Movement patterns and study area boundaries: influences on survival estimation in capture–mark–recapture studies

The inability to account for the availability of individuals in the study area during capture–mark–recapture (CMR) studies and the resultant confounding of parameter estimates can make correct interpretation of CMR model parameter estimates difficult. Although important advances based on the Cormack–Jolly–Seber (CJS) model have resulted in estimators of true survival that work by unconfounding either death or recapture probability from availability for capture in the study area, these methods rely on the researcher's ability to select a method that is correctly matched to emigration patterns in the population. If incorrect assumptions regarding site fidelity (non‐movement) are made, it may be difficult or impossible as well as costly to change the study design once the incorrect assumption is discovered. Subtleties in characteristics of movement (e.g. life history‐dependent emigration, nomads vs territory holders) can lead to mixtures in the probability of being available for capture among members of the same population. The result of these mixtures may be only a partial unconfounding of emigration from other CMR model parameters. Biologically‐based differences in individual movement can combine with constraints on study design to further complicate the problem. Because of the intricacies of movement and its interaction with other parameters in CMR models, quantification of and solutions to these problems are needed. Based on our work with stream‐dwelling populations of Atlantic salmon Salmo salar, we used a simulation approach to evaluate existing CMR models under various mixtures of movement probabilities. The Barker joint data model provided unbiased estimates of true survival under all conditions tested. The CJS and robust design models provided similarly unbiased estimates of true survival but only when emigration information could be incorporated directly into individual encounter histories. For the robust design model, Markovian emigration (future availability for capture depends on an individual's current location) was a difficult emigration pattern to detect unless survival and especially recapture probability were high. Additionally, when local movement was high relative to study area boundaries and movement became more diffuse (e.g. a random walk), local movement and permanent emigration were difficult to distinguish and had consequences for correctly interpreting the survival parameter being estimated (apparent survival vs true survival).     

Disease‐structured N‐mixture models: A practical guide to model disease dynamics using count data

Obtaining inferences on disease dynamics (e.g., host population size, pathogen prevalence, transmission rate, host survival probability) typically requires marking and tracking individuals over time. While multistate mark–recapture models can produce high‐quality inference, these techniques are difficult to employ at large spatial and long temporal scales or in small remnant host populations decimated by virulent pathogens, where low recapture rates may preclude the use of mark–recapture techniques. Recently developed N‐mixture models offer a statistical framework for estimating wildlife disease dynamics from count data. N‐mixture models are a type of state‐space model in which observation error is attributed to failing to detect some individuals when they are present (i.e., false negatives). The analysis approach uses repeated surveys of sites over a period of population closure to estimate detection probability. We review the challenges of modeling disease dynamics and describe how N‐mixture models can be used to estimate common metrics, including pathogen prevalence, transmission, and recovery rates while accounting for imperfect host and pathogen detection. We also offer a perspective on future research directions at the intersection of quantitative and disease ecology, including the estimation of false positives in pathogen presence, spatially explicit disease‐structured N‐mixture models, and the integration of other data types with count data to inform disease dynamics. Managers rely on accurate and precise estimates of disease dynamics to develop strategies to mitigate pathogen impacts on host populations. At a time when pathogens pose one of the greatest threats to biodiversity, statistical methods that lead to robust inferences on host populations are critically needed for rapid, rather than incremental, assessments of the impacts of emerging infectious diseases.     

Models for Estimating Abundance from Repeated Counts of an Open Metapopulation

Summary Using only spatially and temporally replicated point counts, Royle (2004b, Biometrics 60, 108–115) developed an N ‐mixture model to estimate the abundance of an animal population when individual animal detection probability is unknown. One assumption inherent in this model is that the animal populations at each sampled location are closed with respect to migration, births, and deaths throughout the study. In the past this has been verified solely by biological arguments related to the study design as no statistical verification was available. In this article, we propose a generalization of the N ‐mixture model that can be used to formally test the closure assumption. Additionally, when applied to an open metapopulation, the generalized model provides estimates of population dynamics parameters and yields abundance estimates that account for imperfect detection probability and do not require the closure assumption. A simulation study shows these abundance estimates are less biased than the abundance estimate obtained from the original N ‐mixture model. The proposed model is then applied to two data sets of avian point counts. The first example demonstrates the closure test on a single‐season study of Mallards (Anas platyrhynchos), and the second uses the proposed model to estimate the population dynamics parameters and yearly abundance of American robins (Turdus migratorius) from a multi‐year study.     

