Grant Harris

Automatic Storage and Analysis of Camera Trap Data

The invention of the camera trap by George Shiras in the late 1890s and its widespread adoption by hunters 100 years later has armed scientists and managers with a powerful noninvasive tool to collect data on wildlife (Sanderson and Trolle 2005). Imagery from camera traps supports ecological investigations, inventory and monitoring networks, and cataloging biodiversity (e.g., Karanth and Nichols 1998, MacKenzie et al. 2005, Trolle et al. 2007, Stein et al. 2008). Such use of camera traps continues to expand in the number of cameras deployed and images taken (Kays and Slauson 2008, Kays et al. 2009). Yet this increase creates a paradox. While practitioners seek more data to improve analyses, they buckle under mounds of imagery piling up before them. This situation engenders four problems. First, because cataloging imagery is slow, image identification lags behind acquisition, and many images remain unidentified. Second, user entry is tedious, causing errors (Maydanchik 2007). Third, inconsistent filing and naming conventions complicate data retrieval and sharing (Chaudhary et al. 2010). Fourth, the struggle to keep pace with the acquisition and management of data from existing camera traps slows the deployment of additional cameras (and subsequent data acquisition).

Quantitative Analysis of Forest Fragmentation in the Atlantic Forest Reveals More Threatened Bird Species than the Current Red List

Habitat loss and attendant fragmentation threaten the existence of many species. Conserving these species requires a straightforward and objective method that quantifies how these factors affect their survival. Therefore, we compared a variety of metrics that assess habitat fragmentation in bird ranges, using the geographical ranges of 127 forest endemic passerine birds inhabiting the Atlantic Forest of Brazil. A common, non-biological metric — cumulative area of size-ranked fragments within a species range — was misleading, as the least threatened species had the most habitat fragmentation. Instead, we recommend a modified version of metapopulation capacity. The metric links detailed spatial information on fragment sizes and spatial configuration to the birds’ abilities to occupy and disperse across large areas (100,000+ km2). In the Atlantic Forest, metapopulation capacities were largely bimodal, in that most species’ ranges had either low capacity (high risk of extinction) or high capacity (very small risk of extinction). This pattern persisted within taxonomically and ecologically homogenous groups, indicating that it is driven by fragmentation patterns and not differences in species ecology. Worryingly, we found IUCN considers some 28 of 58 species in the low metapopulation capacity cluster to not be threatened. We propose that assessing the effect of fragmentation will separate species more clearly into distinct risk categories than does a simple assessment of remaining habitat.

Data-driven discovery of the spatial scales of habitat choice by elephants

Setting conservation goals and management objectives relies on understanding animal habitat preferences. Models that predict preferences combine location data from tracked animals with environmental information, usually at a spatial resolution determined by the available data. This resolution may be biologically irrelevant for the species in question. Individuals likely integrate environmental characteristics over varying distances when evaluating their surroundings; we call this the scale of selection. Even a single characteristic might be viewed differently at different scales; for example, a preference for sheltering under trees does not necessarily imply a fondness for continuous forest. Multi-scale preference is likely to be particularly evident for animals that occupy coarsely heterogeneous landscapes like savannahs. We designed a method to identify scales at which species respond to resources and used these scales to build preference models. We represented different scales of selection by locally averaging, or smoothing, the environmental data using kernels of increasing radii. First, we examined each environmental variable separately across a spectrum of selection scales and found peaks of fit. These ‘candidate’ scales then determined the environmental data layers entering a multivariable conditional logistic model. We used model selection via AIC to determine the important predictors out of this set. We demonstrate this method using savannah elephants (Loxodonta africana) inhabiting two parks in southern Africa. The multi-scale models were more parsimonious than models using environmental data at only the source resolution. Maps describing habitat preferences also improved when multiple scales were included, as elephants were more often in places predicted to have high neighborhood quality. We conclude that elephants select habitat based on environmental qualities at multiple scales. For them, and likely many other species, biologists should include multiple scales in models of habitat selection. Species environmental preferences and their geospatial projections will be more accurately represented, improving management decisions and conservation planning.

