Thomas J. Rodhouse

Field identification of the cryptic vespertilionid bats, Myotis lucifugus and M. yumanensis

ABSTRACT Recent advances in molecular techniques have provided new tools for confirming species identities, however they can be expensive and results are not immediately available. Myotis lucificugus and M. yumanensis are morphologically cryptic species of bats sympatric in western North America that can be difficult to distinguish in the field. We evaluated a set of models that used morphological and echolocation call characters obtained in the field to predict species identity as determined by DNA analysis. We constructed models using data from 98 M. lucifugus and 100 M. yumanensis captured throughout the Pacific Northwest from which we had obtained high-quality, time-expansion recordings of their echolocation calls. The best model for distinguishing the species combined forearm length and characteristic frequency of echolocation calls and was able to identify 92% of M. lucifugus and 91% of M. yumanensis individuals, with ≥95% confidence. We evaluated the applicability of our model by testing it on additional datasets. Our model correctly classified 83% of M. lucifugus (n = 30) and 93% of M. yumanensis (n = 29) individuals captured in north-central Oregon, whose echolocation calls were recorded using a zero-crossings echolocation detection system. It also correctly classified 86% of M. lucifugus (n = 22) and 85% of M. yumanensis (n = 26) individuals, captured throughout our study area, for which only poor-quality time-expansion recordings of echolocation calls were obtained. Combining morphometrics with echolocation call characteristics may be a useful approach for distinguishing among pairs of cryptic species of bats in other areas.

A Practical Sampling Design for Acoustic Surveys of Bats

ABSTRACT Acoustic surveys are widely used for describing bat occurrence and activity patterns and are increasingly important for addressing concerns for habitat management, wind energy, and disease on bat populations. Designing these surveys presents unique challenges, particularly when a probabilistic sample is required for drawing inference to unsampled areas. Sampling frame errors and other logistical constraints often require survey sites to be dropped from the sample and new sites added. Maintaining spatial balance and representativeness of the sample when these changes are made can be problematic. Spatially balanced sampling designs recently developed to support aquatic surveys along rivers provide solutions to a number of practical challenges faced by bat researchers and allow for sample site additions and deletions, support unequal-probability selection of sites, and provide an approximately unbiased local neighborhood-weighted variance estimator that is efficient for spatially structured populations such as is typical for bats. We implemented a spatially balanced design to survey canyon bat (Parastrellus hesperus) activity along a stream network. The spatially balanced design accommodated typical logistical challenges and yielded a 25% smaller estimated standard error for the mean activity level than the usual simple random sampling estimator. Spatially balanced designs have broad application to bat research and monitoring programs and will improve studies relying on model-based inference (e.g., occupancy models) by providing flexibility and protection against violations of the independence assumption, even if design-based estimators are not used. Our approach is scalable and can be used for pre- and post-construction surveys along wind turbine arrays and for regional monitoring programs.

Assessing the status and trend of bat populations across broad geographic regions with dynamic distribution models

Bats face unprecedented threats from habitat loss, climate change, disease, and wind power development, and populations of many species are in decline. A better ability to quantify bat population status and trend is urgently needed in order to develop effective conservation strategies. We used a Bayesian autoregressive approach to develop dynamic distribution models for Myotis lucifugus, the little brown bat, across a large portion of northwestern USA, using a four-year detection history matrix obtained from a regional monitoring program. This widespread and abundant species has experienced precipitous local population declines in northeastern USA resulting from the novel disease white-nose syndrome, and is facing likely range-wide declines. Our models were temporally dynamic and accounted for imperfect detection. Drawing on species-energy theory, we included measures of net primary productivity (NPP) and forest cover in models, predicting that M. lucifugus occurrence probabilities would covary positively along those gradients. Despite its common status, M. lucifugus was only detected during -50% of the surveys in occupied sample units. The overall naive estimate for the proportion of the study region occupied by the species was 0.69, but after accounting for imperfect detection, this increased to -0.90. Our models provide evidence of an association between NPP and forest cover and M. lucifugus distribution, with implications for the projected effects of accelerated climate change in the region, which include net aridification as snowpack and stream flows decline. Annual turnover, the probability that an occupied sample unit was a newly occupied one, was estimated to be low (-0.04-0.14), resulting in flat trend estimated with relatively high precision (SD = 0.04). We mapped the variation in predicted occurrence probabilities and corresponding prediction uncertainty along the productivity gradient. Our results provide a much needed baseline against which future anticipated declines in M. lucifugus occurrence can be measured. The dynamic distribution modeling approach has broad applicability to regional bat monitoring efforts now underway in several countries and we suggest ways to improve and expand our grid-based monitoring program to gain robust insights into bat population status and trend across large portions of North America.

