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We analyze a real data set pertaining to reindeer fecal pellet‐group counts obtained from a survey conducted in a forest area in northern Sweden. In the data set, over 70% of counts are zeros, and there is high spatial correlation. We use conditionally autoregressive random effects for modeling of spatial correlation in a Poisson generalized linear mixed model (GLMM), quasi‐Poisson hierarchical generalized linear model (HGLM), zero‐inflated Poisson (ZIP), and hurdle models. The quasi‐Poisson HGLM allows for both under‐ and overdispersion with excessive zeros, while the ZIP and hurdle models allow only for overdispersion. In analyzing the real data set, we see that the quasi‐Poisson HGLMs can perform better than the other commonly used models, for example, ordinary Poisson HGLMs, spatial ZIP, and spatial hurdle models, and that the underdispersed Poisson HGLMs with spatial correlation fit the reindeer data best. We develop R codes for fitting these models using a unified algorithm for the HGLMs. Spatial count response with an extremely high proportion of zeros, and underdispersion can be successfully modeled using the quasi‐Poisson HGLM with spatial random effects. In analyzing the real data set on reindeer pellet group counts, where the observed data contain over 70% of zeros and show evidence of spatial correlation, we find that the quasi‐hierarchical generalized linear models (HGLMs) can perform better than the other commonly used models, for example, ordinary Poisson HGLMs, spatial zero‐inflated Poisson, and spatial hurdle models, and that the underdispersed Poisson HGLMs with spatial correlation fit the reindeer data best. The above results lead us to conclude that the count responses with extremely high proportion of zeros, and underdispersion, can be successfully modeled using HGLM with spatial random effects.

Despite widespread use of fecal pellet-group counts as an index of ungulate density, techniques used to convert pellet-group numbers to ungulate numbers rarely are based on counts of known individuals, seldom evaluated across spatial and temporal scales, and precision is infrequently quantified. Using DNA from fecal pellets to identify individual deer, we evaluated the relationship between pellet-group count and count of Sitka black-tailed deer (Odocoileus hemionus sitkensis) during a 3-year study (2006–2008) in 3 watersheds in southeast Alaska, USA. We surveyed 141,054 m2of transect, counted 10,569 pellet groups, and identified 737 unique deer. We used a multilevel mixed-effects generalized linear model to analyze expected deer count as a function of pellet-group count. Pellet-group count was a significant predictor of DNA-based index of deer count, but that relationship varied by transect, watershed, and year, indicating that extrapolation of a single linear relationship across space and time was not possible. More importantly, most of the variation in ourmodels was residual and unexplained. Assuming that our DNA-based results were amore accurate and precise metric of true deer count, we do not support the use of pellet-group count to index deer count in southeast Alaska unless confounding factors are accounted for at fine spatial (e.g., habitat patch) scales. Because of the difficulty in routinely evaluating the influence of confounding variables in remote and unmanaged landscapes, we suggest that wildlife programs in these environments consider alternatives, such as DNA-based methods, for monitoring trends in ungulate populations.

We need to monitor wildlife populations to determine whether management goals are achieved and to improve future decisions. Therefore, it is important to evaluate the cost and accuracy of monitoring strategies in the context of management. Using a computer simulation of a harvested population, we tested the relative performance of three survey methods: aerial survey, pellet-group counts and hunters' observations, to inform about the management of Swedish moose Alces alces populations. Where more than one survey method was used in a single year, we used Bayes' theorem to combine information and estimate population size. We used two measures of performance: the fraction of time in which the population had an ‘undesirable’ size and inter-annual variation in harvest. Furthermore, we traded these performance measures against their cost. An annual aerial survey was the most costly monitoring method (27,000€) and maintained the population within the desired range 72% of the time. The least expensive monitoring strategy (hunters' observations; 1,600€) maintained the population within a desired range of 66% of the time. A combination of two relatively inexpensive survey methods (i.e. pellet-group counts and hunters' observations; at an expense of 10,000€) maintained the population within the desired range in 76% of the simulated years. Thus, a combination of annual pellet-group counts and hunters' observations performed better than annual aerial surveys, but was considerably less expensive. Furthermore, the annual combination of pellet-group counts and hunters' observations also performed best regarding the inter-annual harvest variation. Management actions only maintained the population within the desired range 81% of the time, even when population size was observed without error, mainly due to variable net growth rates. In wildlife management systems, where a variety of monitoring methods are used, the overall performance generally improves with monitoring expenditure, but very few studies explicitly account for expenditure. However, our study shows that combinations of inexpensive methods can reduce monitoring costs substantially while yielding an equal or an increased performance.

