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In the 1930s, the Canada Lynx (Lynx canadensis) was extirpated from Isle Royale, a 535-km2 island located in western Lake Superior, 22 km from the Ontario and Minnesota shorelines. The first half of the 20th century was a time of change on Isle Royale as Caribou (Rangifer tarandus) disappeared, Coyotes (Canis latrans) briefly appeared, Moose (Alces americanus), Grey Wolves (Canis lupus), and Red Foxes (Vulpes vulpes) became established, and the habitat was altered by fire, logging, and over-browsing. Although these changes may have contributed to the demise of the Canada Lynx, our results suggest that over-harvest was a primary cause. Assuming a peak carrying capacity of 75 Canada Lynx and harvest rates comparable to those reported from 1890–1935, a population viability analysis indicated that the island population had a 0% chance of surviving 50 years. The analysis also indicated that, even in the absence of harvest, the population had only a 14% chance of persistence for 250 years. However, when 10 Canada Lynx were added to the modeled population every 10th year, the probability of persistence increased to 100%. Our analyses suggest that the island’s Canada Lynx population maintained itself by periodic immigration across an ice bridge; therefore, natural recolonization should be possible. However, the probability of ice-bridge formation has declined from 0.8 in the winter of 1958–59 to 0.1 in 2012–13, likely as a result of climate change. The Isle Royale situation exemplifies another impact of climate change and the possible need to augment populations to mitigate the loss of connectivity.

Publisher Summary Animal space use and movements are best understood through direct observation. This chapter describes a variety of techniques used to analyze animal space use, including home range estimation, analysis of site fidelity, and animal interactions. It also provides limitations of using many traditional techniques. The chapter provides a discussion of modeling animal movements using a combination of advanced descriptive and visualization approaches, general movement models, and biological models. The importance of sample size and autocorrelation of data in estimating home ranges and animal movements cannot be overemphasized. When considering tradeoffs between sample size and independence, obtaining an adequate sample size is more important than independence between data points. Any analysis of radiotelemetry data must consider the full range of biological and environmental factors that may impact the results. Home range estimation and animal movement analyses must answer difficult questions regarding the internal anatomy of home range boundaries and movement pathways through the judicious use of biological and environmental information. Also, radiotelemetry studies should go beyond simple, general descriptions and use a model-driven approach to examine the key factors determining why and how an animal uses space.

Based on recent applications to wild and trained birds [1–3], Rutz and Bluff highlighted developments in animal-borne video and environmental data collection systems (AVEDs) [4]. We agree that AVEDs can provide novel insights relevant to behavioural ecology and conservation [5]. Their emphasis on video-tracking reinforces our broader recommendations about the utility of combining video with other sensors [5]. However, Rutz and Bluff give ecologists an overly optimistic view of transmission-based AVEDs and video-tracking.

In matters of policy, many scientists view “advocacy” as a dangerous word. In peer-reviewed literature, many scientists practice a subtler form of advocacy in pushing their methods, results, and conclusions. Call this IMRAD (Introduction, Methods, Results, and Discussion) advocacy: Once bold claims about a poorly tested method or weak result are published, their sins are forgiven and they can be worked into future introductions and discussions at will. IMRAD advocates often stretch available data, gloss over uncertainties in their evidence, and ignore contrary results. This occurs throughout the hierarchy of journals. One would hope that it would be least common in prestigious journals. On the other hand, top journals have limited space; they emphasize papers with broad, seemingly decisive conclusions but passively encourage readers not to worry about methods (or rebuttals). Often, this form of advocacy is obvious only to the small percentage of any journal's readers that have scientific expertise in a specialized area—a small pool of appropriate reviewers ([1][1]). As with policy advocacy, there is a gray area between objective justification and flagrant, half-supported promotion. Somewhere in the middle sits the honest, often acrimonious debate necessary for scientific progress. Would anyone disagree that publishing overly liberal conclusions is poor science, that the personal rewards of doing so far outweigh risks, or that the peer-review process should strip papers of this garbage? Humiliation could assist rebuttals and time in the self-correcting process of science. For example, each professional society should survey members at year's end to decide on the five papers in their field with the most overly inflated claims. The authors, approving reviewers, and subject editors could receive suitable prizes. 1. 1.[↵][2]1. R. Hilborn , Fisheries 31, 554 (2006). [OpenUrl][3] [1]: #ref-1 [2]: #xref-ref-1-1 "View reference 1. in text" [3]: {openurl}?query=rft.jtitle%253DFisheries%26rft.volume%253D31%26rft.spage%253D554%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx

Ecologists often develop complex regression models that include multiple categorical and continuous variables, interactions among predictors, and nonlinear relationships between the response and predictor variables. Nomograms, which are graphical devices for presenting mathematical functions and calculating output values, can aid biologists in interpreting and presenting these complex models. To illustrate benefits of nomograms, we developed a logistic regression model of elk (Cervus elaphus) resource selection. With this model, we demonstrated how a nomogram helps scientists and managers interpret interactions among variables, compare the relative biological importance of variables, and examine predicted shapes of relationships (e.g., linear vs. nonlinear) between response and predictor variables. Although our example focused on logistic regression, nomograms are equally useful for other linear and nonlinear models. Regardless of the approach used for model development, nomograms and other graphical summaries can help scientists and managers develop, interpret, and apply statistical models.