Accounting for false-positive acoustic detections of bats using occupancy models

Summary 1. Acoustic surveys have become a common survey method for bats and other vocal taxa. Previous work shows that bat echolocation may be misidentified, but common analytic methods, such as occupancy models, assume that misidentifications do not occur. Unless rare, such misidentifications could lead to incorrect inferences with significant management implications. 2. We fit a false-positive occupancy model to data from paired bat detector and mist-net surveys to estimate probability of presence when survey data may include false positives. We compared estimated occupancy and detection rates to those obtained from a standard occupancy model. We also derived a formula to estimate the probability that bats were present at a site given its detection history. As an example, we analysed survey data for little brown bats Myotis lucifugus from 135 sites in Washington and Oregon, USA. 3. We estimated that at an unoccupied site, acoustic surveys had a 14% chance per night of producing spurious M. lucifugus detections. Estimated detection rates were higher and occupancy rates were lower under the false-positive model, relative to a standard occupancy model. Un-modelled false positives also affected inferences about occupancy at individual sites. For example, probability of occupancy at individual sites with acoustic detections but no captures ranged from 2% to 100% under the false-positive occupancy model, but was always 100% under a standard occupancy model. 4. Synthesis and applications. Our results suggest that false positives sufficient to affect inferences may be common in acoustic surveys for bats. We demonstrate an approach that can estimate occupancy, regardless of the false-positive rate, when acoustic surveys are paired with capture surveys. Applications of this approach include monitoring the spread of WhiteNose Syndrome, estimating the impact of climate change and informing conservation listing decisions. We calculate a site-specific probability of occupancy, conditional on survey results, which could inform local permitting decisions, such as for wind energy projects. More generally, the magnitude of false positives suggests that false-positive occupancy models can improve accuracy in research and monitoring of bats and provide wildlife managers with more reliable information.

Predicting foundation bunchgrass species abundances: model-assisted decision-making in protected-area sagebrush steppe

Foundation species are structurally dominant members of ecological communities that can stabilize ecological processes and influence resilience to disturbance and resistance to invasion. Being common, they are often overlooked for conservation but are increasingly threatened from land use change, biological invasions, and over-exploitation. The pattern of foundation species abundances over space and time may be used to guide decision-making, particularly in protected areas for which they are iconic. We used ordinal logistic regression to identify the important environmental influences on the abundance patterns of bluebunch wheatgrass (Pseudoroegneria spicata), Thurbers needlegrass (Achnatherum thurberianum), and Sandberg bluegrass (Poa secunda) in protected-area sagebrush steppe. We then predicted bunchgrass abundances along gradients of topography, disturbance, and invasive annual grass abundance. We used model predictions to prioritize the landscape for implementation of a management and restoration decision-support tool. Models were fit to categorical estimates of grass cover obtained from an extensive ground-based monitoring dataset. We found that remnant stands of abundant wheatgrass and bluegrass were associated with steep north-facing slopes in higher and more remote portions of the landscape outside of recently burned areas where invasive annual grasses were less abundant. These areas represented only 25% of the landscape and were prioritized for protection efforts. Needlegrass was associated with south-facing slopes, but in low abundance and in association with invasive cheatgrass (Bromus tectorum). Abundances of all three species were strongly negatively correlated with occurrence of another invasive annual grass, medusahead (Taeniatherum caput-medusae). The rarity of priority bunchgrass stands underscored the extent of degradation and the need for prioritization. We found no evidence that insularity reduced invasibility; annual grass invasion represents a serious threat to protected-area bunchgrass communities. Our study area was entirely within the Wyoming big sagebrush ecological zone, understood to have inherently low resilience to disturbance and resistance to weed invasion. However, our study revealed important variation in abundance of the foundation species associated with resilience and resistance along the topographic-soil moisture gradient within this zone, providing an important foothold for conservation decision-making in these steppe ecosystems. We found the foundation species focus a parsimonious strategy linking monitoring to decision-making via biogeographic modeling.