The functional response of a predator describes the change in per capita kill rate to changes in prey density. This response can be influenced by predator densities, giving a predator‐dependent functional response. In social carnivores which defend a territory, kill rates also depend on the individual energetic requirements of group members and their contribution to the kill rate. This study aims to provide empirical data for the functional response of wolves Canis lupus to the highly managed moose Alces alces population in Scandinavia. We explored prey and predator dependence, and how the functional response relates to the energetic requirements of wolf packs. Winter kill rates of GPS‐collared wolves and densities of cervids were estimated for a total of 22 study periods in 15 wolf territories. The adult wolves were identified as the individuals responsible for providing kills to the wolf pack, while pups could be described as inept hunters. The predator‐dependent, asymptotic functional response models (i.e. Hassell–Varley type II and Crowley–Martin) performed best among a set of 23 competing linear, asymptotic and sigmoid models. Small wolf packs acquired >3 times as much moose biomass as required to sustain their field metabolic rate (FMR), even at relatively low moose abundances. Large packs (6–9 wolves) acquired less biomass than required in territories with low moose abundance. We suggest the surplus killing by small packs is a result of an optimal foraging strategy to consume only the most nutritious parts of easy accessible prey while avoiding the risk of being detected by humans. Food limitation may have a stabilizing effect on pack size in wolves, as supported by the observed negative relationship between body weight of pups and pack size.

With the recent recovery of large carnivores in Europe, preventive measures to protect livestock are on the rise. Fences that exclude carnivores from grazing areas have been proven as effective, but they can be costly as well as posing a barrier for wildlife. We studied the effect of exclosures of > 10 km2 to protect sheep Ovis aries on the distribution and density of moose Alces alces using fecal pellet group counts in two study areas in southeastern Norway. During the summer grazing season, the fences were powered. Outside of the grazing season, one exclosure remained fenced while the other fence was demounted. This quasi‐experimental setting allowed us to investigate whether fences had a barrier effect for moose, and/or whether moose density was affected by interactions with sheep (competition or facilitation) or large carnivores (refuge hypothesis). During winter, moose pellet group density was about equal inside and outside of the exclosure with demounted wire strands, but less than half inside the permanently fenced exclosure compared to outside, indicating a potential fragmentation effect of the fence. During the grazing season, when wire strands were powered, moose pellet group density was equal or doubled inside as compared to outside both exclosures. Moose may have sought refuge from large carnivores inside the fences. Fecal pellet group densities of moose and sheep inside the fence were neither positively (facilitation) nor negatively (competition) correlated. However, moose used young forest, the most used habitat type by sheep, to a lesser extent inside than outside of the exclosures, maybe due to interference competition. Our study demonstrates that livestock protection fences can have an impact on more than the targeted wildlife species. To understand the mechanisms behind direct and indirect effects of fences, monitoring the movement and survival of individuals by means of GPS and camera traps would be needed.

Effective wildlife management often relies on estimates of animal density, and cue counting is a viable estimation strategy. A key component of density estimation from dung, a form of cue counting, is estimation of the persistence time, t^, of dung piles. However, differences between observers on what constitutes a dung pile may alter subsequent density estimates. Additionally, many researchers studying white-tailed deer (Odocoileus virginianus) have substituted for t^ the number of days between the date in which 98% of deciduous trees shed leaves in autumn and field sampling. To address these 2 concerns, we compared 3 methods for estimating t^ of whitetailed deer pellet groups: (1) a common modelling approach based on observations from a single observer (single-observer method), (2) a method that accommodates interobserver variation on the status of dung during field surveys (interobserver method), and (3) the days elapsed since 98% of deciduous trees shed autumn leaves (leaf-off method). We then applied these 3 t^ estimates to distance-sampling data on pellet groups from white-tailed deer that we collected along transects during 3 sampling seasons from 2019–2021 in west-central Indiana. We estimated habitat- and year-specific deer densities. Persistence probability of pellet groups varied across habitats and years, positively with age and number of pellets, and negatively with precipitation and temperature. In several instances, we found strong or marginal differences between densities estimated using the leaf-off method and the other methods. The densities using the interobserver and singleobserver methods were similar, with the latter being larger by an average of 8.0% (SE = 1.71). The latter also yielded coefficients of variation (CV) that averaged 16.6% (SE = 4.8) larger, attributable to interobserver discrepancies in scoring dung persistence. Density estimates from the leaf-off method were 32.6% (SE = 15.3) and 37.8% (SE = 13.0) less than the density estimates from the interobserver and single-observer methods, respectively. We encourage future researchers estimating density using multiple observers and dung sampling techniques to incorporate interobserver variation. We advocate that biologists relying on dung-based estimation of density for white-tailed deer abandon the conventional leafoff method and adopt other modelling approaches.

We studied the impact of proximity to human concentrations, hikers, and field vehicles on mountain gazelles (Gazella gazella gazella) space-use patterns, flight distance, and visibility in the southern coastal plain of Israel. We collected data on gazelle behavior and human disturbance from fixed observation sites, drive counts, and pellet counts. The density of pellets was positively correlated with the distance to human concentrations, and the flight distance was positively correlated with human disturbance level, suggesting mountain gazelle space use and flight distance were affected by human disturbance. Gazelles were less visible in the more disturbed areas. Our findings provide a framework for conservation measures such as determining the size of buffer zones and where and when enforcement efforts should take place to keep mountain gazelle populations viable in spite of the ecological impacts of human encroachment on mountain gazelle habitat.