Although bats are a group with high species richness in shrub-steppe areas of the Columbia Basin, limited information is available on distributions, species occurrence, and habitat use. We surveyed bat populations of the Hanford Site, which has provided a refuge for shrub-steppe flora and fauna in Washington State. We used Anabat detectors, harp traps, mist nets, and auditory-visual observations during 1997 and 1998 to document the occurrence of bat species and to compare activity levels among major habitat types on the site. Across both years, we recorded 6,470 bat calls during 302 bat detector survey-nights at 66 locations. Bats of the genus Myotis constituted 84 to 88% of total calls each year. The hoary bat (Lasiurus cinereus), silver-haired bat (Lasionycteris noctivagans), western pipistrelle (Pipistrellus hesperus), big brown bat (Eptesicusfuscus), and pallid bat (Antrozous pallidus) also were recorded. Big brown bats have not been reported previously at the Hanford Site. Detection rates were highest near potential roosting structures and riparian foraging areas, and lowest in upland shrub-steppe cover types. During 133 trap nights across both years, we captured 81 bats of 5 species: the western smallfooted myotis (Myotis ciliolabrum), little brown myotis (M. lucifugus), Yuma myotis (M. yumanensis), silver-haired bat, and pallid bat. The site supports at least 3 arid-land bats (western small-footed myotis, western pipistrelle, and pallid bat) that are near the northern edge of their species distributions. By protecting large patches of undisturbed habitat, the Hanford Site has helped maintain a diverse bat fauna in Washington's shrub-steppe. In addition, some habitat modifications at the site may have benefited bats, as evidenced by heavy use of old buildings, irrigation run-off wetlands, and planted trees.

We tested the validity of a spatially explicit habitat effectiveness model for Rocky Mountain elk (Cervus elaphus nelsoni). The model scored habitat effectiveness based on seasonal changes in the quality, quantity, and availability of forage. Seasonal forage potential scores were derived by integrating information on existing vegetation, site potential, historic disturbances, topography, and roads. The model generated maps of seasonal habitat effectiveness that were used to create utilization distributions (UD; i.e. 3-dimensional density estimates). We tested the elk habitat model using telemetry data collected on 5 cow elk sub-herds from 1993 to 1997 in Custer State Park (CSP), South Dakota, USA. We computed fixed kernel UD from elk telemetry data and simulated random UD within the confines of each sub-herd boundary. The degree of fit between elk UD and model predicted UD (elk-model UD) and random UD and model predicted UD (random-model UD) was represented by sub-herd, season, and year using the Volume of Intersection test statistic (V.I. Index). There were no differences in V.I. Indices by year for elk-model (1993 = 0.59, 1994 = 0.54, 1995 = 0.60, 1996 = 0.57, 1997 = 0.57; F 4,70 = 0.93, P = 0.45) or random-model (1993 = 0.59, 1994 = 0.55, 1995 = 0.59, 1996 = 0.58, 1997 = 0.59; F 4,70 = 1.49, P = 0.21) UD; thus, V.I. Indices were pooled across years. Two-way analysis of variance indicated that elk-model V.I. Indices did not differ by sub-herd (B = 0.50, Y = 0.58, A = 0.56, S = 0.58, R = 0.63; F 4,12 = 2.68, P = 0.08), season (Spring = 0.55, Summer = 0.55, Fall = 0.60, Winter = 0.58; F 3,12 = 0.80, P = 0.52), or the interaction terms (B Spring = 0.48, B Summer = 0.52, B Fall = 0.52, B Winter = 0.49, Y Spring = 0.60, Y Summer = 0.52, Y Fall = 0.58, Y Winter = 0.62, A Spring = 0.47, A Summer = 0.58, A Fall = 0.64, A Winter = 0.54, S Spring = 0.54, S Summer = 0.49, S Fall = 0.64, S Winter = 0.65, R Spring = 0.70, R Summer 0.64, R Fall = 0.60, R Winter = 0.59; F 12,55 = 1.68, P = 0.10). V.I. Indices for random-model UD did not differ by season (Spring = 0.58, Summer = 0.57, Fall = 0.58, Winter = 0.59; F 3,12 = 0.56, P = 0.65) or interaction term (B Spring = 0.55, B Summer = 0.58, B Fall = 0.56, B Winter = 0.54, Y Spring = 0.57, Y Summer = 0.53, Y Fall = 0.53, Y Winter = 0.57, A Spring = 0.61, A Summer = 0.63, A Fall = 0.63, A Winter = 0.64, S Spring = 0.60, S Summer = 0.49, S Fall = 0.57, S Winter = 0.59, R Spring = 0.59, R Summer 0.60, R Fall = 0.60, R Winter = 0.59; F 12,55 = 1.44, P = 0.17); however, differences were noted among sub-herds (B = 0.56, Y = 0.55, A = 0.63, S = 0.56, R = 0.60; F 4,12 = 4.48, P = 0.02). V.I. Indices for elk-model UD differed from random-model UD (F 4,12 = 4.71, P = 0.02); model performance was worse than random (i.e., lower V.I. Indices) for 2 sub-herds (elk-model sub-herd B = 0.50 vs. random-mode sub-herd B = 0.56 and elk-model sub-herd A = 0.56 vs. random-model sub-herd A = 0.63). Lower V.I. Indices were observed for 2 sub-herds that occupied areas recently subjected to large-scale wildfires. For sub-herds not subjected to fire effects (i.e., greater loss of vegetation security cover), the model portrayed elk habitat use less consistently, as represented by greater variability (27-42% larger standard errors) in V.I. Indices, during summer. Conversely, the model portrayed elk habitat use most consistent for the same 3 sub-herds during fall.