Replicated landscape genetic and network analyses reveal wide variation in functional connectivity for American pikas.

Landscape connectivity is essential for maintaining viable populations, particularly for species restricted to fragmented habitats or naturally arrayed in metapopulations and facing rapid climate change. The importance of assessing both structural connectivity (physical distribution of favorable habitat patches) and functional connectivity (how species move among habitat patches) for managing such species is well understood. However, the degree to which functional connectivity for a species varies among landscapes, and the resulting implications for conservation, have rarely been assessed. We used a landscape genetics approach to evaluate resistance to gene flow and, thus, to determine how landscape and climate-related variables influence gene flow for American pikas (Ochotona princeps) in eight federally managed sites in the western United States. We used empirically derived, individual-based landscape resistance models in conjunction with predictive occupancy models to generate patch-based network models describing functional landscape connectivity. Metareplication across landscapes enabled identification of limiting factors for dispersal that would not otherwise have been apparent. Despite the cool microclimates characteristic of pika habitat, south-facing aspects consistently represented higher resistance to movement, supporting the previous hypothesis that exposure to relatively high temperatures may limit dispersal in American pikas. We found that other barriers to dispersal included areas with a high degree of topographic relief, such as cliffs and ravines, as well as streams and distances greater than 1-4 km depending on the site. Using the empirically derived network models of habitat patch connectivity, we identified habitat patches that were likely disproportionately important for maintaining functional connectivity, areas in which habitat appeared fragmented, and locations that could be targeted for management actions to improve functional connectivity. We concluded that climate change, besides influencing patch occupancy as predicted by other studies, may alter landscape resistance for pikas, thereby influencing functional connectivity through multiple pathways simultaneously. Spatial autocorrelation among genotypes varied across study sites and was largest where habitat was most dispersed, suggesting that dispersal distances increased with habitat fragmentation, up to a point. This study demonstrates how landscape features linked to climate can affect functional connectivity for species with naturally fragmented distributions, and reinforces the importance of replicating studies across landscapes.

Field Identification of Myotis yumanensis and Myotis lucifugus: A Morphological Evaluation

Abstract Myotis lucifigus and Myotis yumanensis are 2 species of bats subject to potentially high rates of misidentification because they are often difficult to differentiate in the hand under field conditions. We tested the utility of a suite of external morphological characteristics frequently cited in regional keys to differentiate the 2 species in the field. Forearm length, dorsal pelage sheen, ear color, and forehead slope were examined from 101 bats captured in central Oregon during 2002–2003. Post hoc genetic analysis was performed on tissue samples collected from the 101 bats to confirm identification. Forearm lengths overlapped considerably between species. Only 18% of M. yumanensis and 17% of M. lucifugus were correctly identified with probability ≥95% using forearm length alone. Pelage sheen, ear color, and forehead slope successfully identified 96%, 82%, and 77% of individual bats, respectively. When forearm length was considered together with other traits, identification rates ranged from 92% to 20%. Ability to correctly identify M. yumanensis was 2–6 times greater than for M. lucifugus. Pelage sheen was useful in our study; however, using this character required a subjective decision from the observer, and the result often contradicted other characters for species identification stated in regional keys. For these reasons, we recommend that morphological features be used judiciously and only as supportive criteria for field identification in combination with voucher echolocation calls and genetic confirmation.

A goodness‐of‐fit test for occupancy models with correlated within‐season revisits