Effects of climate change on long-term population growth of pronghorn in an arid environment

Climate often drives ungulate population dynamics, and as climates change, some areas may become unsuitable for species persistence. Unraveling the relationships between climate and population dynamics, and projecting them across time, advances ecological understanding that informs and steers sustainable conservation for species. Using pronghorn (Antilocapra americana) as an ecological model, we used a Bayesian approach to analyze long-term population, precipitation, and temperature data from 18 populations in the southwestern United States. We determined which long-term (12 and 24 months) or short-term (gestation trimester and lactation period) climatic conditions best predicted annual rate of population growth (λ). We used these predictions to project population trends through 2090. Projections incorporated downscaled climatic data matched to pronghorn range for each population, given a high and a lower atmospheric CO2 concentration scenario. Since the 1990s, 15 of the pronghorn populations declined in abundance. Sixteen populations demonstrated a significant relationship between precipitation and λ, and in 13 of these, temperature was also significant. Precipitation predictors of λ were highly seasonal, with lactation being the most important period, followed by early and late gestation. The influence of temperature on λ was less seasonal than precipitation, and lacked a clear temporal pattern. The climatic projections indicated that all of these pronghorn populations would experience increased temperatures, while the direction and magnitude of precipitation had high population-specific variation. Models predicted that nine populations would be extirpated or approaching extirpation by 2090. Results were consistent across both atmospheric CO2 concentration scenarios, indicating robustness of trends irrespective of climatic severity. In the southwestern United States, the climate underpinning pronghorn populations is shifting, making conditions increasingly inhospitable to pronghorn persistence. This realization informs and steers conservation and management decisions for pronghorn in North America, while exemplifying how similar research can aid ungulates inhabiting arid regions and confronting similar circumstances elsewhere.

Sampling designs matching species biology produce accurate and affordable abundance indices

Wildlife biologists often use grid-based designs to sample animals and generate abundance estimates. Although sampling in grids is theoretically sound, in application, the method can be logistically difficult and expensive when sampling elusive species inhabiting extensive areas. These factors make it challenging to sample animals and meet the statistical assumption of all individuals having an equal probability of capture. Violating this assumption biases results. Does an alternative exist? Perhaps by sampling only where resources attract animals (i.e., targeted sampling), it would provide accurate abundance estimates more efficiently and affordably. However, biases from this approach would also arise if individuals have an unequal probability of capture, especially if some failed to visit the sampling area. Since most biological programs are resource limited, and acquiring abundance data drives many conservation and management applications, it becomes imperative to identify economical and informative sampling designs. Therefore, we evaluated abundance estimates generated from grid and targeted sampling designs using simulations based on geographic positioning system (GPS) data from 42 Alaskan brown bears (Ursus arctos). Migratory salmon drew brown bears from the wider landscape, concentrating them at anadromous streams. This provided a scenario for testing the targeted approach. Grid and targeted sampling varied by trap amount, location (traps placed randomly, systematically or by expert opinion), and traps stationary or moved between capture sessions. We began by identifying when to sample, and if bears had equal probability of capture. We compared abundance estimates against seven criteria: bias, precision, accuracy, effort, plus encounter rates, and probabilities of capture and recapture. One grid (49 km2 cells) and one targeted configuration provided the most accurate results. Both placed traps by expert opinion and moved traps between capture sessions, which raised capture probabilities. The grid design was least biased (−10.5%), but imprecise (CV 21.2%), and used most effort (16,100 trap-nights). The targeted configuration was more biased (−17.3%), but most precise (CV 12.3%), with least effort (7,000 trap-nights). Targeted sampling generated encounter rates four times higher, and capture and recapture probabilities 11% and 60% higher than grid sampling, in a sampling frame 88% smaller. Bears had unequal probability of capture with both sampling designs, partly because some bears never had traps available to sample them. Hence, grid and targeted sampling generated abundance indices, not estimates. Overall, targeted sampling provided the most accurate and affordable design to index abundance. Targeted sampling may offer an alternative method to index the abundance of other species inhabiting expansive and inaccessible landscapes elsewhere, provided their attraction to resource concentrations.

Correction to: Climate change and ecosystem composition across large landscapes

In the original publication, the Electronic Supplementary Material (ESM) was published incorrectly. The correct version of ESM is given in the link below.

Nonbreeding duck use at Central Flyway National Wildlife Refuges

Abstract Within the U.S. portion of the Central Flyway, the U.S. Fish and Wildlife Service manages waterfowl on numerous individual units (i.e., Refuges) within the National Wildlife Refuge System. Presently, the extent of waterfowl use that Refuges receive and the contribution of Refuges to waterfowl populations (i.e., the proportion of the Central Flyway population registered at each Refuge) remain unassessed. Such an evaluation would help determine to what extent Refuges support waterfowl relative to stated targets, aid in identifying species requiring management attention, inform management targets, and improve fiscal efficiencies. Using historic monitoring data (1954–2008), we performed this assessment for 23 Refuges in Texas, New Mexico, Oklahoma, Kansas, and Nebraska during migration and wintering months (October–March). We examined six dabbling ducks and two diving ducks, plus all dabbling ducks and all diving ducks across two periods (long-term [all data] and short-term [last 10 October–March per...

