Survival of breeding male American woodcock in Maine

A common challenge for studying wildlife populations occurs when different survey methods provide inconsistent or incomplete inference on the trend, dynamics, or viability of a population. A potential solution to the challenge of conflicting or piecemeal data relies on the integration of multiple data types into a unified modeling framework, such as integrated population models (IPMs). IPMs are a powerful approach for species that inhabit spatially and seasonally complex environments. We provide guidance on exploiting the capabilities of IPMs to address inferential discrepancies that stem from spatiotemporal data mismatches. We illustrate this issue with analysis of a migratory species, the American Woodcock (Scolopax minor), in which individual monitoring programs suggest differing population trends. To address this discrepancy, we synthesized several long‐term data sets (1963–2015) within an IPM to estimate continental‐scale population trends, and link dynamic drivers across the full annual cycle and complete extent of the woodcock's geographic range in eastern North America. Our analysis reveals the limiting portions of the life cycle by identifying time periods and regions where vital rates are lowest and most variable, as well as which demographic parameters constitute the main drivers of population change. We conclude by providing recommendations for resolving conflicting population estimates within an integrated modeling approach, and discuss how strategies (e.g., data thinning, expert opinion elicitation) from other disciplines could be incorporated into ecological analyses when attempting to combine multiple, incongruent data types.

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“…), seasonal survival from radio telemetry (Longcore et al. ), harvest surveys (Padding et al. ), and band‐recovery analyses (Krementz et al.…”

Section: Introductionmentioning

confidence: 99%

Disentangling data discrepancies with integrated population models

Saunders

Farr

Wright

et al. 2019

Ecology

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“…We found no sex or age‐related differences in woodcock survival rates during fall. Similarly, no sex or age‐related variation has been found in most telemetry‐based studies of woodcock survival (Krementz et al , Longcore et al , Krementz and Berdeen , Longcore et al , McAuley et al ) during various parts of the year. An exception was Derleth and Sepik (), who found that summer‐fall survival rate estimates were related to age.…”

Section: Discussionmentioning

confidence: 98%

“…Data from the Harvest Information Program suggest that most woodcock hunting mortality occurs in breeding ground states (Cooper and Rau ). Woodcock survival rates have been documented with radiotelemetry during various periods of the year (e.g., Derleth and Sepik ; Krementz et al ; Longcore et al , ) but only McAuley et al () examined survival in a hunted population during fall, and their study was conducted in the Eastern Region. Thus, information on survival during fall is lacking for the Central Region.…”

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confidence: 99%

Fall survival of American woodcock in the western Great Lakes Region

et al. 2013

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We estimated fall (10 Sep–8 Nov) survival rates, cause‐specific mortality rates, and determined the magnitude and sources of mortality of 1,035 radio‐marked American woodcock (Scolopax minor) in Michigan, Minnesota, and Wisconsin during 2001–2004. In all 3 states, we radio‐marked woodcock on paired study areas; 1 of which was open to hunting and expected to receive moderate to high hunter use and the other of which was either closed to hunting (Michigan and Minnesota) or was relatively inaccessible to hunters (Wisconsin). We used Program MARK to estimate fall survival rates, to evaluate a set of candidate models to examine the effects of hunting and several covariates (sex, age, year, state) on survival, and to examine the relationship between survival rates and kill rates due to hunting. Hunting accounted for 70% of the 86 woodcock deaths in the hunted areas, followed by predation (20%) and various other sources of mortality (10%). Woodcock deaths that occurred in the non‐hunted and lightly hunted areas (n = 50) were caused by predators (46%), hunting (32%), and various other sources (22%). Based on small‐sample corrected Akaike's Information Criterion values, variation in fall survival of woodcock was best explained by treatment (i.e., hunted vs. non‐hunted), year, and period (pre‐hunting season intervals vs. hunting season intervals). The average fall survival estimate from our best model for woodcock in the non‐hunted areas (0.893, 95% CI = 0.864–0.923) was greater than the average for the hunted areas (0.820, 95% CI = 0.786–0.854 [this estimate includes data from the lightly hunted area in Wisconsin]), and the average treatment effect (i.e., greater survival rates in non‐hunted areas) was 0.074 (95% CI = 0.018–0.129). The kill rate due to hunting was 0.120 (95% CI = 0.090–0.151) when data were pooled among states and years. We detected a negative relationship between hunting kill rates and survival in our hunted areas, which suggests that hunting mortality was at least partially additive during fall. Our results illustrate the influence of hunting relative to other sources of mortality in Michigan, Minnesota, and Wisconsin, and indicate that managers may be able to influence fall survival rates by manipulating hunting regulations or access on public land. © 2013 The Wildlife Society.

