Krishna Pacifici

The ecology of microscopic life in household dust

We spend the majority of our lives indoors; yet, we currently lack a comprehensive understanding of how the microbial communities found in homes vary across broad geographical regions and what factors are most important in shaping the types of microorganisms found inside homes. Here, we investigated the fungal and bacterial communities found in settled dust collected from inside and outside approximately 1200 homes located across the continental US, homes that represent a broad range of home designs and span many climatic zones. Indoor and outdoor dust samples harboured distinct microbial communities, but these differences were larger for bacteria than for fungi with most indoor fungi originating outside the home. Indoor fungal communities and the distribution of potential allergens varied predictably across climate and geographical regions; where you live determines what fungi live with you inside your home. By contrast, bacterial communities in indoor dust were more strongly influenced by the number and types of occupants living in the homes. In particular, the female : male ratio and whether a house had pets had a significant influence on the types of bacteria found inside our homes highlighting that who you live with determines what bacteria are found inside your home.

Addressing structural and observational uncertainty in resource management

Most natural resource management and conservation problems are plagued with high levels of uncertainties, which make good decision making difficult. Although some kinds of uncertainties are easily incorporated into decision making, two types of uncertainty present more formidable difficulties. The first, structural uncertainty, represents our imperfect knowledge about how a managed system behaves. The second, observational uncertainty, arises because the state of the system must be inferred from imperfect monitoring systems. The former type of uncertainty has been addressed in ecology using Adaptive Management (AM) and the latter using the Partially Observable Markov Decision Processes (POMDP) framework. Here we present a unifying framework that extends standard POMDPs and encompasses both standard POMDPs and AM. The approach allows any system variable to be observed or not observed and uses any relevant observed variable to update beliefs about unknown variables and parameters. This extends standard AM, which only uses realizations of the state variable to update beliefs and extends standard POMDP by allowing more general stochastic dependence among the observable variables and the state variables. This framework enables both structural and observational uncertainty to be simultaneously modeled. We illustrate the features of the extended POMDP framework with an example.

Guidelines for a priori grouping of species in hierarchical community models

Recent methodological advances permit the estimation of species richness and occurrences for rare species by linking species-level occurrence models at the community level. The value of such methods is underscored by the ability to examine the influence of landscape heterogeneity on species assemblages at large spatial scales. A salient advantage of community-level approaches is that parameter estimates for data-poor species are more precise as the estimation process “borrows” from data-rich species. However, this analytical benefit raises a question about the degree to which inferences are dependent on the implicit assumption of relatedness among species. Here, we assess the sensitivity of community/group-level metrics, and individual-level species inferences given various classification schemes for grouping species assemblages using multispecies occurrence models. We explore the implications of these groupings on parameter estimates for avian communities in two ecosystems: tropical forests in Puerto Rico and temperate forests in northeastern United States. We report on the classification performance and extent of variability in occurrence probabilities and species richness estimates that can be observed depending on the classification scheme used. We found estimates of species richness to be most precise and to have the best predictive performance when all of the data were grouped at a single community level. Community/group-level parameters appear to be heavily influenced by the grouping criteria, but were not driven strictly by total number of detections for species. We found different grouping schemes can provide an opportunity to identify unique assemblage responses that would not have been found if all of the species were analyzed together. We suggest three guidelines: (1) classification schemes should be determined based on study objectives; (2) model selection should be used to quantitatively compare different classification approaches; and (3) sensitivity of results to different classification approaches should be assessed. These guidelines should help researchers apply hierarchical community models in the most effective manner.

Fungi identify the geographic origin of dust samples.

