Vincenzo Gervasi

Predicting the potential demographic impact of predators on their prey: a comparative analysis of two carnivore–ungulate systems in Scandinavia

1. Understanding the role of predation in shaping the dynamics of animal communities is a fundamental issue in ecological research. Nevertheless, the complex nature of predator–prey interactions often prevents researchers from modelling them explicitly. 2. By using periodic Leslie–Usher matrices and a simulation approach together with parameters obtained from long-term field projects, we reconstructed the underlying mechanisms of predator–prey demographic interactions and compared the dynamics of the roe deer–red fox–Eurasian lynx–human harvest system with those of the moose–brown bear–gray wolf–human harvest system in the boreal forest ecosystem of the southern Scandinavian Peninsula. 3. The functional relationship of both roe deer and moose λ to changes in predation rates from the four predators was remarkably different. Lynx had the strongest impact among the four predators, whereas predation rates by wolves, red foxes, or brown bears generated minor variations in prey population λ. Elasticity values of lynx, wolf, fox and bear predation rates were −0·157, −0·056, −0·031 and −0·006, respectively, but varied with both predator and prey densities. 4. Differences in predation impact were only partially related to differences in kill or predation rates, but were rather a result of different distribution of predation events among prey age classes. Therefore, the age composition of killed individuals emerged as the main underlying factor determining the overall per capita impact of predation. 5. Our results confirm the complex nature of predator–prey interactions in large terrestrial mammals, by showing that different carnivores preying on the same prey species can exert a dramatically different demographic impact, even in the same ecological context, as a direct consequence of their predation patterns. Similar applications of this analytical framework in other geographical and ecological contexts are needed, but a more general evaluation of the subject is also required, aimed to assess, on a broader systematic and ecological range, what specific traits of a carnivore are most related to its potential impact on prey species.

An Individual-Based Method to Measure Animal Activity Levels: A Test on Brown Bears

Abstract Measuring activity levels in animals provides important information about their behavioral ecology and may be a relevant factor in management and conservation. We tested an individual-based method to discriminate active and passive behaviors on brown bears (Ursus arctos), using a dual-axis motion sensor mounted on Global Positioning System–Global System for Mobile Communications (GPS–GSM) collars. The method takes into account individual variation in activity levels and does not require further calibration. We validated the method through direct observations of captive bears and an extensive survey of wild bear signs in the boreal forest of central Sweden. We found good correspondence between sensor-measured and observed activity on captive bears. Analysis of wild bear signs at GPS locations and its comparison with the collar-based activity status confirmed the possibility of successfully applying the method to study brown bear activity patterns in the wild. The method provided 94.3% correct activity classification on captive bears and about 78.2% on wild bears. We tested the possibility of using this technique to measure increasing levels of activity by analyzing the correlation between the collar-derived numeric activity and the intensity of bear movement. At a broader scale (active vs. passive), the sensor-measured value provided information on the degree of activity, but no correlation was evident at a finer scale (specific behaviors). We suggest that using more sensors in different regions of a bears body could overcome this difficulty and improve our knowledge of animal behavior in the wild, through remote monitoring of activity levels. We conclude that this method can be useful in the study of behavioral ecology of a wide range of animals, especially species that are difficult to observe or move great distances.

Decomposing risk: Landscape structure and wolf behavior generate different predation patterns in two sympatric ungulates

Recolonizing carnivores can have a large impact on the status of wild ungulates, which have often modified their behavior in the absence of predation. Therefore, understanding the dynamics of reestablished predator-prey systems is crucial to predict their potential ecosystem effects. We decomposed the spatial structure of predation by recolonizing wolves (Canis lupus) on two sympatric ungulates, moose (Alces alces) and roe deer (Capreolus capreolus), in Scandinavia during a 10-year study. We monitored 18 wolves with GPS collars, distributed over 12 territories, and collected records from predation events. By using conditional logistic regression, we assessed the contributions of three main factors, the utilization patterns of each wolf territory, the spatial distribution of both prey species, and fine-scale landscape structure, in determining the spatial structure of moose and roe deer predation risk. The reestablished predator-prey system showed a remarkable spatial variation in kill occurrence at the intra-territorial level, with kill probabilities varying by several orders of magnitude inside the same territory. Variation in predation risk was evident also when a spatially homogeneous probability for a wolf to encounter a prey was simulated. Even inside the same territory, with the same landscape structure, and when exposed to predation by the same wolves, the two prey species experienced an opposite spatial distribution of predation risk. In particular, increased predation risk for moose was associated with open areas, especially clearcuts and young forest stands, whereas risk was lowered for roe deer in the same habitat types. Thus, fine-scale landscape structure can generate contrasting predation risk patterns in sympatric ungulates, so that they can experience large differences in the spatial distribution of risk and refuge areas when exposed to predation by a recolonizing predator. Territories with an earlier recolonization were not associated with a lower hunting success for wolves. Such constant efficiency in wolf predation during the recolonization process is in line with previous findings about the naive nature of Scandinavian moose to wolf predation. This, together with the human-dominated nature of the Scandinavian ecosystem, seems to limit the possibility for wolves to have large ecosystem effects and to establish a behaviorally mediated trophic cascade in Scandinavia.

