A spatially explicit population model of simulated fisheries impact on loggerhead sea turtles (Caretta caretta) in the Northwest Atlantic Ocean

Warden, Melissa L.; Haas, Heather L.; Rose, Kenneth A.; Richards, Paul M.

doi:10.1016/j.ecolmodel.2014.11.025

Cited by 17 publications

(15 citation statements)

References 37 publications

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“…This is especially true for species like sea turtles, which make use of both pelagic and benthic environments and have a high chance of interacting with fisheries (Lewison et al, 2004;Wallace et al, 2009;Warden et al, 2015). In the northwest Atlantic, loggerhead turtles are caught in gillnets, longlines, trawls and scallop dredges (Wallace et al, 2009;Murray, 2011).…”

Section: Introductionmentioning

confidence: 99%

Using a remotely operated vehicle (ROV) to observe loggerhead sea turtle (Caretta caretta) behavior on foraging grounds off the mid-Atlantic United States

Smolowitz

Patel

Haas

et al. 2015

Journal of Experimental Marine Biology and Ecology

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Keywords:In-water videography Cold water foraging Interspecies interactions Intraspecies interactions Baited remote underwater video system (BRUV) This study represents the first documented use of a remotely operated vehicle (ROV) to actively track sea turtles in situ. From 2008 to 2014, an ROV was deployed to track the at-sea behavior of loggerhead turtles in the Northwest Atlantic Ocean. Seventy turtles were tracked, totaling 44.7 h of direct turtle footage. For all attempts, usable video with a turtle retained in view for a minimum of 30 s, was produced at a rate of 43.5% of effort. Turtles were first spotted from the boat, and then when the turtle was within~50 m of the boat, the ROV was deployed to track the turtle for as long as possible. Tracking durations reached up to 426.1 min. Tracked turtles often remained within~10 m of the surface; however loggerheads were tracked to the seafloor on 12 occasions. Turtles were filmed foraging both pelagically and benthically, even though bottom temperatures reached as low as 7.1°C. A range of inter-and intra-species interactions were also captured. Several varieties of fish remained associated with individual turtles for extended periods of time, even during benthic foraging dives. Additionally, a variety of social interactions between loggerheads were documented. Generally these interactions were filmed occurring near the ocean surface. Overall, using the ROV provided great insight into loggerhead at-sea behavior, otherwise unattainable using previously established techniques.

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Section: Introductionmentioning

confidence: 99%

Using a remotely operated vehicle (ROV) to observe loggerhead sea turtle (Caretta caretta) behavior on foraging grounds off the mid-Atlantic United States

Smolowitz

Patel

Haas

et al. 2015

Journal of Experimental Marine Biology and Ecology

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“…; Warden et al . ), age and developmental stage (Caswell ; Caswell & Salguero‐Gómez ), age and frailty (Caswell ), age and parity (Caswell ), stage and infection status (Klepac & Caswell ; Metcalf et al . ) and combinations of stage and time or environmental state (Caswell , ; Hernandez‐Suarez, Rabinovich & Hernandez ; Perez‐Heydrich, Oli & Brown ).…”

Section: Introductionmentioning

confidence: 99%

“…A systematic approach to the construction and analysis of multistate models, using the vec-permutation matrix, was introduced by Hunter & Caswell (2005). The approach to multistate models has been applied to models in which individuals are classified by stage and location (Burns & Grear 2008;Tinker, Doak & Estes 2008;Ozgul et al 2009;Ezard et al 2010;Goldberg et al 2010;Strasser et al 2012;Flockhart et al 2015;Warden et al 2015), age and developmental stage (Caswell 2012;Caswell & Salguero-G omez 2013), age and frailty (Caswell 2014b), age and parity (Caswell 2014a), stage and infection status (Klepac & Caswell 2011;Metcalf et al 2012) and combinations of stage and time or environmental state (Caswell , 2006(Caswell , , 2011Hernandez-Suarez, Rabinovich & Hernandez 2012;Perez-Heydrich, Oli & Brown 2012). One case of the vec-permutation model yields the widely used 'megamatrix' model (e.g.…”