Inferences about population dynamics from count data using multistate models: a comparison to capture–recapture approaches

Wildlife populations consist of individuals that contribute disproportionately to growth and viability. Understanding a population's spatial and temporal dynamics requires estimates of abundance and demographic rates that account for this heterogeneity. Estimating these quantities can be difficult, requiring years of intensive data collection. Often, this is accomplished through the capture and recapture of individual animals, which is generally only feasible at a limited number of locations. In contrast, N‐mixture models allow for the estimation of abundance, and spatial variation in abundance, from count data alone. We extend recently developed multistate, open population N‐mixture models, which can additionally estimate demographic rates based on an organism's life history characteristics. In our extension, we develop an approach to account for the case where not all individuals can be assigned to a state during sampling. Using only state‐specific count data, we show how our model can be used to estimate local population abundance, as well as density‐dependent recruitment rates and state‐specific survival. We apply our model to a population of black‐throated blue warblers (Setophaga caerulescens) that have been surveyed for 25 years on their breeding grounds at the Hubbard Brook Experimental Forest in New Hampshire, USA. The intensive data collection efforts allow us to compare our estimates to estimates derived from capture–recapture data. Our model performed well in estimating population abundance and density‐dependent rates of annual recruitment/immigration. Estimates of local carrying capacity and per capita recruitment of yearlings were consistent with those published in other studies. However, our model moderately underestimated annual survival probability of yearling and adult females and severely underestimates survival probabilities for both of these male stages. The most accurate and precise estimates will necessarily require some amount of intensive data collection efforts (such as capture–recapture). Integrated population models that combine data from both intensive and extensive sources are likely to be the most efficient approach for estimating demographic rates at large spatial and temporal scales.     

Stable isotopes reveal unexpected relationships between fire history and the diet of Spotted Owls

Although the effects of shifting fire regimes on bird populations have been recognized as important to ecology and conservation, the consequences of fire for trophic interactions of avian species – and raptors in particular – remain relatively unknown. Here, we found that within national parks with long‐standing (40+ years) fire management programmes, California Spotted Owls Strix occidentalis occidentalis consumed predominantly Woodrats Neotoma spp. and Pocket Gophers Thomomys spp.; however, in contrast to our predictions, when their territories experienced more extensive and frequent fire, Spotted Owls consumed proportionally more Flying Squirrels Glaucomys oregonensis. We hypothesize this finding could have been driven by either changes to prey abundance following fires (e.g. increases in flying squirrels) or changes to prey availability (e.g. shifts in forest structure or flying squirrel spatial distribution that increased predation upon them by owls). Our work thus demonstrates that fire may have unexpected consequences for the trophic interactions of raptor species and provides valuable information for the conservation of Spotted Owls in fire‐prone forest landscapes.     

Seasonal abundance and adult survival of bottlenose dolphins (Tursiops truncatus) in a community that cooperatively forages with fishermen in southern Brazil

A subgroup of a population of Tursiops truncatus in southern Brazil is known for a cooperative behavior with artisanal fishermen whereby the dolphins shoal fish towards net‐casting fishermen. Combining photo‐identification data collected between September 2007 and 2009 with mark‐recapture and Pollock's robust design models, we assessed abundance within seasons and survival and temporary emigration rates of dolphins between seasons. We also reanalyzed a previous data set collected during 1989–1991, and Cormack‐Jolly‐Seber models were applied to estimate survival rates for each of the study periods. The abundance of marked “cooperative” dolphins varied between seasons from 18 (CI: 17–24) to 21 (CI: 20–24). The total abundance varied from 59 in the winter of 2008 (CI: 49–72) to 50 in the autumn of 2009 (CI: 40–62). The annual adult survival was estimated to be 0.917 (CI: 0.876–0.961), close to that estimated from data collected in the 1990s (0.941; CI: 0.888–0.998). The emigration probability was low (0.031; CI: 0.011–0.084) and different capture probabilities between the “cooperative” and “noncooperative” dolphins indicated a degree of behavioral segregation. The precision of our estimates is likely to provide sufficient power to detect population change, but we recommend a precautionary management approach to protect this vulnerable dolphin community and its unique cooperative feeding tradition.     

