditional transects are analyzed to produce density estimates, which are then used to make inferences about population status or trends. A key assumption of the Pollard-Yates transect is that detection probability is 1.0, or constant but unknown, out to a fixed distance (generally 2.5 m on either side of a transect line). However, species-specific estimates of detection probability would allow for sampling at farther distances, resulting in more detections of individuals. Our objectives were to (1) evaluate butterfly density estimates derived from Pollard-Yates line transects and distance sampling, (2) estimate how detection probabilities for butterflies vary across sampling distances and butterfly wing lengths, and (3) offer advice on future butterfly sampling techniques to estimate population density. We conducted Pollard-Yates transects and distance-sampling transects in central Iowa in 2014. For comparison to densities derived from Pollard-Yates transects, we used Program DISTANCE to model detection probability (p) and estimate density (D) for eight butterfly species representing a range of morphological characteristics. We found that detection probability among species varied beyond 2.5 m, with variation apparent even within 5 m of the line. Such variation correlated with wing size, where species with larger wing size generally had higher detection probabilities. Distance sampling estimated higher densities at the 5-m truncation for five of the eight species tested. At this truncation, detection probability was <0.8 for all species, and ranged from 0.53 to 0.79. With the exception of the little yellow (Pyrisitia lisa), species with median wing length <5.0 mm had the lowest detection probabilities. We recommend that researchers integrate distance sampling into butterfly sampling and monitoring, particularly for studies utilizing survey transects >5 m wide and when smaller species are targeted.

Long-term gut microbiome dynamics in Drosophila melanogaster reveal environment-specific associations between bacterial taxa at the family level

The influence of the microbiome on its host is well-documented, but the interplay of its members is not yet well-understood. Even for simple microbiomes, the interaction among members of the microbiome is difficult to study. Longitudinal studies provide a promising approach to studying such interactions through the temporal covariation of different taxonomic units. By contrast to most longitudinal studies, which span only a single host generation, we here present a post hoc analysis of a whole-genome dataset of 81 samples that follows microbiome composition for up to 180 host generations, which cover nearly 10 years. The microbiome diversity remained rather stable in replicated Drosophila melanogaster populations exposed to two different temperature regimes. The composition changed, however, systematically across replicates of the two temperature regimes. Significant associations between families, mostly specific to one temperature regime, indicate functional interdependence of different microbiome components. These associations also involve moderately abundant families, which emphasizes their functional importance, and highlights the importance of looking beyond the common constituents of the Drosophila microbiome.

Greater topoclimatic control of above- versus below-ground communities

Assessing the degree to which climate explains the spatial distributions of different taxonomic and functional groups is essential for anticipating the effects of climate change on ecosystems. Most effort so far has focused on above-ground organisms, which offer only a partial view on the response of biodiversity to environmental gradients. Here including both above- and below-ground organisms, we quantified the degree of topoclimatic control on the occurrence patterns of >1,500 taxa and phylotypes along a c. 3,000 m elevation gradient, by fitting species distribution models. Higher model performances for animals and plants than for soil microbes (fungi, bacteria and protists) suggest that the direct influence of topoclimate is stronger on above-ground species than on below-ground microorganisms. Accordingly, direct climate change effects are predicted to be stronger for above-ground than for below-ground taxa, whereas factors expressing local soil microclimate and geochemistry are likely more important to explain and forecast the occurrence patterns of soil microbiota. Detailed mapping and future scenarios of soil microclimate and microhabitats, together with comparative studies of interacting and ecologically dependent above- and below-ground biota, are thus needed to understand and realistically forecast the future distribution of ecosystems.

A Stepwise Approach to Assess the Occupancy State of Larval Lampreys in Streams

Pacific Lamprey Entosphenus tridentatus is an ecologically and culturally important anadromous species of conservation concern for which fisheries managers use information on occupancy state in streams to assess species status and inform stream management decisions. Here we developed a stepwise approach that incorporates the potential for nondetection and a preselected expected maximum probability of stream occupancy if field crews do not document larval Pacific Lamprey during sampling. Our approach includes seven steps: define the occupancy question; select the maximum acceptable probability of occupancy, if the species is not documented during sampling; define an assumed detection probability for the target organism; calculate required sampling effort; select sampling units; conduct sampling; and interpret sampling results into probabilistic occupancy conclusions. We examined detection probability of our approach for larval lamprey using data from multiple occupied streams in the Pacific Northwest. We illustrated our approach by evaluating Balm Grove Dam as a barrier to Pacific Lamprey migration in Gales Creek, Oregon. Bayesian estimates of detection probability in occupied streams ranged from 0.15 to 0.94, with an overall median of 0.70 (95% credible interval: 0.60–0.79). Assuming detection probability is at least 0.15 (i.e., lowest estimate), 19 reaches are required for the expected maximum probability of occupancy to be not more than 0.05, if the species is not documented through our sampling approach. Although detected downstream, we detected no larvae upstream of Balm Grove Dam; thus, we conclude that the maximum probability of occupancy upstream of Balm Grove Dam was not more than 0.05 at an assumed detection probability of 0.4, suggesting the dam as a barrier to adult migration. We provide an occupancy assessment tool with standardized sampling requirements that incorporates the potential for nondetection and the flexibility to select an expected maximum probability of occupancy if researchers document no larvae, to aid management and restoration in a single stream.