A Preliminary Estimate of The Apennine Brown Bear Population Size Based on Hair-Snag Sampling and Multiple Data Source Mark–Recapture Huggins Models

Abstract Although the brown bear (Ursus arctos) population in Abruzzo (central Apennines, Italy) suffered high mortality during the past 30 years and is potentially at high risk of extinction, no formal estimate of its abundance has been attempted. In 2004, the Italian Forest Service and Abruzzo National Park applied DNA-based techniques to hair-snag samples from the Apennine bear population. Even though sampling and theoretical limitations prevented estimating population size from being the objective of these first applications, we extracted the most we could out of the 2004 data to produce the first estimate of population size. To overcome the limitations of the sampling strategies (systematic grid, opportunistic sampling at buckthorn [Rhamnus alpina] patches, incidental sampling during other field activities), we used a multiple data-source approach and Huggins closed models implemented in program MARK. To account for model uncertainty, we averaged plausible models using Akaike weights and estimated an unconditional population size of 43 bears (95% CI  =  35–67). We urge caution in interpreting these results because other expected but undefined sources of heterogeneity (i.e., gender) may have biased this estimate. The low capture probability obtained through the systematic grid prevented the use of this sampling technique as a stand-alone tool to estimate the Apennine bear population size. Therefore, further applications in this direction will require a substantial improvement of field procedures, the use of a multiple data-source approach, or both. In this perspective, we used Monte Carlo simulations to compare the relative performance of the 3 sampling approaches and discuss their feasibility to overcome the problem of small and sparse DNA data that often prevent reliable capture–mark–recapture applications in small bear populations.

The risks of learning: confounding detection and demographic trend when using count-based indices for population monitoring.

Theory recognizes that a treatment of the detection process is required to avoid producing biased estimates of population rate of change. Still, one of three monitoring programmes on animal or plant populations is focused on simply counting individuals or other fixed visible structures, such as natal dens, nests, tree cavities. This type of monitoring design poses concerns about the possibility to respect the assumption of constant detection, as the information acquired in a given year about the spatial distribution of reproductive sites can provide a higher chance to detect the species in subsequent years. We developed an individual-based simulation model, which evaluates how the accumulation of knowledge about the spatial distribution of a population process can affect the accuracy of population growth rate estimates, when using simple count-based indices. Then, we assessed the relative importance of each parameter in affecting monitoring performance. We also present the case of wolverines (Gulo gulo) in southern Scandinavia as an example of a monitoring system with an intrinsic tendency to accumulate knowledge and increase detectability. When the occupation of a nest or den is temporally autocorrelated, the monitoring system is prone to increase its knowledge with time. This happens also when there is no intensification in monitoring effort and no change in the monitoring conditions. Such accumulated knowledge is likely to increase detection probability with time and can produce severe bias in the estimation of the rate and direction of population change over time. We recommend that a systematic sampling of the population process under study and an explicit treatment of the underlying detection process should be implemented whenever economic and logistical constraints permit, as failure to include detection probability in the estimation of population growth rate can lead to serious bias and severe consequences for management and conservation.