Abstract Occupancy modeling is important for exploring species distribution patterns and for conservation monitoring. Within this framework, explicit attention is given to species detection probabilities estimated from replicate surveys to sample units. A central assumption is that replicate surveys are independent Bernoulli trials, but this assumption becomes untenable when ecologists serially deploy remote cameras and acoustic recording devices over days and weeks to survey rare and elusive animals. Proposed solutions involve modifying the detection‐level component of the model (e.g., first‐order Markov covariate). Evaluating whether a model sufficiently accounts for correlation is imperative, but clear guidance for practitioners is lacking. Currently, an omnibus goodness‐of‐fit test using a chi‐square discrepancy measure on unique detection histories is available for occupancy models (MacKenzie and Bailey, Journal of Agricultural, Biological, and Environmental Statistics, 9, 2004, 300; hereafter, MacKenzie–Bailey test). We propose a join count summary measure adapted from spatial statistics to directly assess correlation after fitting a model. We motivate our work with a dataset of multinight bat call recordings from a pilot study for the North American Bat Monitoring Program. We found in simulations that our join count test was more reliable than the MacKenzie–Bailey test for detecting inadequacy of a model that assumed independence, particularly when serial correlation was low to moderate. A model that included a Markov‐structured detection‐level covariate produced unbiased occupancy estimates except in the presence of strong serial correlation and a revisit design consisting only of temporal replicates. When applied to two common bat species, our approach illustrates that sophisticated models do not guarantee adequate fit to real data, underscoring the importance of model assessment. Our join count test provides a widely applicable goodness‐of‐fit test and specifically evaluates occupancy model lack of fit related to correlation among detections within a sample unit. Our diagnostic tool is available for practitioners that serially deploy survey equipment as a way to achieve cost savings.

Habitat selection of rodents along a pinon-juniper woodland- savannah gradient

Abstract The biodiversity of piñon–juniper (P-J) woodlands of western North America has received increasing attention from conservationists because of concerns about woodland invasion into adjacent shrub-steppe communities. Small mammals contribute substantially to the faunal diversity of P-J woodlands, but the responses of small mammals to the inherent structural complexity of these woodlands have not been well described. We used occupancy modeling and ordination to describe habitat selection along a woodland–savannah gradient of 4 species of rodents, including 2 putative P-J woodland specialists, Peromyscus truei and Tamias dorsalis. We accounted for the influences of season and prior conspecific capture on detectability. P. truei and T. dorsalis preferred old-growth woodland sites near rock outcrops with high canopy and bare-ground cover and low herb and shrub cover. Tamias minimus preferred savannah sites with high shrub and grass cover farther from rock outcrops. We found no evidence of resource selection along the gradient by Perognathus parvus. Our results provide insight into the role of habitat selection in facilitating coexistence among closely related species within P-J woodlands and suggest that management efforts that better discriminate between old, presettlement stands and young, invasive stands may enhance regional biodiversity. We demonstrate an effective application of occupancy modeling for describing habitat selection of cryptic rodents, which are particularly prone to imperfect detection.

Woodland Dynamics at the Northern Range Periphery: A Challenge for Protected Area Management in a Changing World

Managers of protected natural areas increasingly are confronted with novel ecological conditions and conflicting objectives to preserve the past while fostering resilience for an uncertain future. This dilemma may be pronounced at range peripheries where rates of change are accelerated and ongoing invasions often are perceived as threats to local ecosystems. We provide an example from City of Rocks National Reserve (CIRO) in southern Idaho, positioned at the northern range periphery of pinyon-juniper (P-J) woodland. Reserve managers are concerned about P-J woodland encroachment into adjacent sagebrush steppe, but the rates and biophysical variability of encroachment are not well documented and management options are not well understood. We quantified the rate and extent of woodland change between 1950 and 2009 based on a random sample of aerial photo interpretation plots distributed across biophysical gradients. Our study revealed that woodland cover remained at approximately 20% of the study area over the 59-year period. In the absence of disturbance, P-J woodlands exhibited the highest rate of increase among vegetation types at 0.37% yr−1. Overall, late-successional P-J stands increased in area by over 100% through the process of densification (infilling). However, wildfires during the period resulted in a net decrease of woody evergreen vegetation, particularly among early and mid-successional P-J stands. Elevated wildfire risk associated with expanding novel annual grasslands and drought is likely to continue to be a fundamental driver of change in CIRO woodlands. Because P-J woodlands contribute to regional biodiversity and may contract at trailing edges with global warming, CIRO may become important to P-J woodland conservation in the future. Our study provides a widely applicable toolset for assessing woodland ecotone dynamics that can help managers reconcile the competing demands to maintain historical fidelity and contribute meaningfully to the U.S. protected area network in a future with novel, no-analog ecosystems.