Extending density surface models to include multiple and double-observer survey data

David L. Miller was funded by OPNAV N45 and the SURTASS LFA Settlement Agreement, being managed by the U.S. Navy’s Living Marine Resources program under Contract No. N39430-17-C-1982, collaboration between Douglas B. Sigourney and David L. Miller was also facilitated by the DenMod working group (https://synergy.st-andrews.ac.uk/denmod/) funded under the same agreement. The survey that the fin whale data originate from was funded through two inter-agency agreements with the National Marine Fisheries Service: inter-agency agreement number M14PG00005 with the US Department of the Interior, Bureau of Ocean Energy Management, Environmental Studies Program, Washington, DC and inter-agency agreement number NEC-16-011-01-FY18 with the US Navy. The survey that the fulmar data originate from was funded by the UK Natural Environmental Research Council (NERC) grant NE/M017990/1.Spatial models of density and abundance are widely used in both ecological research (e.g., to study habitat use) and wildlife management (e.g., for population monitoring and environmental impact assessment). Increasingly, modellers are tasked with integrating data from multiple sources, collected via different observation processes. Distance sampling is an efficient and widely used survey and analysis technique. Within this framework, observation processes are modelled via detection functions. We seek to take multiple data sources and fit them in a single spatial model. Density surface models (DSMs) are a two-stage approach: first accounting for detectability via distance sampling methods, then modelling distribution via a generalized additive model. However, current software and theory does not address the issue of multiple data sources. We extend the DSM approach to accommodate data from multiple surveys, collected via conventional distance sampling, double-observer distance sampling (used to account for incomplete detection at zero distance) and strip transects. Variance propagation ensures that uncertainty is correctly accounted for in final estimates of abundance. Methods described here are implemented in the dsm R package. We briefly analyse two datasets to illustrate these new developments. Our new methodology enables data from multiple distance sampling surveys of different types to be treated in a single spatial model, enabling more robust abundance estimation, potentially over wider geographical or temporal domains.Publisher PDFPeer reviewe

Growth of Atlantic Salmon (Salmo salar) in Freshwater

Growth plays a key role in regulating ecological and population dynamics. Life history characteristics such as age at maturity, fecundity and age and size at migration are tightly linked to growth rate. In addition, size can often determine survival and individual breeding success. To fully understand the process of growth it is important to understand the mechanisms that drive growth rates. In Atlantic salmon, growth is critical in determining life history pathways. Models to estimate growth could be useful in the broader context of predicting population dynamics. In this dissertation I investigate the growth process in juvenile Atlantic salmon (Salmo salar). I first used basic modeling approaches and data on individually tagged salmon to investigate the assumptions of different growth metrics. I demonstrate the size-dependency in certain growth metrics when assumptions are violated. Next, I assessed the efficacy of linear mixed effects models in modeling length-weight relationships from longitudinal data. I show that combining a random effects approach with third order polynomials can be an effective way to model length-weight relationships with mark-recapture data. I extend this hierarchical modeling approach to develop a Bayesian growth model. With limited assumptions, I derive a relatively simple discrete time model from von Bertalanffy growth that includes a nonparametric seasonal growth function. The linear dynamics of this model allow for efficient estimation of parameters in a Bayesian framework. Finally, I investigated the role of life history in driving compensatory growth patterns in immature Atlantic salmon. This analysis demonstrates the importance of considering life history as a mechanism in compensatory growth. Information provided in this dissertation will help provide ecologists with statistical tools to estimate growth rates, estimate length-weight relationships, and forecast growth from mark-recapture data. In addition, comparisons of seasonal growth within and among life history groups and within and among tributaries should make a valuable contribution to the important literature on growth in Atlantic salmon

Acoustic Data Integration

The code in this repository contains the scripts to run the analyses described in the manuscript " Integrating passive acoustic data from a towed hydrophone array with visual line transect data to estimate abundance and availability bias: A case study with sperm whales (Physeter macrocephalus)" by Sigourney et al. 2023 (submitted to PeerJ). It is publicly available on github at https://github.com/NEFSC/READ-PSB-DSIGOURNEY-AcousticDataIntegration

Hard choices in assessing survival past dams - a comparison of single and paired release strategies

Mark-recapture models are widely used to estimate survival of salmon smolts migrating past dams. Paired releases have been used to improve estimate accuracy by removing components mortality not attributable to the dam. This method is accompanied by reduced precision because: i) sample size is reduced relative to a single, large release; and ii) variance calculations inflate error. We modeled an idealized system with a single dam to assess tradeoffs between accuracy and precision and compared methods using root mean squared error (RMSE). Simulations were run under pre-defined conditions (dam mortality, background mortality, detection probability, and sample size) to determine scenarios when the paired release was preferable to a single release. We demonstrate that a paired-release provides a theoretical advantage over a single-release design only at large sample sizes and high probabilities of detection. At release numbers typical of many survival studies, paired release can result in overestimation of dam survival. Failures to meet model assumptions of a paired release may result in further overestimation of dam-related survival. Under most conditions, a single-release strategy was preferable.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Extending density surface models to include multiple and double-observer survey data