Sharing data improves monitoring of trans-boundary populations: the case of wolverines in central Scandinavia

Populations of wide-ranging species are likely to extend across multiple jurisdictions, including national and international borders. This requires that local institutions implement data sharing and a standardization of monitoring designs. However, a formal evaluation of the benefits of integrated monitoring systems had not, of yet, been performed. Using the wolverines in central Scandinavia as a study case, we assessed the benefits of data sharing for the monitoring of trans-boundary populations. We also assessed the performance of two demographic monitoring systems, one relying on a count of reproductive units, the other resulting from non-invasive genetic sampling and capture-recapture modeling. Sharing data across the border between Norway and Sweden allowed a strong increase in the precision of population size, population growth rate and vital rates estimates. It also allowed revealing that the probability to emigrate from Sweden to Norway was significantly higher than in the opposite direction, a required condition for the existence of a source—sink dynamics. These findings would have been impossible without trans-boundary data sharing. While the den count monitoring provided an estimated population growth of 138% over the 12-year period, the DNA-based estimate was only 72%. A positive trend likely occurred in the detectability of wolverine dens during the first years of the study, and the index was not able to separate the actual demographic trend from the trend in the systems ability to detect reproductions, thus providing positively biased estimates of population growth rate during the initial phase of the study. Data sharing is a crucial need for the study of the processes occurring in trans-boundary populations. It should be enhanced wherever trans-boundary ecological processes occur. Also, managers should be aware that count-based monitoring has a risk of overestimating population growth during the first years after its implementation.

Assessment of key reproductive traits in the Apennine brown bear population

Abstract Although knowledge of reproductive parameters is critical to project the probability of persistence of small and endangered populations, no such data are available for the relict Apennine brown bear (Ursus arctos marsicanus) population (central Italy). From 2005 through 2014, we compiled re-sight data on marked adult female bears (3 ≤ n ≤ 10/yr, for 78 total bear-years) and unmarked, distinct family groups (n = 17) to estimate basic reproductive traits in Apennine bears. We had a high rate of radiocollar failure, so we included in our sample marked, adult female bears with non-functioning radiocollars and used multi-event models in a capture–recapture, robust-design framework to correct for their incomplete detection and potential classification error. We obtained annual detection probabilities of 0.77 and 0.82 for reproductive and non-reproductive female bears, respectively, and the classification error of their reproductive state was negligible (P = 0.003). Mean litter size was 1.9 (±0.7 SD) cubs, weaning occurred at approximately 1.4 years, and the interbirth interval was 3.7 years. Based on our multi-event model, female bears had highest probability to reproduce 3–4 years after their last reproduction, and their average reproductive rate was 0.243 (95% CI = 0.072–0.594). Average survival of adult female bears was 0.93 (95% CI = 0.83–0.97) whereas apparent cub survival was 0.49, based on the proportion of cubs seen before weaning the year following birth. Our findings place reproductive parameters of the Apennine bear population at the lower bound along the spectrum reported for other non-hunted brown bear populations. Coupled with high levels of human-caused mortality, a relatively low reproductive performance may explain why Apennine bears have not expanded their range beyond their historical minimum. More in-depth demographic investigations are urgently needed to corroborate our results and to assess the relative role of density-dependence versus inbreeding depression in affecting the dynamics of this imperiled bear population.

Estimating survival in the Apennine brown bear accounting for uncertainty in age classification

For most rare and elusive species, estimating age-specific survival is a challenging task, although it is an important requirement to understand the drivers of population dynamics, and to inform conservation actions. Apennine brown bears Ursus arctos marsicanus are a small, isolated population under a severe risk of extinction, for which the main demographic mechanisms underlying population dynamics are still unknown, and population trends have not been formally assessed. We present a 12-year analysis of their survival rates using non-invasive genetic sampling data collected through four different sampling techniques. By using multi-event capture–recapture models, we estimated survival probabilities for two broadly defined age classes (cubs and older individuals), even though the age of the majority of sampled bears was unknown. We also applied the Pradel model to provide a preliminary assessment of population trend during the study period. Survival was different between cubs [ϕ = 0.51, 95% CI (0.22, 0.79)], adult males [ϕ = 0.85, 95% CI (0.76, 0.91)] and adult females [ϕ = 0.92, 95% CI (0.87, 0.95)], no temporal variation in survival emerged, suggesting that bear survival remained substantially stable throughout the study period. The Pradel analysis of population trend yielded an estimate of λ = 1.009 [SE = 0.018; 95% CI (0.974, 1.046)]. Our results indicate that, despite the status of full legal protection, the basically stable demography of this relict population is compatible with the observed lack of range expansion, and that a relatively high cub mortality could be among the main factors depressing recruitment and hence population growth.