Integrating sign surveys and telemetry data for estimating brown bear (Ursus arctos) density in the Romanian Carpathians

Accurate population size estimates are important information for sustainable wildlife management. The Romanian Carpathians harbor the largest brown bear (Ursus arctos) population in Europe, yet current management relies on estimates of density that lack statistical oversight and ignore uncertainty deriving from track surveys. In this study, we investigate an alternative approach to estimate brown bear density using sign surveys along transects within a novel integration of occupancy models and home range methods. We performed repeated surveys along 2‐km segments of forest roads during three distinct seasons: spring 2011, fall‐winter 2011, and spring 2012, within three game management units and a Natura 2000 site. We estimated bears abundances along transects using the number of unique tracks observed per survey occasion via N‐mixture hierarchical models, which account for imperfect detection. To obtain brown bear densities, we combined these abundances with the effective sampling area of the transects, that is, estimated as a function of the median (± bootstrapped SE) of the core home range (5.58 ± 1.08 km²) based on telemetry data from 17 bears tracked for 1‐month periods overlapping our surveys windows. Our analyses yielded average brown bear densities (and 95% confidence intervals) for the three seasons of: 11.5 (7.8–15.3), 11.3 (7.4–15.2), and 12.4 (8.6–16.3) individuals/100 km². Across game management units, mean densities ranged between 7.5 and 14.8 individuals/100 km². Our method incorporates multiple sources of uncertainty (e.g., effective sampling area, imperfect detection) to estimate brown bear density, but the inference fundamentally relies on unmarked individuals only. While useful as a temporary approach to monitor brown bears, we urge implementing DNA capture–recapture methods regionally to inform brown bear management and recommend increasing resources for GPS collars to improve estimates of effective sampling area.     

Assessing the abundance of Bristol Bay belugas with genetic mark‐recapture methods

The Bristol Bay stock of beluga whales (Delphinapterus leucas) is genetically distinct and resides in Bristol Bay year‐round. We estimated the abundance of this population using genetic mark‐recapture, whereby genetic markers from skin biopsies, collected between 2002 and 2011, were used to identify individuals. We identified 516 individual belugas in two inner bays, 468 from Kvichak Bay and 48 from Nushagak Bay, and recaptured 75 belugas in separate years. Using a POPAN Jolly‐Seber model, abundance was estimated at 1,928 belugas (95% CI = 1,611–2,337), not including calves, which were not sampled. Most belugas were sampled in Kvichak Bay at a time when belugas are also known to occur in Nushagak Bay. The pattern of genetic recaptures and data from belugas with satellite transmitters suggested that belugas in the two bays regularly mix. Hence, the estimate of abundance likely applies to all belugas within Bristol Bay. Simulations suggested that POPAN estimates of abundance are robust to most forms of emigration, but that emigration causes negative bias in both capture and survival probabilities. Because it is likely that some belugas do not enter the sampling area during sampling, our estimate of abundance is best considered a minimum population size.     

Using the robust design framework and relative abundance to predict the population size of pallid sturgeon Scaphirhynchus albus in the lower Missouri River

Several population viability models were constructed to aid recovery in endangered Scaphirhynchus albus, but these models are dependent upon accurate and precise input parameters that are not provided with standard catch per unit effort (CPUE) indices. Nine years of sampling efforts, under the robust design framework, provided 1223 unique captures with an 18·3% recapture rate. The annual population estimates varied from 4·0–7·3 fish rkm^?1 for wild and 8·4–18·4 fish rkm^?1 for hatchery‐reared S. albus. The relationship between abundance (N) and annual trot‐line CPUE indices (x = 70.726y + 2·533, R² = 0·91, P < 0·001) was used to predict an abundance of 13 616 ± 7142 s.e. S. albus in the lower Missouri River. The use of small‐scale intensive sampling to develop a relationship with relative abundance indices reported here, may provide a framework for other fisheries management applications where large‐scale intensive sampling is not feasible, but catch data are available.     

