Variations in age‐ and sex‐specific survival rates help explain population trend in a discrete marine mammal population

Understanding the drivers underlying fluctuations in the size of animal populations is central to ecology, conservation biology, and wildlife management. Reliable estimates of survival probabilities are key to population viability assessments, and patterns of variation in survival can help inferring the causal factors behind detected changes in population size. We investigated whether variation in age‐ and sex‐specific survival probabilities could help explain the increasing trend in population size detected in a small, discrete population of bottlenose dolphins Tursiops truncatus off the east coast of Scotland. To estimate annual survival probabilities, we applied capture–recapture models to photoidentification data collected from 1989 to 2015. We used robust design models accounting for temporary emigration to estimate juvenile and adult survival, multistate models to estimate sex‐specific survival, and age models to estimate calf survival. We found strong support for an increase in juvenile/adult annual survival from 93.1% to 96.0% over the study period, most likely caused by a change in juvenile survival. Examination of sex‐specific variation showed weaker support for this trend being a result of increasing female survival, which was overall higher than for males and animals of unknown sex. Calf survival was lower in the first than second year; a bias in estimating third‐year survival will likely exist in similar studies. There was some support first‐born calf survival being lower than for calves born subsequently. Coastal marine mammal populations are subject to the impacts of environmental change, increasing anthropogenic disturbance and the effects of management measures. Survival estimates are essential to improve our understanding of population dynamics and help predict how future pressures may impact populations, but obtaining robust information on the life history of long‐lived species is challenging. Our study illustrates how knowledge of survival can be increased by applying a robust analytical framework to photoidentification data.     

Multiscale patterns of movement in fragmented landscapes and consequences on demography of the snail kite in Florida

1. Habitat loss and fragmentation are major factors affecting vertebrate populations. A major effect of these habitat alterations is that they reduce movement of organisms. Despite the accepted importance of movement in driving the dynamics of many natural populations, movement of vertebrates in fragmented landscapes have seldom been estimated with robust statistical methods. 2. We estimated movement probabilities of snail kites Rosthramus sociabilis within the remaining wetlands in Florida. Using both radio-telemetry and banding information, we used a multistate modelling approach to estimate transition probabilities at two temporal scales (month; year) and multiple spatial scales. We examined kite movement among wetlands altered by three different levels of fragmentation: among wetlands separated by small physical barriers (e.g. road); among wetlands separated by moderate amount of matrix (< 5 km); and among wetlands separated by extensive matrix areas (> 15 km). 3. Kites moved extensively among contiguous wetlands (movement probability 0.29 per month), but significantly less among isolated wetlands (movement probability 0.10 per month). 4. Kites showed high levels of annual site fidelity to most isolated wetlands (probability ranged from 0.72 to 0.95 per year). 5. We tested the effects of patch size and interpatch distance on movement. Our modelling indicated an effect of both distance and patch size on juveniles' movement (but not adult) when examining movements among fragments. 6. Only a small proportion of kites escaped a regional drought by moving to refugia (wetlands less affected by drought). Many individuals died after the drought. During drought adult survival dropped by 16% while juvenile survival dropped by 86% (possibly because juveniles were less likely to reach refugia). 7. We hypothesize that fragmentation may decrease kite's resistance to drought by restricting exploratory behaviour.     

Modeling the Western Arctic Caribou Herd During a Positive Growth Phase: Potential Effects of Wolves and Radiocollars

ABSTRACT Population modeling exercises can lead to both expected and unexpected results useful for wildlife research and management, even though inferences must often be qualitative, given underlying assumptions. Our main objective was to use empirical data on wolf (Canis lupus) kill rates and growth of the Western Arctic caribou (Rangifer tarandus) herd (WAH) of Alaska, USA, to assess the potential for predator regulation. We used available data and published literature to construct a deterministic density-dependent population model fitted to trends of the WAH from 1976 through 2003. By increasing wolf densities in the baseline model, we failed to reject the hypothesis that wolves at a density of 6.5 wolves per 1,000 km² could regulate a caribou herd at a density of 0.4 caribou per km². In addition, our model may be conservative by underestimating the regulatory potential of wolves. We suggest that this relatively simple predator-preysystem shows signs of a predation—food 2-state model. Elasticities from matrix models may be deceiving. Although herd growth is most sensitive to changes in adult female survival, survival of younger cohorts may be more easily influenced by natural conditions or management action. Management of the WAH near maximum sustained yield may not be attainable if desired, but modeling exercises such as this elucidate options. In conducting this research, we also discovered by Monte Carlo simulation that survival and productivity data from radiocollared females and calves were negatively biased and failed to predict herd growth. Thus, researchers should consider potential effects of neck collars on vital rates of female tundra caribou and concomitant offspring when using sample data to model population dynamics or test hypotheses.     

