Optimal risk-based allocation of disease surveillance effort for clustered disease outbreaks.

Designing a disease surveillance program to detect a disease is challenging when animals are organized into herds, in part because disease cases are likely to be clustered. Clustered diseases are often surveilled using two-stage sampling, which allocates tests both among herds and within herds. Finding the optimal allocation of tests is computationally difficult, so some surveillance programs simply seek an approximate solution. We developed a search algorithm to find the optimal allocation of tests by iteratively searching for adjustments to the test allocation that yielded marginal improvements in system sensitivity. We digitally generated 21 herds of various sizes, evenly divided among three regions that differed in relative risk. We then analyzed 29 scenarios that differed in disease and testing characteristics. We also analyzed a Chronic Wasting Disease (CWD) surveillance effort for 23 elk game management units of various sizes that were spread across three regions in Arizona, USA. We compared our marginal sensitivity approach to two other strategies for approximating the optimal distribution of tests: allocating the same number of tests to all herds selected for testing, and allocating tests so that all herds selected for testing achieve the same sensitivity. Across analysis scenarios, we found that low prevalence, high relative risk, a small budget, or high overhead costs were best addressed by concentrating tests in large, high-risk herds. When we expect multiple herds to be infected, the optimal allocation of tests depended on how we expected the cases to be distributed. Across the analyzed scenarios, our marginal sensitivity approach was most efficient, with alternative strategies requiring 0-228 % more tests to achieve the same sensitivity. For CWD in Arizona, we found the potential to double system sensitivity, given a population design prevalence of 0.16 %, from 35.8 % to 70.5 %, although social and budgetary considerations would likely constrain changes to the current allocation of tests. The marginal sensitivity approach we developed has the potential to improve disease surveillance, especially when a population includes a limited number of herds that differ in size. An important limitation of our approach is that computer runtimes could become unacceptably long for a population with many herds.

Partitioning global change: Assessing the relative importance of changes in climate and land cover for changes in avian distribution.

Understanding the relative impact of climate change and land cover change on changes in avian distribution has implications for the future course of avian distributions and appropriate management strategies. Due to the dynamic nature of climate change, our goal was to investigate the processes that shape species distributions, rather than the current distributional patterns. To this end, we analyzed changes in the distribution of Eastern Wood Pewees (Contopus virens) and Red-eyed Vireos (Vireo olivaceus) from 1997 to 2012 using Breeding Bird Survey data and dynamic correlated-detection occupancy models. We estimated the local colonization and extinction rates of these species in relation to changes in climate (hours of extreme temperature) and changes in land cover (amount of nesting habitat). We fit six nested models to partition the deviance explained by spatial and temporal components of land cover and climate. We isolated the temporal components of environmental variables because this is the essence of global change. For both species, model fit was significantly improved when we modeled vital rates as a function of spatial variation in climate and land cover. Model fit improved only marginally when we added temporal variation in climate and land cover to the model. Temporal variation in climate explained more deviance than temporal variation in land cover, although both combined only explained 20% (Eastern Wood Pewee) and 6% (Red-eyed Vireo) of temporal variation in vital rates. Our results showing a significant correlation between initial occupancy and environmental covariates are consistent with biological expectation and previous studies. The weak correlation between vital rates and temporal changes in covariates indicated that we have yet to identify the most relevant components of global change influencing the distributions of these species and, more importantly, that spatially significant covariates are not necessarily driving temporal shifts in avian distributions.

Effect of Canine Parvovirus and Canine Distemper Virus on the Mexican Wolf (Canis lupus baileyi) Population in the USA.

Mexican wolves (Canis lupus baileyi), classified as probably extinct in the wild in Mexico and endangered in the US, were reintroduced into Arizona in 1998. We combined annual serologic testing results from samples collected between 2003 and 2016 from 108 wolves and known survival data from 118 wolves born in the recovery area from 2003 to 2014 to evaluate whether exposure to canine distemper virus (CDV) or canine parvovirus (CPV) was associated with a greater risk of mortality before 2 yr of age. We used mixed-effects logistic regression to estimate the effect of CDV and CPV on the probability of mortality. Annual seroprevalence rates for CDV and CPV ranged from 0% to 62% and from 33% to 100%, respectively (median, 14.2% and 90.3%, respectively). The covariate, age at testing, had a negative effect on mortality, indicating that younger animals had lower survival, whereas sex had little effect on mortality. The best-supported model excluded any effect of CPV or CDV on death before 2 yr old at both the pack and individual level. Although our analysis did not detect an effect of these viruses on mortality before 2 yr old, CDV was later identified as the cause of mortality in two individuals in 2017. Additional information is needed to assess the impact of these diseases on Mexican wolves.

Accounting for imperfect detection of groups and individuals when estimating abundance.

If animals are independently detected during surveys, many methods exist for estimating animal abundance despite detection probabilities <1. Common estimators include double-observer models, distance sampling models and combined double-observer and distance sampling models (known as mark-recapture-distance-sampling models; MRDS). When animals reside in groups, however, the assumption of independent detection is violated. In this case, the standard approach is to account for imperfect detection of groups, while assuming that individuals within groups are detected perfectly. However, this assumption is often unsupported. We introduce an abundance estimator for grouped animals when detection of groups is imperfect and group size may be under-counted, but not over-counted. The estimator combines an MRDS model with an N-mixture model to account for imperfect detection of individuals. The new MRDS-Nmix model requires the same data as an MRDS model (independent detection histories, an estimate of distance to transect, and an estimate of group size), plus a second estimate of group size provided by the second observer. We extend the model to situations in which detection of individuals within groups declines with distance. We simulated 12 data sets and used Bayesian methods to compare the performance of the new MRDS-Nmix model to an MRDS model. Abundance estimates generated by the MRDS-Nmix model exhibited minimal bias and nominal coverage levels. In contrast, MRDS abundance estimates were biased low and exhibited poor coverage. Many species of conservation interest reside in groups and could benefit from an estimator that better accounts for imperfect detection. Furthermore, the ability to relax the assumption of perfect detection of individuals within detected groups may allow surveyors to re-allocate resources toward detection of new groups instead of extensive surveys of known groups. We believe the proposed estimator is feasible because the only additional field data required are a second estimate of group size.