Trends in bowhead whales in West Greenland: Aerial surveys vs. genetic capture‐recapture analyses

We contrast two methods for estimating the trends of bowhead whales (Balaena mysticetus) in West Greenland: (1) double platform visual aerial survey, corrected for missed sightings and the time the whales are available at the surface; and (2) a genetic capture‐recapture approach based on a 14‐yr‐long biopsy sampling program in Disko Bay. The aerial survey covered 39,000 km² and resulted in 58 sightings, yielding an abundance estimate of 744 whales (CV = 0.34, 95% CI: 357–1,461). The genetic method relied on determining sex, mitochondrial haplotypes and genotypes of nine microsatellite markers. Based on samples from a total of 427 individuals, with 11 recaptures from previous years in 2013, this resulted in an estimate of 1,538 whales (CV = 0.24, 95% CI: 827–2,249). While the aerial survey is considered a snapshot of the local spring aggregation in Disko Bay, the genetic approach estimates the abundance of the source of this aggregation. As the whales in Disko Bay primarily are adult females that do not visit the bay annually, the genetic method would presumably yield higher estimates. The studies indicate that an increase in abundance observed between 1998 and 2006 has leveled off.     

Distance models as a tool for modelling detection probability and density of native bumblebees

Effective monitoring of native bee populations requires accurate estimates of population size and relative abundance among habitats. Current bee survey methods, such as netting or pan trapping, may be adequate for a variety of study objectives but are limited by a failure to account for imperfect detection. Biases due to imperfect detection could result in inaccurate abundance estimates or erroneous insights about the response of bees to different environments. To gauge the potential biases of currently employed survey methods, we compared abundance estimates of bumblebees (Bombus spp.) derived from hierarchical distance sampling models (HDS) to bumblebee counts collected from fixed‐area net surveys (“net counts”) and fixed‐width transect counts (“transect counts”) at 47 early‐successional forest patches in Pennsylvania. Our HDS models indicated that detection probabilities of Bombus spp. were imperfect and varied with survey‐ and site‐covariates. Despite being conspicuous, Bombus spp. were not reliably detected beyond 5 m. Habitat associations of Bombus spp. density were similar across methods, but the strength of association with shrub cover differed between HDS and net counts. Additionally, net counts suggested sites with more grass hosted higher Bombus spp. densities whereas HDS suggested that grass cover was associated with higher detection probability but not Bombus spp. density. Density estimates generated from net counts and transect counts were 80%–89% lower than estimates generated from distance sampling. Our findings suggest that distance modelling provides a reliable method to assess Bombus spp. density and habitat associations, while accounting for imperfect detection caused by distance from observer, vegetation structure, and survey covariates. However, detection/non‐detection data collected via point‐counts, line‐transects and distance sampling for Bombus spp. are unlikely to yield species‐specific density estimates unless individuals can be identified by sight, without capture. Our results will be useful for informing the design of monitoring programs for Bombus spp. and other pollinators.     

State uncertainty models and mark‐resight models for understanding non‐breeding site use by Piping Plovers

Conservation of beach‐nesting medium‐distance migrants has focused on breeding areas because protection of nests is more tractable than protection of non‐breeding habitat. As breeding ground management has encountered diminishing returns, interest in understanding threats in non‐breeding areas has increased. However, robust estimates of non‐breeding demographic rates and abundance are generally lacking, hindering the study of limiting factors. Estimating such rates is made more difficult by complex population dynamics at non‐breeding sites. In South Carolina, endangered Piping Plovers Charadrius melodus start arriving in July and some depart prior to December (the autumn‐only population) while others remain through at least March (the wintering population). State uncertainty capture‐mark‐recapture models provide a means for estimating vital rates for such co‐occurring populations. We estimated the proportion of the population entering the study area per survey (entry probability) and proportion remaining per survey (persistence rate) for both populations during autumn, and abundance of the wintering population, at four sites in South Carolina in 2006/7 and 2007/8, taking advantage of birds previously colour‐ringed on the breeding grounds. We made fairly precise estimates of entry and persistence rates with small sample sizes. Cumulative entry probability was ~50% by the end of July and reached 95% for both populations by October. Estimated stopover duration for birds in the autumn‐only population was 35 days in year 1 and 42 days in year 2. We estimated a wintering super‐population size of 71 ± 16 se birds in the first year and 75 ± 16 in the second. If ringing programmes on the breeding grounds continue, standardized resighting surveys in the non‐breeding period and mark‐recapture models can provide robust estimates of entry and persistence rates and abundance. Habitat protection intended to benefit non‐breeding Piping Plovers at our coastal sites should be in effect by late summer, as many birds are resident from July to the end of winter.     