The demographic impact of extreme events: stochastic weather drives survival and population dynamics in a long-lived seabird

1. Most scenarios for future climate change predict increased variability and thus increased frequency of extreme weather events. To predict impacts of climate change on wild populations, we need to understand whether this translates into increased variability in demographic parameters, which would lead to reduced population growth rates even without a change in mean parameter values. This requires robust estimates of temporal process variance, for example in survival, and identification of weather covariates linked to interannual variability. 2. The European shag Phalacrocorax aristotelis (L.) shows unusually large variability in population size, and large-scale mortality events have been linked to winter gales. We estimated first-year, second-year and adult survival based on 43 years of ringing and dead recovery data from the Isle of May, Scotland, using recent methods to quantify temporal process variance and identify aspects of winter weather linked to survival. 3. Survival was highly variable for all age groups, and for second-year and adult birds process variance declined strongly when the most extreme year was excluded. Survival in these age groups was low in winters with strong onshore winds and high rainfall. Variation in first-year survival was not related to winter weather, and process variance, although high, was less affected by extreme years. A stochastic population model showed that increasing process variance in survival would lead to reduced population growth rate and increasing probability of extinction. 4. As in other cormorants, shag plumage is only partially waterproof, presumably an adaptation to highly efficient underwater foraging. We speculate that this adaptation may make individuals vulnerable to rough winter weather, leading to boom-and-bust dynamics, where rapid population growth under favourable conditions allows recovery from periodic large-scale weather-related mortality. 5. Given that extreme weather events are predicted to become more frequent, species such as shags that are vulnerable to such events are likely to exhibit stronger reductions in population growth than would be expected from changes in mean climate. Vulnerability to extreme events thus needs to be accounted for when predicting the ecological impacts of climate change.     

Upper bound estimators of the population size based on ordinal models for capture‐recapture experiments

Capture‐recapture studies have attracted a lot of attention over the past few decades, especially in applied disciplines where a direct estimate for the size of a population of interest is not available. Epidemiology, ecology, public health, and biodiversity are just a few examples. The estimation of the number of unseen units has been a challenge for theoretical statisticians, and considerable progress has been made in providing lower bound estimators for the population size. In fact, it is well known that consistent estimators for this cannot be provided in the very general case. Considering a case where capture‐recapture studies are summarized by a frequency of frequencies distribution, we derive a simple upper bound of the population size based on the cumulative distribution function. We introduce two estimators of this bound, without any specific parametric assumption on the distribution of the observed frequency counts. The behavior of the proposed estimators is investigated using several benchmark datasets and a large‐scale simulation experiment based on the scheme discussed by Pledger.     

Wolf monitoring in Scandinavia: evaluating counts of packs and reproduction events

Large carnivores are elusive and use large areas, which causes monitoring to be challenging and costly. Moreover, management to reduce conflicts and simultaneously ensure long-term population viability require precise population estimates. In Scandinavia, the monitoring of wolves (Canis lupus) is primarily based on counting packs, identifying reproduction, and genetically identifying territorial wolves from noninvasive DNA samples. We assessed the reliability of wolf monitoring in Scandinavia by estimating the detectability of territorial pairs, packs, and reproduction. Our data, comprising snow-tracking data and DNA-identified individuals from 2005–2016, covered 11 consecutive winter monitoring seasons (Oct–Mar). Among 343 cases where we identified a wolf pack, territorial wolves were also detected in the same area during the previous season in 323 (94.2%) cases. In only 6 of the remaining 20 cases, there was no prior knowledge of territorial wolves in the area. Among the 328 detected reproduction events (litter born to a pack), we detected 97% during the monitoring period and identified the rest ≥1 year later from kinship assessments of all DNA-detected individuals. These results suggest that we failed to detect only few packs with reproduction events during the monitoring season that followed breeding. Yearly monitoring of territorial individuals and continuous updates of the pedigree allowed us to retrospectively identify reproduction events and packs that were not identified earlier.     