Estimating indices of range shifts in birds using dynamic models when detection is imperfect.

There is intense interest in basic and applied ecology about the effect of global change on current and future species distributions. Projections based on widely used static modeling methods implicitly assume that species are in equilibrium with the environment and that detection during surveys is perfect. We used multiseason correlated detection occupancy models, which avoid these assumptions, to relate climate data to distributional shifts of Louisiana Waterthrush in the North American Breeding Bird Survey (BBS) data. We summarized these shifts with indices of range size and position and compared them to the same indices obtained using more basic modeling approaches. Detection rates during point counts in BBS surveys were low, and models that ignored imperfect detection severely underestimated the proportion of area occupied and slightly overestimated mean latitude. Static models indicated Louisiana Waterthrush distribution was most closely associated with moderate temperatures, while dynamic occupancy models indicated that initial occupancy was associated with diurnal temperature ranges and colonization of sites was associated with moderate precipitation. Overall, the proportion of area occupied and mean latitude changed little during the 1997-2013 study period. Near-term forecasts of species distribution generated by dynamic models were more similar to subsequently observed distributions than forecasts from static models. Occupancy models incorporating a finite mixture model on detection - a new extension to correlated detection occupancy models - were better supported and may reduce bias associated with detection heterogeneity. We argue that replacing phenomenological static models with more mechanistic dynamic models can improve projections of future species distributions. In turn, better projections can improve biodiversity forecasts, management decisions, and understanding of global change biology.

The effect of call libraries and acoustic filters on the identification of bat echolocation.

Quantitative methods for species identification are commonly used in acoustic surveys for animals. While various identification models have been studied extensively, there has been little study of methods for selecting calls prior to modeling or methods for validating results after modeling. We obtained two call libraries with a combined 1556 pulse sequences from 11 North American bat species. We used four acoustic filters to automatically select and quantify bat calls from the combined library. For each filter, we trained a species identification model (a quadratic discriminant function analysis) and compared the classification ability of the models. In a separate analysis, we trained a classification model using just one call library. We then compared a conventional model assessment that used the training library against an alternative approach that used the second library. We found that filters differed in the share of known pulse sequences that were selected (68 to 96%), the share of non-bat noises that were excluded (37 to 100%), their measurement of various pulse parameters, and their overall correct classification rate (41% to 85%). Although the top two filters did not differ significantly in overall correct classification rate (85% and 83%), rates differed significantly for some bat species. In our assessment of call libraries, overall correct classification rates were significantly lower (15% to 23% lower) when tested on the second call library instead of the training library. Well-designed filters obviated the need for subjective and time-consuming manual selection of pulses. Accordingly, researchers should carefully design and test filters and include adequate descriptions in publications. Our results also indicate that it may not be possible to extend inferences about model accuracy beyond the training library. If so, the accuracy of acoustic-only surveys may be lower than commonly reported, which could affect ecological understanding or management decisions based on acoustic surveys.

Tree structure and cavity microclimate: implications for bats and birds.

It is widely assumed that tree cavity structure and microclimate affect cavity selection and use in cavity-dwelling bats and birds. Despite the interest in tree structure and microclimate, the relationship between the two has rarely been quantified. Currently available data often comes from artificial structures that may not accurately represent conditions in natural cavities. We collected data on tree cavity structure and microclimate from 45 trees in five cypress-gum swamps in the Coastal Plain of Georgia in the United States in 2008. We used hierarchical linear models to predict cavity microclimate from tree structure and ambient temperature and humidity, and used Aikaike's information criterion to select the most parsimonious models. We found large differences in microclimate among trees, but tree structure variables explained <28% of the variation, while ambient conditions explained >80% of variation common to all trees. We argue that the determinants of microclimate are complex and multidimensional, and therefore cavity microclimate cannot be deduced easily from simple tree structures. Furthermore, we found that daily fluctuations in ambient conditions strongly affect microclimate, indicating that greater weather fluctuations will cause greater differences among tree cavities.

Simple syllabic calls accompany discrete behavior patterns in captive Pteronotus parnellii: an illustration of the motivation-structure hypothesis.

Mustached bats, Pteronotus parnellii, are highly social and vocal. Individuals of this species roost in tight clusters, and emit an acoustically rich repertoire of calls whose behavioral significance is largely unknown. We recorded their social and vocal behaviors within a colony housed under semi-natural conditions. We also quantified the spatial spread of each bat's roosting location and discovered that this was relatively fixed and roughly confined to an individual's body width. The spatial precision in roosting was accompanied by an equally remarkable match between specific vocalizations and well-timed, discrete, identifiable postures/behaviors, as revealed by logistic regression analysis. The bodily behaviors included crouching, marking, yawning, nipping, flicking, fighting, kissing, inspecting, and fly-bys. Two echolocation-like calls were used to maintain spacing in the colony, two noisy broadband calls were emitted during fights, two tonal calls conveyed fear, and another tonal call signaled appeasement. Overall, the results establish that mustached bats exhibit complex social interactions common to other social mammals. The correspondence of relatively low frequency and noisy, broadband calls with aggression, and of tonal, high frequency calls with fear supports Morton's Motivation-Structure hypothesis, and establishes a link between motivation and the acoustic structure of social calls emitted by mustached bats.