There is a long history of archaeologists and forensic scientists using pollen found in a dust sample to identify its geographic origin or history. Such palynological approaches have important limitations as they require time-consuming identification of pollen grains, a priori knowledge of plant species distributions, and a sufficient diversity of pollen types to permit spatial or temporal identification. We demonstrate an alternative approach based on DNA sequencing analyses of the fungal diversity found in dust samples. Using nearly 1,000 dust samples collected from across the continental U.S., our analyses identify up to 40,000 fungal taxa from these samples, many of which exhibit a high degree of geographic endemism. We develop a statistical learning algorithm via discriminant analysis that exploits this geographic endemicity in the fungal diversity to correctly identify samples to within a few hundred kilometers of their geographic origin with high probability. In addition, our statistical approach provides a measure of certainty for each prediction, in contrast with current palynology methods that are almost always based on expert opinion and devoid of statistical inference. Fungal taxa found in dust samples can therefore be used to identify the origin of that dust and, more importantly, we can quantify our degree of certainty that a sample originated in a particular place. This work opens up a new approach to forensic biology that could be used by scientists to identify the origin of dust or soil samples found on objects, clothing, or archaeological artifacts.

Occupancy estimation for rare species using a spatially-adaptive sampling design

Summary Spatially clustered populations create unique challenges for conservation monitoring programmes. Advances in methodology typically are focused on either the design or the modelling stage of the study but do not involve integration of both. We integrate adaptive cluster sampling and spatial occupancy modelling by developing two models to handle the dependence induced by cluster sampling. We compare these models to scenarios using simple random sampling and traditional occupancy models via simulation and data collected on a rare plant species, Tamarix ramosissima, found in China. Our simulations show a marked improvement in confidence interval coverage for the new models combined with cluster sampling compared to simple random sampling and traditional occupancy models, with greatest improvement in the presence of low detection probability and spatial correlation in occupancy. Accounting for the design using the simple cluster random-effects model reduces bias considerably, and full spatial modelling reduces bias further, especially for large n when the spatial covariance parameters can be estimated reliably. Both new models build on the strength of occupancy modelling and adaptive sampling and perform at least as well, and often better, than occupancy modelling alone. We believe our approach is unique and potentially useful for a variety of studies directed at patchily distributed, clustered or rare species exhibiting spatial variation.

Assessing the influence of habitat quality on movements of the endangered shortnose sturgeon

Movements of the endangered shortnose sturgeon Acipenser brevirostrum in the Ogeechee River (Georgia, USA) may be limited by unsuitable habitat conditions during June–September. The research objective was to determine if habitat quality is likely to impede movements and spawning of shortnose sturgeon in this system. We inserted ultrasonic transmitters in 18 adult shortnose sturgeon to monitor their monthly in-stream movements. Water quality data were collected at discrete locations along the Ogeechee River. We used geostatistical models based on Weighted Asymmetric Hydrologic Distance, in place of Euclidean distance, to predict water quality variables along the Ogeechee River, avoiding problems associated with linear distance metrics in a river network. Using ArcGIS, we constructed habitat quality models based on physiological tolerance to water temperature, dissolved oxygen, and salinity. During the summer months, tagged fish remained congregated above the fresh-saltwater interface. However, individuals appeared to move in response to changing water quality conditions. Seasonal habitat availability in other southern rivers should be similarly analyzed to assess potential relationships between the habitat and sturgeon movements. Although further laboratory and field studies are needed to better understand latitudinal variation in life history and environmental tolerances of shortnose sturgeon, the results of our study suggest that temporal and spatial variability in water quality affect habitat availability of southern populations of shortnose sturgeon.

Efficient Use of Information in Adaptive Management with an Application to Managing Recreation near Golden Eagle Nesting Sites

It is generally the case that a significant degree of uncertainty exists concerning the behavior of ecological systems. Adaptive management has been developed to address such structural uncertainty, while recognizing that decisions must be made without full knowledge of how a system behaves. This paradigm attempts to use new information that develops during the course of management to learn how the system works. To date, however, adaptive management has used a very limited information set to characterize the learning that is possible. This paper uses an extension of the Partial Observable Markov Decision Process (POMDP) framework to expand the information set used to update belief in competing models. This feature can potentially increase the speed of learning through adaptive management, and lead to better management in the future. We apply this framework to a case study wherein interest lies in managing recreational restrictions around golden eagle (Aquila chrysaetos) nesting sites. The ultimate management objective is to maintain an abundant eagle population in Denali National Park while minimizing the regulatory burden on park visitors. In order to capture this objective, we developed a utility function that trades off expected breeding success with hiker access. Our work is relevant to the management of human activities in protected areas, but more generally demonstrates some of the benefits of POMDP in the context of adaptive management.