Deer population control is important in wildlife management, because overabundance of deer is a problem worldwide. For practical deer population control, deer population dynamics and the factors that influence them need to be evaluated in low-cost and time-efficient ways. However, in traditional methods of estimation, such as cohort analysis, large numbers of deer need to be caught for many years, and the ages of the deer must be determined. We estimated deer population dynamics by using a Bayesian state-space model with multiple deer abundance indices (seen deer per unit effort, pellet group count, and block count) and numbers of deer hunted and culled in Yamanashi Prefecture, central Japan. In the state process of our state-space model, latent deer abundance at year t in location m (Dt,m), with m being each cell of a grid mesh covering Yamanashi Prefecture, was assumed to decrease annually through hunting and culling, to increase with the population growth rate of each mesh (rm; which was determined from the percentages of forest, evergreen forest, and artificial grassland), and to fluctuate stochastically. In the observation process, Dt,m m was assumed to be correlated with the deer abundance indices and a Gaussian white noise in the deer abundance indices. The estimated Dt,m was correlated with each deer abundance index, but the correlation coefficient was the greatest for pellet group density. The percentage of hunted and culled deer needed to reach 30% to reduce the annual growth rate (Dt,m/Dt-1,m). Increasing the percentage of artificial grassland increased rm. Our results showed that 1) deer abundance could be estimated by using only deer abundance indices in addition to population growth rate and the percentage of hunted and culled deer; and 2) preventing the intrusion of deer onto artificial grassland and intensive culling on artificial grassland were important to decrease deer abundance.

Worldwide there is a rush toward wind power development and its associated infrastructure. In Fennoscandia, large‐scale wind farms comprising several hundred windmills are currently built in important grazing ranges used for Sámi reindeer husbandry. In this study, reindeer habitat use was assessed using reindeer fecal pellet group counts in relation to two relatively small wind farms, with 8 and 10 turbines, respectively. In 2009, 1,315 15‐m2 plots were established and pellet groups were counted and cleaned from the plots. This was repeated once a year in May, during preconstruction, construction, and operation of the wind farms, covering 6 years (2009–2014) of reindeer habitat use in the area. We modeled the presence/absence of any pellets in a plot at both the local (wind farm site) and regional (reindeer calving to autumn range) scale with a hierarchical logistic regression, where spatial correlation was accounted for via random effects, using vegetation type, and the interaction between distance to wind turbine and time period as predictor variables. Our results revealed an absolute reduction in pellet groups by 66% and 86% around each wind farm, respectively, at local scale and by 61% at regional scale during the operation phase compared to the preconstruction phase. At the regional, scale habitat use declined close to the turbines in the same comparison. However, at the local scale, we observed increased habitat use close to the wind turbines at one of the wind farms during the operation phase. This may be explained by continued use of an important migration route close to the wind farm. The reduced use at the regional scale nevertheless suggests that there may be an overall avoidance of both wind farms during operation, but further studies of reindeer movement and behavior are needed to gain a better understanding of the mechanisms behind this suggested avoidance. In this study, reindeer habitat use was assessed using reindeer faecal pellet group counts in relation to two relatively small wind farms (eight and 10 turbines, respectively) within a calving and postcalving range. Our results revealed an absolute decline of pellet groups at both local (by 66% and 86%) and regional scale (by 61%), and decreased habitat use close to the wind turbines at the regional scale, but an increased use at the local scale during the operation phase compared to the preconstruction phase. The reduced use at the regional scale nevertheless suggests that there may be an overall avoidance of both wind farms during operation, but further studies of reindeer movement and behavior are needed to gain a better understanding of the mechanisms behind this suggested avoidance.

Although fecal pellet counts have been widely used to index changes in deer abundance in forests, few studies have modeled the relationship between the indices and deer density. We examined the relationships between 3 fecal pellet indices (total pellets, pellet groups, and pellet frequency) and the density of deer (primarily red deer [Cervus elaphus scoticus]) in 20 enclosures in the North and South islands of New Zealand. In each enclosure we estimated the 3 indices on 30 randomly located 150-m transects, with each transect having 30 circular plots of 3.14 m2. We developed 4 candidate models (1 linear and 3 nonlinear) to describe the relationship between the indices and deer density. We used a Bayesian analysis to account for uncertainty in the estimates of deer abundance and to facilitate fitting models that included random transect effects. The 4 models explained the relationship between the 3 indices and deer density similarly well. The slopes of the linear relationships between the 3 indices and deer density were positive. Our results suggest that fecal pellet counts may be useful indices of deer abundance.