Are whooping cranes destined for extinction? Climate change imperils recruitment and population growth

Summary Identifying climatic drivers of an animal populations vital rates and locating where they operate steers conservation efforts to optimize species recovery. The population growth of endangered whooping cranes (Grus americana) hinges on juvenile recruitment. Therefore, we identify climatic drivers (solar activity [sunspots] and weather) of whooping crane recruitment throughout the species’ life cycle (breeding, migration, wintering). Our method uses a repeated cross‐validated absolute shrinkage and selection operator approach to identify drivers of recruitment. We model effects of climate change on those drivers to predict whooping crane population growth given alternative scenarios of climate change and solar activity. Years with fewer sunspots indicated greater recruitment. Increased precipitation during autumn migration signified less recruitment. On the breeding grounds, fewer days below freezing during winter and more precipitation during breeding suggested less recruitment. We predicted whooping crane recruitment and population growth may fall below long‐term averages during all solar cycles when atmospheric CO2 concentration increases, as expected, to 500 ppm by 2050. Species recovery during a typical solar cycle with 500 ppm may require eight times longer than conditions without climate change and the chance of population decline increases to 31%. Although this whooping crane population is growing and may appear secure, long‐term threats imposed by climate change and increased solar activity may jeopardize its persistence. Weather on the breeding grounds likely affects recruitment through hydrological processes and predation risk, whereas precipitation during autumn migration may influence juvenile mortality. Mitigating threats or abating climate change should occur within ≈30 years or this wild population of whooping cranes may begin declining.

Weather and Prey Predict Mammals’ Visitation to Water

Throughout many arid lands of Africa, Australia and the United States, wildlife agencies provide water year-round for increasing game populations and enhancing biodiversity, despite concerns that water provisioning may favor species more dependent on water, increase predation, and reduce biodiversity. In part, understanding the effects of water provisioning requires identifying why and when animals visit water. Employing this information, by matching water provisioning with use by target species, could assist wildlife management objectives while mitigating unintended consequences of year-round watering regimes. Therefore, we examined if weather variables (maximum temperature, relative humidity [RH], vapor pressure deficit [VPD], long and short-term precipitation) and predator-prey relationships (i.e., prey presence) predicted water visitation by 9 mammals. We modeled visitation as recorded by trail cameras at Sevilleta National Wildlife Refuge, New Mexico, USA (June 2009 to September 2014) using generalized linear modeling. For 3 native ungulates, elk (Cervus Canadensis), mule deer (Odocoileus hemionus), and pronghorn (Antilocapra americana), less long-term precipitation and higher maximum temperatures increased visitation, including RH for mule deer. Less long-term precipitation and higher VPD increased oryx (Oryx gazella) and desert cottontail rabbits (Sylvilagus audubonii) visitation. Long-term precipitation, with RH or VPD, predicted visitation for black-tailed jackrabbits (Lepus californicus). Standardized model coefficients demonstrated that the amount of long-term precipitation influenced herbivore visitation most. Weather (especially maximum temperature) and prey (cottontails and jackrabbits) predicted bobcat (Lynx rufus) visitation. Mule deer visitation had the largest influence on coyote (Canis latrans) visitation. Puma (Puma concolor) visitation was solely predicted by prey visitation (elk, mule deer, oryx). Most ungulate visitation peaked during May and June. Coyote, elk and puma visitation was relatively consistent throughout the year. Within the diel-period, activity patterns for predators corresponded with prey. Year-round water management may favor species with consistent use throughout the year, and facilitate predation. Providing water only during periods of high use by target species may moderate unwanted biological costs.

Birds, montane forest, state of Rio de Janeiro, Southeastern Brazil

Field surveys in montane Atlantic forest of Rio de Janeiro state, Brazil, provided a list of 82 bird species in four sites visited. Our protocol relied on standardized use of mist nets and observations. The birds recorded include 40 Atlantic forest endemics, three globally and two nationally Vulnerable species, and two regionally Endangered species. Data on species elevation are included and discussed. This work enhances baseline knowledge of these species to assist future studies in these poorly understood, but biologically important areas.