Food web quantification using secondary production analysis: predaceous invertebrates of the snag habitat in a subtropical river

1. Secondary production was estimated for Plecoptera, Odonata and Megaloptera (mostly large predators) occurring on the snag habitat of a subtropical blackwater river in the southeastern U.S.A. Coastal Plain for 2 years. Production estimates and gut analyses were used in estimating species‐specific ingestion to construct a quantitative food web of the predator portion of the invertebrate assemblage. Neither basal resources (e.g. detritus) nor predaceous vertebrates (e.g. fishes) were considered in this analysis. A discharge‐specific model of snag‐habitat availability was used to convert values per m² of snag surface to values per m² of river bed.
2. These three orders included the major large predators on the snag habitat, as well as two detritivorous stoneflies. The major predators were the hellgrammite (Corydaluscornutus), five perlid stoneflies (Paragnetinakansensis, Perlestaplacida, Neoperlaclymene, Acroneuriaevoluta and Acroneuriaabnormis) and two dragonflies (Neurocorduliamolesta and Boyeriavinosa). The detritivores were Pteronarcysdorsata and Taeniopteryxlita.
3. Total predator production was high, but varied from only 7.1 to 7.4 g dry mass (DM) m^?2 y^?1 of snag surface (2.4–2.7 g DM m^?2 y^?1 of river bed) over two years. Corydalus was the largest predator and had the highest production (2.8–3.1 g DM m^?2 of snag surface). The most productive stoneflies were Perlesta (0.7–1.0 g DM m^?2 of snag surface) and Paragnetina (1.0–1.3 g DM m^?2 of snag surface). The most productive dragonfly was Neurocordulia (0.7–1.9 g DM m^?2 of snag surface). Production of the non‐predaceous stoneflies was 1.0–2.3 g DM m^?2 of snag surface. Production values per m² of river bed were 2–3.5 times lower than the values per m² snag surface.
4. Measurement of ingestion fluxes within the predator portion of the food web showed that predaceous invertebrates were primarily supported by chironomid and mayfly prey. However, the greatest consumption of chironomids and mayflies was by omnivorous hydropsychid caddisflies, which had a considerably higher production than the larger predators. There was a hierarchy of feeding with Corydalus as top predator consuming all other groups, followed in order by dragonflies, stoneflies and hydropsychids. Although the feeding hierarchy suggested the presence of four predatory trophic levels within the invertebrate assemblage, calculations of trophic position indicated there were less than two. With primary consumers (e.g. midges) having a trophic position of 2, Corydalus had a trophic position of only 3.5.
5. A relatively high fraction of invertebrate production was consumed by predaceous invertebrates, ranging from 9 to >100% for various primary consumer groups, with total consumption representing 52% of total production. Because these estimates do not include vertebrate consumption or emergence, it means that a high fraction of larval mortality is due to predation.     

Mark‐recapture abundance estimate of tucuxi dolphins (Sotalia fluviatilis) in a lake system of the Central Amazon

The tucuxi (Sotalia fluviatilis) is a small dolphin endemic to the Amazon River basin. Because the abundance and trends are currently unknown for the species, this study aimed to estimate its abundance in a lake system of the Central Amazon. A total of 10 two‐day sampling periods were carried out from March to June of 2013 throughout a 13.5 km² area in the Mamirauá Reserve. In the 104 encounters with the species, a minimum number of 389 dolphins were sighted and photographed, which allowed the positive identification of 49 individuals. Mark‐recapture models were used to estimate an abundance of 119 individuals (95% CI = 105–150) (corrected for the proportion of identifiable individuals). This is the first estimation of S. fluviatilis abundance using mark‐recapture analyses and, together with the photo‐id catalog made available, provides a useful reference for future studies regarding tucuxi dolphins.     