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“…If survival is <1 between the banding and the recovery periods, thenf is underestimated because it is confounded with survival during those periods. Radio-telemetry estimates of winter and spring survival probabilities for woodcock are relatively low ( S 25 December to 7 February ¼ 0:72, Krementz and Berdeen (1997); S 1 April to 15 June ¼ 0:79, Longcore et al (1996)) compared with summer and early fall ( S June to October > 0:894, Derleth and Sepik (1990)). Therefore, survival of birds banded in July and August is likely to be~1, but survival of birds banded in other months is likely to be <1, so that the model of Brownie et al (1985) is likely to underestimate f. Thus, to minimise bias and include as many bandings as possible, we included two groups of banded woodcock for estimating f age,t .…”

Section: Band-recovery Modelmentioning

confidence: 99%

Estimating migratory game-bird productivity by integrating age ratio and banding data

et al. 2010

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Context. Reproduction is a critical component of fitness, and understanding factors that influence temporal and spatial dynamics in reproductive output is important for effective management and conservation. Although several indices of reproductive output for wide-ranging species, such as migratory birds, exist, there has been no theoretical justification for their estimators or associated measures of variance.Aims. The aims of our research were to develop statistical justification for an estimator of reproduction and associated variances on the basis of an existing national wing-collection survey and banding data, and to demonstrate the applicability of this estimator to a migratory game bird.Methods. We used a Bayesian hierarchical modelling approach to integrate wing-collection data, which provides information on population age ratios, and band-recovery data, which provides information on recovery probabilities of various age classes, for American woodcock (Scolopax minor) to estimate productivity and associated measures of variance. We present two models of relative vulnerability between age classes: one model assumed that adult recovery probabilities were higher, but that annual fluctuations were synchronous between the two age classes (i.e. an additive effect of age and year). The second model assumed that adults, on average, had higher recovery probabilities than did juveniles and that annual fluctuations were asynchronous through time (i.e. an interaction between age and year).Key results. Fitting our models within a hierarchical Bayesian framework efficiently incorporates the two data types into a single estimator and derives appropriate variances for the productivity estimator. Further, use of Bayesian methods enabled us to derive credible intervals that avoid the reliance on asymptotic assumptions. When applied to American woodcock data, the additive model resulted in biologically realistic and more precise age-ratio estimates each year and is adequate when the relative vulnerability to sampling only slightly varies or does not vary among components of a population (e.g. age, sex class) among years. Therefore, we recommend using woodcock indices from our analysis based on this model. Conclusions.We provide a flexible modelling framework for estimating productivity and associated variances that can incorporate ecological covariates to explore various factors that could drive annual dynamics in productivity. Applying our model to the American woodcock data indicated that assumptions about the variability in relative recovery probabilities could greatly influence the precision of our productivity estimator. Therefore, researchers should carefully consider the assumption of temporally variable relative recovery probabilities (i.e. ratio of juvenile to adults' recovery probability) for different age classes when applying this estimator.Implications. Several national and international management strategies for migratory game birds in North America rely on measures of productivity from harvest survey parts co...

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Survival of breeding male American woodcock in Maine

Cited by 13 publications

References 16 publications

Disentangling data discrepancies with integrated population models

Disentangling data discrepancies with integrated population models

Fall survival of American woodcock in the western Great Lakes Region

Estimating migratory game-bird productivity by integrating age ratio and banding data

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