Section: Introductionmentioning

confidence: 99%

Hyperstate matrix models: extending demographic state spaces to higher dimensions

Roth

Caswell

2016

Methods Ecol Evol

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Summary1. Demographic models describe population dynamics in terms of the movement of individuals among states (e.g. size, age, developmental stage, parity, frailty, physiological condition). Matrix population models originally classified individuals by a single characteristic. This was enlarged to two characteristics in vec-permutation models and the closely related megamatrix models. However, it has been recognised that the interplay of more than two characteristics can affect the population dynamics. 2. Here, we present a framework, called hyperstate matrix model, in which individuals may be classified by any number of characteristics, using the generalisation of the vec-permutation approach to hypermatrices. These models are constructed from a simple block-diagonal matrix formulation of the movement of individuals among each dimension of the i-state. 3. This framework provides a step-by-step construction and makes available the usual demographic analysis developed for classical matrix models. In particular, we derive a general formula for the sensitivity of any output of the hyperstate matrix model, to any vector of parameters. In spite of the technicalities underlying these models, implementation is straightforward and we provide the MATLAB code to carry it out. 4. We apply this approach to a three-dimensional example in which individuals are classified by developmental stage, age and heterogeneity classes. The analysis of this model provides insights into how the heritability of the heterogeneity classes affects the long-term growth rate of the population. 5. As the questions in conservation biology become more sophisticated and data on threatened species become more detailed, multiple dimensions in demographic models will become increasingly important. Hyperstate matrix methods will make such analyses possible and directly applicable to conservation and population management.

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“…Ideally, we would want to use adult survival estimates that reflected conditions in the absence of anthropogenicrelated mortality Warden et al (2015). Ideally, we would want to use adult survival estimates that reflected conditions in the absence of anthropogenicrelated mortality Warden et al (2015).…”

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confidence: 99%

Life‐history constraints on maximum population growth for loggerhead turtles in the northwest Atlantic

Hatch

Haas

Richards

et al. 2019

Ecology and Evolution

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Conservation planning for protected species often relies on estimates of life‐history parameters. A commonly used parameter is the instantaneous maximum population growth rate (rmax) that can be used to limit removals and design recovery targets. Estimation of rmax can be challenging because of limited availability of species‐ and population‐specific data and life‐history information. We applied a method proposed by Neil and Lebreton, originally developed for birds, to loggerhead turtles. The method uses age‐at‐first‐reproduction and adult survival to estimate rmax. We used a variety of datasets and matrix population models to confirm an allometric assumption required by the method, and to generate estimates of age‐at‐first‐reproduction and adult survival. A meta‐analysis was applied to parameters from reported growth curves, which were then combined with the size distribution of neophyte nesters to derive estimates of age‐at‐first‐reproduction. Adult survival rates were obtained from an existing matrix population model. Monte Carlo simulation was then used to combine the estimates of the allometric coefficients, age‐at‐first‐reproduction, and adult survival to obtain a probability distribution of approximate rmax values. Estimated annual maximum population growth rates averaged 0.024, with a mode of 0.017 and a 95% highest density interval of 0.006–0.047. These estimates were similar to values reported by others using different methods and captured the variability in positive, annual change estimates across nesting beach sites for the northwest Atlantic loggerhead population. The use of life‐history parameters has a long history in wildlife and fisheries management and conservation planning. Our estimates of rmax, while having some biases and uncertainty, encompassed values presently used in recovery planning for loggerhead turtles and offer additional information for the management of endangered and threatened species.

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A spatially explicit population model of simulated fisheries impact on loggerhead sea turtles (Caretta caretta) in the Northwest Atlantic Ocean

Cited by 17 publications

References 37 publications

Using a remotely operated vehicle (ROV) to observe loggerhead sea turtle (Caretta caretta) behavior on foraging grounds off the mid-Atlantic United States

Using a remotely operated vehicle (ROV) to observe loggerhead sea turtle (Caretta caretta) behavior on foraging grounds off the mid-Atlantic United States

Hyperstate matrix models: extending demographic state spaces to higher dimensions

Life‐history constraints on maximum population growth for loggerhead turtles in the northwest Atlantic

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