Environmental DNA facilitates accurate,inexpensive, and multiyear population estimates of millions of anadromous fish

Although environmental DNA shed from an organism is now widely used for species detection in a wide variety of contexts, mobilizing environmental DNA for management requires estimation of population size and trends in addition to assessing presence or absence. However, the efficacy of environmental‐DNA‐based indices of abundance for long‐term population monitoring have not yet been assessed. Here we report on the relationship between six years of mark‐recapture population estimates for eulachon (Thaleichthys pacificus) and “eDNA rates” which are calculated from the product of stream flow and DNA concentration. Eulachon are a culturally and biologically important anadromous fish that have significantly declined in the southern part of their range but were historically rendered into oil and traded. Both the peak eDNA rate and the area under the curve of the daily eDNA rate were highly predictive of the mark‐recapture population estimate, explaining 84.96% and 92.53% of the deviance, respectively. Even in the absence of flow correction, the peak of the daily eDNA concentration explained an astonishing 89.53% while the area under the curve explained 90.74% of the deviance. These results support the use of eDNA to monitor eulachon population trends and represent a >80% cost savings over mark‐recapture, which could be further increased with automated water sampling, reduced replication, and focused temporal sampling. Due to its logistical ease and affordability, eDNA sampling can facilitate monitoring a larger number of rivers and in remote locations where mark‐recapture is infeasible.     

multimark: an R package for analysis of capture–recapture data consisting of multiple “noninvasive” marks

I describe an open‐source R package, multimark , for estimation of survival and abundance from capture–mark–recapture data consisting of multiple “noninvasive” marks. Noninvasive marks include natural pelt or skin patterns, scars, and genetic markers that enable individual identification in lieu of physical capture. multimark provides a means for combining and jointly analyzing encounter histories from multiple noninvasive sources that otherwise cannot be reliably matched (e.g., left‐ and right‐sided photographs of bilaterally asymmetrical individuals). The package is currently capable of fitting open population Cormack–Jolly–Seber (CJS) and closed population abundance models with up to two mark types using Bayesian Markov chain Monte Carlo (MCMC) methods. multimark can also be used for Bayesian analyses of conventional capture–recapture data consisting of a single‐mark type. Some package features include (1) general model specification using formulas already familiar to most R users, (2) ability to include temporal, behavioral, age, cohort, and individual heterogeneity effects in detection and survival probabilities, (3) improved MCMC algorithm that is computationally faster and more efficient than previously proposed methods, (4) Bayesian multimodel inference using reversible jump MCMC, and (5) data simulation capabilities for power analyses and assessing model performance. I demonstrate use of multimark using left‐ and right‐sided encounter histories for bobcats (Lynx rufus) collected from remote single‐camera stations in southern California. In this example, there is evidence of a behavioral effect (i.e., trap “happy” response) that is otherwise indiscernible using conventional single‐sided analyses. The package will be most useful to ecologists seeking stronger inferences by combining different sources of mark–recapture data that are difficult (or impossible) to reliably reconcile, particularly with the sparse datasets typical of rare or elusive species for which noninvasive sampling techniques are most commonly employed. Addressing deficiencies in currently available software, multimark also provides a user‐friendly interface for performing Bayesian multimodel inference using capture–recapture data consisting of a single conventional mark or multiple noninvasive marks.     

Cormack–Jolly–Seber model with environmental covariates: A P‐spline approach

In capture–recapture models, survival and capture probabilities can be modelled as functions of time‐varying covariates, such as temperature or rainfall. The Cormack–Jolly–Seber (CJS) model allows for flexible modelling of these covariates; however, the functional relationship may not be linear. We extend the CJS model by semi‐parametrically modelling capture and survival probabilities using a frequentist approach via P‐splines techniques. We investigate the performance of the estimators by conducting simulation studies. We also apply and compare these models with known semi‐parametric Bayesian approaches on simulated and real data sets.     