Spatiotemporal relationship between adult census size and genetic population size across a wide population size gradient

Adult census population size (N) and effective number of breeders (N_b) are highly relevant for designing effective conservation strategies. Both parameters are often challenging to quantify, however, making it of interest to determine whether one parameter can be generalized from the other. Yet, the spatiotemporal relationship between N and N_b has not been well characterized empirically in many taxa. We analysed this relationship for 5–7 consecutive years in twelve brook trout populations varying greatly in N (49‐10032) and N_b (3‐567) and identified major environmental variables affecting the two parameters. N or habitat size alone explained 47–57% of the variance in N_b, and N_b was strongly correlated with effective population size. The ratio N_b/N ranged from 0.01 to 0.45 and increased at small N or following an annual decrease in N, suggesting density‐dependent constraints on N_b. We found no evidence for a consistent, directional difference between variability in N_b and/or N_b/N among small and large populations; however, small populations had more varying temporal variability in N_b/N ratios than large populations. Finally, N_b and N_b/N were 2.5‐ and 2.3‐fold more variable among populations than temporally within populations. Our results demonstrate a clear linkage between demographic and evolutionary parameters, suggesting that N_b could be used to approximate N (or vice versa) in natural populations. Nevertheless, using one variable to infer the other to monitor trends within populations is less recommended, perhaps even less so in small populations given their less predictable N_b vs. N dynamics.     

Estimability of migration survival rates from integrated breeding and winter capture–recapture data

Long‐distance migration is a common phenomenon across the animal kingdom but the scale of annual migratory movements has made it difficult for researchers to estimate survival rates during these periods of the annual cycle. Estimating migration survival is particularly challenging for small‐bodied species that cannot carry satellite tags, a group that includes the vast majority of migratory species. When capture–recapture data are available for linked breeding and non‐breeding populations, estimation of overall migration survival is possible but current methods do not allow separate estimation of spring and autumn survival rates. Recent development of a Bayesian integrated survival model has provided a method to separately estimate the latent spring and autumn survival rates using capture–recapture data, though the accuracy and precision of these estimates has not been formally tested. Here, I used simulated data to explore the estimability of migration survival rates using this model. Under a variety of biologically realistic scenarios, I demonstrate that spring and autumn migration survival can be estimated from the integrated survival model, though estimates are biased toward the overall migration survival probability. The direction and magnitude of this bias are influenced by the relative difference in spring and autumn survival rates as well as the degree of annual variation in these rates. The inclusion of covariates can improve the model's performance, especially when annual variation in migration survival rates is low. Migration survival rates can be estimated from relatively short time series (4–5 years), but bias and precision of estimates are improved when longer time series (10–12 years) are available. The ability to estimate seasonal survival rates of small, migratory organisms opens the door to advancing our understanding of the ecology and conservation of these species. Application of this method will enable researchers to better understand when mortality occurs across the annual cycle and how the migratory periods contribute to population dynamics. Integrating summer and winter capture data requires knowledge of the migratory connectivity of sampled populations and therefore efforts to simultaneously collect both survival and tracking data should be a high priority, especially for species of conservation concern.     

Wolf population dynamics in the U.S. Northern Rocky Mountains are affected by recruitment and human-caused mortality

Reliable analyses can help wildlife managers make good decisions, which are particularly critical for controversial decisions such as wolf (Canis lupus) harvest. Creel and Rotella (2010) recently predicted substantial population declines in Montana wolf populations due to harvest, in contrast to predictions made by Montana Fish, Wildlife and Parks (MFWP). We replicated their analyses considering only those years in which field monitoring was consistent, and we considered the effect of annual variation in recruitment on wolf population growth. Rather than assuming constant rates, we used model selection methods to evaluate and incorporate models of factors driving recruitment and human-caused mortality rates in wolf populations in the Northern Rocky Mountains. Using data from 27 area-years of intensive wolf monitoring, we show that variation in both recruitment and human-caused mortality affect annual wolf population growth rates and that human-caused mortality rates have increased with the sizes of wolf populations. We document that recruitment rates have decreased over time, and we speculate that rates have decreased with increasing population sizes and/or that the ability of current field resources to document recruitment rates has recently become less successful as the number of wolves in the region has increased. Estimates of positive wolf population growth in Montana from our top models are consistent with field observations and estimates previously made by MFWP for 2008–2010, whereas the predictions for declining wolf populations of Creel and Rotella (2010) are not. Familiarity with limitations of raw data, obtained first-hand or through consultation with scientists who collected the data, helps generate more reliable inferences and conclusions in analyses of publicly available datasets. Additionally, development of efficient monitoring methods for wolves is a pressing need, so that analyses such as ours will be possible in future years when fewer resources will be available for monitoring. © 2011 The Wildlife Society.