Quantifying changes in abundance without counting animals: extensions to a method of fitting integrated population models

Freeman, Stephen N.; Besbeas, Panagiotis

doi:10.1007/s10336-011-0667-4

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…Recognizing the interest in combining capture–recapture and occupancy protocols, Freeman & Besbeas () developed an integrated approach in which they combined count survey and ring‐recovery data to estimate abundance. Based on simulations and the analysis of a real case study, they demonstrated that combining different sources of data provided a better precision allowing the detection of change in abundance and other demographic parameters that would not be possible otherwise.…”

Section: Introductionmentioning

confidence: 99%

“…Based on simulations and the analysis of a real case study, they demonstrated that combining different sources of data provided a better precision allowing the detection of change in abundance and other demographic parameters that would not be possible otherwise. Nevertheless, Freeman & Besbeas () assumed that detectability was equal to 1 and did not explicitly consider the observation process.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improving abundance estimation by combining capture–recapture and occupancy data: example with a large carnivore

Blanc

Marboutin

Gatti

et al. 2014

Journal of Applied Ecology

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Summary1. Abundance is a key quantity for conservation and management strategies but remains challenging to assess in the field. Capture-recapture (CR) methods are often used to estimate abundance while correcting for imperfect detection, but these methods are costly. Occupancy, sometimes considered as a surrogate for abundance, is estimated through the collection of presence/absence data and is less costly while allowing gathering of information at a large spatial scale. 2. Building on the recent pieces of work on the combination of different data sources, we showed how abundance data can be complemented by presence/absence data and can be analysed conjointly to improve abundance estimates. Our approach relies on a hierarchical model that makes explicit the link between the abundance and occupancy state variables while formally accounting for imperfect detection. 3. We used a population of Eurasian lynx in France monitored via camera traps and a collection of presence signs as an illustration of our approach. 4. Synthesis and applications. We combined capture-recapture and occupancy data and demonstrated that we can efficiently improve abundance estimates. Our method can be used by managers when estimates of trends in abundance lack power due to sparse data collected during an intensive survey, by simply integrating data collected during non-systematic survey. Furthermore, combining these two sampling procedures makes full use of all available data and allows the development of conservation and management strategies based on precise abundance estimates. Overall, the combination of different data sources in an integrated statistical framework has great potential, especially for elusive species.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving abundance estimation by combining capture–recapture and occupancy data: example with a large carnivore

Blanc

Marboutin

Gatti

et al. 2014

Journal of Applied Ecology

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“…Essentially, a likelihood factor for the demographic data is multiplied by a likelihood factor for the survey data, with some parameters shared with the first factor, to form a joint likelihood. Estimating an integrated population model amounts to optimizing the joint likelihood, which can be computationally demanding for complex sampling schemes (Freeman and Besbeas 2012). A pseudo ML approach to the survey likelihood could simplify analyses substantially.…”

Section: Discussionmentioning

confidence: 99%

Using uncertainty estimates in analyses of population time series

Knape

Besbeas

Valpine

2013

Ecology

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Recording and monitoring wildlife is crucial for the conservation of wild species and the protection of their environment. The most common type of information reported from a monitoring scheme is a time series of population abundance estimates, but the potential of such data for analyzing population dynamics is limited due to lack of information on sampling error. Recent work has shown that replicating the sampling process and analyzing replicates jointly in a dynamical model can considerably increase estimation efficiency compared to analyzing population estimates alone. This method requires that independent replicates are available, and model fitting can be complex in general. Often, however, population estimates are accompanied by standard errors, or standard errors may be estimated from raw data using a sampling model. We evaluate a method where standard errors are used in combination with population estimates to account for sampling variability in state-space models of population dynamics. The method is simple and lends itself readily to data derived from many sampling procedures but ignores uncertainty in the standard errors themselves. We simulate data from a Gaussian state-space model where several observations, which may come from different sites, are available for the population at each time. Fitting the simulated data, we show that the method yields similar or even better results than a method utilizing all observations, even when there are few observations at each time. This holds under a range of simulation settings involving heteroscedastic observation error, site effects, and correlation among observations. We illustrate the approach on real data from the North American Breeding Bird Survey and show that it performs well in comparison to a more difficult maximum-likelihood analysis of the full data under non-Gaussian sampling error.

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“…Multiple data sources are usually combined using joint-likelihood methods (but see Pacifici et al, 2017) and often have the advantage of increased population parameter precision (Besbeas et al, 2002;Schaub & Abadi, 2011). Several data type combinations have been used in these joint models, including capture-recapture, count, and fecundity data (integrated models; i.e., Ahrestani, Saracco, Sauer, Pardieck, & Royle, 2017;Schaub & Abadi, 2011;Wilson, Gil-Weir, Clark, Robertson, & Bidwell, 2016), capture-recapture and census/count data (Catchpole, Freeman, Morgan, & Harris, 1998), radiotelemetry and capture-recapture data (Powell, Conroy, Hines, Nichols, & Krementz, 2000), count and detection/non-detection data (Zipkin et al, 2017), and capture-recapture and detection/ non-detection data (Freeman & Besbeas, 2012). In addition, recent advances with Bayesian hierarchical models have illustrated the utility of integrated data models (Schaub & Abadi, 2011;Schaub et al, 2007), and created opportunities for designing future studies incorporating multiple data sources.…”

Section: Introductionmentioning

confidence: 99%

Precision gain versus effort with joint models using detection/non‐detection and banding data

Sanderlin

Block

Strohmeyer

et al. 2019

Ecology and Evolution

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Capture–recapture techniques provide valuable information, but are often more cost‐prohibitive at large spatial and temporal scales than less‐intensive sampling techniques. Model development combining multiple data sources to leverage data source strengths and for improved parameter precision has increased, but with limited discussion on precision gain versus effort. We present a general framework for evaluating trade‐offs between precision gained and costs associated with acquiring multiple data sources, useful for designing future or new phases of current studies.We illustrated how Bayesian hierarchical joint models using detection/non‐detection and banding data can improve abundance, survival, and recruitment inference, and quantified data source costs in a northern Arizona, USA, western bluebird (Sialia mexicana) population. We used an 8‐year detection/non‐detection (distributed across the landscape) and banding (subset of locations within landscape) data set to estimate parameters. We constructed separate models using detection/non‐detection and banding data, and a joint model using both data types to evaluate parameter precision gain relative to effort.Joint model parameter estimates were more precise than single data model estimates, but parameter precision varied (apparent survival > abundance > recruitment). Banding provided greater apparent survival precision than detection/non‐detection data. Therefore, little precision was gained when detection/non‐detection data were added to banding data. Additional costs were minimal; however, additional spatial coverage and ability to estimate abundance and recruitment improved inference. Conversely, more precision was gained when adding banding to detection/non‐detection data at higher cost. Spatial coverage was identical, yet survival and abundance estimates were more precise. Justification of increased costs associated with additional data types depends on project objectives.We illustrate a general framework for evaluating precision gain relative to effort, applicable to joint data models with any data type combination. This framework evaluates costs and benefits from and effort levels between multiple data types, thus improving population monitoring designs.

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Quantifying changes in abundance without counting animals: extensions to a method of fitting integrated population models

Cited by 6 publications

References 29 publications

Improving abundance estimation by combining capture–recapture and occupancy data: example with a large carnivore

Improving abundance estimation by combining capture–recapture and occupancy data: example with a large carnivore

Using uncertainty estimates in analyses of population time series

Precision gain versus effort with joint models using detection/non‐detection and banding data

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