Survival of Greater Sage‐Grouse broods: survey method affects disturbance and age‐specific detection probability

Investigators rely on brood surveys to estimate annual fecundity of game birds. However, investigators often do not account for factors that influence brood detection probability nor rarely document how much females and their broods are disturbed (flush rates) during surveys, which could lead to biased survival estimates. We used 45 radio‐tagged female Greater Sage‐Grouse (Centrocercus urophasianus) with broods to compare detection probabilities and document disturbance among four survey methods to allow future investigators to select the method that best meets their objectives. These methods included daytime flush, daytime visual, nocturnal spotlight, and fecal surveys at nocturnal roost sites, with the latter being a novel method. We used Cormack–Jolly–Seber (CJS) models to compare detection probability and daily survival estimates for visual and fecal surveys of broods 0–47 d post‐hatch and a double‐survey approach to compare detection probabilities among flush, fecal, and spotlight surveys ~42 d post‐hatch when investigators often determine brood fate. From CJS models, detection probability for visual surveys increased with brood age (0.618–0.881), whereas detection probability for fecal surveys did not (0.748). Daily survival probability estimates increased with brood age and differed annually based on fecal surveys (2016: 0.978–1.000 and 2017: 0.839–0.998). We detected age‐specific daily survival probability with visual surveys (0.956–0.997), but not annual differences. Based on the double‐survey approach, detection probability was high (0.857–1.000) for all methods. We flushed ~310–750% fewer females and broods during fecal and spotlight surveys than during both types of daytime surveys. Our results highlight the need to account for detection probabilities among methods and document disturbance to hens and broods that can help investigators design surveys to minimize impacts to birds. Furthermore, our result suggest that actions to improve brood survival during the first week post‐hatch may improve local recruitment.     

Survival and abundance of short‐finned pilot whales in the archipelago of Madeira,NE Atlantic

Estimates of population parameters for the short‐finned pilot whale, Globicephala macrorhynchus, are scarce in literature, contributing to an International Union for Conservation of Nature (IUCN) status of Data Deficient. In this study, photo‐identification data collected over 7 yr from Madeira were used to estimate for the first time survivorship, capture probability, and abundance in this species using mark‐recapture methodology. The Cormack‐Jolly‐Seber model estimated that the adult island‐associated (i.e., resident and regular visitor) whales had a constant survival rate of 0.960 (95% CI: 0.853–0.990) and an annual capture probability varying between 0.372 (CI: 0.178–0.619) and 0.843 (CI: 0.619–0.947). A parameterization of the Jolly‐Seber model estimated that 140 island‐associated whales (CI: 131–151) used the area throughout the course of the study. Based on a closed population model, the most precise (lower CV) annual estimate of the total number of pilot whales using the southern and eastern waters of Madeira (~900 km²) in a 3 mo period covering summer/autumn was 334 animals (CI: 260–437). No trend was observed. Despite including biases, the approach used in this study provided plausible estimates of population parameters, which can contribute to the regional conservation strategies.     

Trap‐shyness subsidence is a threshold function of mark–recapture interval in brown mudfish Neochanna apoda populations

The influence of capture interval on trap shyness, and temperature, rainfall and drought on capture probability (p) in 827 brown mudfish Neochanna apoda was quantified using mark–recapture models. In particular, it was hypothesized that the loss of trapping memory in marked N. apoda would lead to a capture‐interval threshold required to minimize trap shyness. Neochanna apoda trap shyness approximated a threshold response to capture interval, declining rapidly with increasing capture intervals up to 16·5 days, after which p remained constant. Tests for detecting trap‐dependent capture probability in Cormack–Jolly–Seber models failed to detect trap shyness in N. apoda capture histories with capture intervals averaging 16 days. This confirmed the applicability of the 16 day capture‐interval threshold for mark–recapture studies. Instead, N. apoda p was positively influenced by water temperature and rainfall during capture. These results imply that a threshold capture interval is required to minimize the trade‐off between the competing assumptions of population closure and p homogeneity between capture occasions in closed mark–recapture models. Moreover, environmental factors that influence behaviour could potentially confound abundance indices, and consequently abundance trends should be interpreted with caution in the face of long‐term climate change, such as with global warming.