bia, Idaho, and Oregon (Watkins 1977; Nagorsen and Brigham 1993; Verts and Carraway 1998). Sarell and McGuinness (1993) presented the first reports of spotted bats in north-central Washington State. Subsequent surveys have noted spotted bats at additional locations, including sites in Douglas, Grant, and Okanogan counties (John Fleckenstein, Natural Heritage Program, Washington Department of Natural Resources, Olympia, WA, pers. comm.; Neal Hedges, Bureau of Land Management, Wenatchee, WA, pers. comm.). However, limited information is available about the distribution

Documenting space-use patterns of elk (Cervus elaphus) and hunters is important for determining whether disturbance by hunters affects elk movements and resource use. We compared utilization distributions of elk and hunters during 4 hunting seasons (early archery, trophy rifle, antlerless rifle, and late archery) from 1993 to 1996 in the southern Black Hills, South Dakota, using the Volume of Intersection Index statistic. Volume of Intersection Indices were used as the response variable in a general linear regression model analysis to determine if environmental features were correlated with measures of space-use sharing. Space-use sharing for cow elk and hunters was lowest during the late archery hunt and highest during the trophy rifle and early archery seasons. Space-use sharing was lowest for bull elk and hunters during the trophy rifle hunt and highest during the early archery season. Hunter density, secondary road use, and tertiary road density were negatively correlated with space-use sharing. In contrast, vegetative cover was positively correlated with space-use sharing. Subherds occupying areas dominated by overstory-killed habitat exhibited less overlap with hunters than subherds residing in more heavily forested habitats. These results suggested that control of hunter density, including limitation of road access, in areas which lack vegetative cover may help mitigate hunter disturbance of elk. Variation in elk movements and environmental features correlated with overlap measures indicated that elk response to hunters was adaptive and short-lived.

interactions with prey, bears, and humans [dissertation]. Moscow, ID: University of Idaho. 147 p. NOWAK MC. 1999. Predation rates and foraging ecology of adult female mountain lions in northeastern Oregon [thesis]. Pullman, WA: Washington State University. 75 p. Ross IP, JALKOTZY MG. 1996. Cougar predation on moose in southwestern Alberta. Alces 32:1-8. VERTS BJ, CARRAWAY LN. 1998. Land mammals of Oregon. Berkeley, CA: University of California Press. 668 p.

Kernel methods for estimating home range are being used increasingly in wildlife research, but the effect of sample size on their accuracy is not known. We used computer simulations of 10-200 points/ home range and compared accuracy of home range estimates produced by fixed and adaptive kernels with the reference (REF) and least-squares cross-validation (LSCV) methods for determining the amount of smoothing. Simulated home ranges varied from simple to complex shapes created by mixing bivariate normal distributions. We used the size of the 95% home range area and the relative mean squared error of the surface fit to assess the accuracy of the kernel home range estimates. For both measures, the bias and variance approached an asymptote at about 50 observations/home range. The fixed kernel with smoothing selected by LSCV provided the least-biased estimates of the 95% home range area. All kernel methods produced similar surface fit for most simulations, but the fixed kernel with LSCV had the lowest frequency and magnitude of very poor estimates. We reviewed 101 papers published in The Journal of Wildlife Management (JWM) between 1980 and 1997 that estimated animal home ranges. A minority of these papers used nonparametric utilization distribution (UD) estimators, and most did not adequately report sample sizes. We recommend that home range studies using kernel estimates use LSCV to determine the amount of smoothing, obtain a minimum of 30 observations per animal (but preferably >50), and report sample sizes in published results.