Spatial models of density and abundance are widely used in both ecological research (e.g., to study habitat use) and wildlife management (e.g., for population monitoring and environmental impact assessment). Increasingly, modellers are tasked with integrating data from multiple sources, collected via different observation processes. Distance sampling is an efficient and widely used survey and analysis technique. Within this framework, observation processes are modelled via detection functions. We seek to take multiple data sources and fit them in a single spatial model. Density surface models (DSMs) are a two-stage approach: first accounting for detectability via distance sampling methods, then modelling distribution via a generalized additive model. However, current software and theory does not address the issue of multiple data sources. We extend the DSM approach to accommodate data from multiple surveys, collected via conventional distance sampling, double-observer distance sampling (used to account for incomplete detection at zero distance) and strip transects. Variance propagation ensures that uncertainty is correctly accounted for in final estimates of abundance. Methods described here are implemented in the dsm R package. We briefly analyse two datasets to illustrate these new developments. Our new methodology enables data from multiple distance sampling surveys of different types to be treated in a single spatial model, enabling more robust abundance estimation, potentially over wider geographical or temporal domains

Transport, Dam Passage, and Size Selection of Adult Atlantic Salmon in the Penobscot River, Maine

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Robust estimates of environmental effects on population vital rates:an integrated capture–recapture model of seasonal brook trout growth, survival and movement in a stream network

Modelling the effects of environmental change on populations is a key challenge for ecologists, particularly as the pace of change increases. Currently, modelling efforts are limited by difficulties in establishing robust relationships between environmental drivers and population responses.We developed an integrated capture–recapture state-space model to estimate the effects of two key environmental drivers (stream flow and temperature) on demographic rates (body growth, movement and survival) using a long-term (11 years), high-resolution (individually tagged, sampled seasonally) data set of brook trout (Salvelinus fontinalis) from four sites in a stream network. Our integrated model provides an effective context within which to estimate environmental driver effects because it takes full advantage of data by estimating (latent) state values for missing observations, because it propagates uncertainty among model components and because it accounts for the major demographic rates and interactions that contribute to annual survival.We found that stream flow and temperature had strong effects on brook trout demography. Some effects, such as reduction in survival associated with low stream flow and high temperature during the summer season, were consistent across sites and age classes, suggesting that they may serve as robust indicators of vulnerability to environmental change. Other survival effects varied across ages, sites and seasons, indicating that flow and temperature may not be the primary drivers of survival in those cases. Flow and temperature also affected body growth rates; these responses were consistent across sites but differed dramatically between age classes and seasons. Finally, we found that tributary and mainstem sites responded differently to variation in flow and temperature.Annual survival (combination of survival and body growth across seasons) was insensitive to body growth and was most sensitive to flow (positive) and temperature (negative) in the summer and fall.These observations, combined with our ability to estimate the occurrence, magnitude and direction of fish movement between these habitat types, indicated that heterogeneity in response may provide a mechanism providing potential resilience to environmental change. Given that the challenges we faced in our study are likely to be common to many intensive data sets, the integrated modelling approach could be generally applicable and useful

Robust estimates of environmental effects on population vital rates:an integrated capture–recapture model of seasonal brook trout growth, survival and movement in a stream network

Modelling the effects of environmental change on populations is a key challenge for ecologists, particularly as the pace of change increases. Currently, modelling efforts are limited by difficulties in establishing robust relationships between environmental drivers and population responses.We developed an integrated capture–recapture state-space model to estimate the effects of two key environmental drivers (stream flow and temperature) on demographic rates (body growth, movement and survival) using a long-term (11 years), high-resolution (individually tagged, sampled seasonally) data set of brook trout (Salvelinus fontinalis) from four sites in a stream network. Our integrated model provides an effective context within which to estimate environmental driver effects because it takes full advantage of data by estimating (latent) state values for missing observations, because it propagates uncertainty among model components and because it accounts for the major demographic rates and interactions that contribute to annual survival.We found that stream flow and temperature had strong effects on brook trout demography. Some effects, such as reduction in survival associated with low stream flow and high temperature during the summer season, were consistent across sites and age classes, suggesting that they may serve as robust indicators of vulnerability to environmental change. Other survival effects varied across ages, sites and seasons, indicating that flow and temperature may not be the primary drivers of survival in those cases. Flow and temperature also affected body growth rates; these responses were consistent across sites but differed dramatically between age classes and seasons. Finally, we found that tributary and mainstem sites responded differently to variation in flow and temperature.Annual survival (combination of survival and body growth across seasons) was insensitive to body growth and was most sensitive to flow (positive) and temperature (negative) in the summer and fall.These observations, combined with our ability to estimate the occurrence, magnitude and direction of fish movement between these habitat types, indicated that heterogeneity in response may provide a mechanism providing potential resilience to environmental change. Given that the challenges we faced in our study are likely to be common to many intensive data sets, the integrated modelling approach could be generally applicable and useful