Occupancy and Abundance of Eleutherodactylus Frogs in Coffee Plantations in Puerto Rico

Abstract Shaded coffee plantations are of conservation value for many taxa, particularly for resident avifauna in the face of extensive landscape changes. Yet, little is known about the value of coffee plantations for amphibians because there are scant demographic data to index their value among species with different habitat preferences. We estimated the probability of occupancy of three frog species: Eleutherodactylus wightmanae, a forest species; E. brittoni, a grassland species; and E. antillensis, an open habitat species. Occupancy was estimated in sun and shaded plantations, and in secondary forest, in the west-central mountains of Puerto Rico. We also estimated the probability that a survey station was occupied by no individuals, one, or >1 individual, as a proxy of abundance. The aforementioned parameters, and local colonization and extinction probability, were modeled as a function of weather conditions (temperature, humidity) and vegetation cover at the sampling station (5 m) and contextual (100 m) scales. Encounter histories were obtained with passive acoustic recorders between February and July in 2015. Consistent with known habitat preferences, the highest occupancies were associated with secondary forests for E. wightmanae and sun plantations for E. brittoni. Occupancy probability for E. antillensis was similar across habitat types, indicating no aversion to shaded–forested habitats. Shaded plantations harbored moderate levels of occupancy for all species, indicating their potential value for multispecies conservation. Local colonization rates increased with forest cover for E. wightmanae, and with open habitats for E. brittoni and E. antillensis. Open habitats harbored a higher abundance of E. brittoni and E antillensis, but lower values for E. wightmanae. Sun and shaded plantations could provide quality habitat for Eleutherodactylus spp. if managed for features that promote local colonization and abundance.

Is the Red Wolf a Listable Unit Under the US Endangered Species Act

Abstract Defining units that can be afforded legal protection is a crucial, albeit challenging, step in conservation planning. As we illustrate with a case study of the red wolf (Canis rufus) from the southeastern United States, this step is especially complex when the evolutionary history of the focal taxon is uncertain. The US Endangered Species Act (ESA) allows listing of species, subspecies, or Distinct Population Segments (DPSs) of vertebrates. Red wolves were listed as an endangered species in 1973, and their status remains precarious. However, some recent genetic studies suggest that red wolves are part of a small wolf species (C. lycaon) specialized for heavily forested habitats of eastern North America, whereas other authors suggest that red wolves arose, perhaps within the last ~400 years, through hybridization between gray wolves (C. lupus) and coyotes (C. latrans). Using published genetic, morphological, behavioral, and ecological data, we evaluated whether each evolutionary hypothesis would lead to a listable unit for red wolves. Although the potential hybrid origin of red wolves, combined with abundant evidence for recent hybridization with coyotes, raises questions about status as a separate species or subspecies, we conclude that under any proposed evolutionary scenario red wolves meet both criteria to be considered a DPS: they are Discrete compared with other conspecific populations, and they are Significant to the taxon to which they belong. As population-level units can qualify for legal protection under endangered-species legislation in many countries throughout the world, this general approach could potentially be applied more broadly.

A spatial model for rare binary events

Many predominant spatial methods for binary data use a latent Gaussian process to capture spatial dependence. However, this may not be appropriate for rare data because these methods based on Gaussian processes are asymptotically independent as the event probability goes to zero. In this paper, we propose a method for rare binary data that builds on spatial extreme value theory. We model binary events as exceedances of a max-stable process and show that this construction maintains spatial dependence even as the event probability goes to zero. We compare our model to spatial probit and logistic methods through a simulation study and analysis of a survey of Tamarix ramosissima and Hedysarum scoparium. We find some evidence that for very rare data the max-stable extension provides an improvement in spatial prediction compared to Gaussian models.