Predicted distributions and abundances of the sea turtle ‘lost years’ in the western North Atlantic Ocean

Nathan F Putman ,Wendy G Teas ,Beth Brost ,Erin E Seney ,Charles W Caillouet ,Raúl De Jesús González Díaz Mirón ,Miriam Tzeek ,Luis Jaime Peña ,Paul M Richards ,Jonathan A Ravell Ley ,Vicente Guzmán ,Donna J Shaver ,Benny J Gallaway ,Lyndsey N Howell ,Roldán A Valverde ,Mandy C Tumlin ,Eduardo Cuevas ,Cristóbal Cáceres G Cantón ,Phlippe Verley ,Melania C López‐Castro ,Matthew A Cook ,Simona A Ceriani ,Katherine Mansfield

doi:10.1111/ecog.04929

Abstract

Oceanic dispersal characterizes the early juvenile life‐stages of numerous marine species of conservation concern. This early stage may be a ‘critical period’ for many species, playing an overriding role in population dynamics. Often, relatively little information is available on their distribution during this period, limiting the effectiveness of efforts to understand environmental and anthropogenic impacts on these species. Here we present a simple model to predict annual variation in the distribution and abundance of oceanic‐stage juvenile sea turtles based on species’ reproductive output, movement and mortality. We simulated dispersal of 25 cohorts (1993–2017) of oceanic‐stage juveniles by tracking the movements of virtual hatchling sea turtles released in a hindcast ocean circulation model. We then used estimates of annual hatchling production from Kemp's ridley Lepidochelys kempii (n = 3), green Chelonia mydas (n = 8) and loggerhead Caretta caretta (n = 5) nesting areas in the northwestern Atlantic (inclusive of the Gulf of Mexico, Caribbean Sea and eastern seaboard of the U.S.) and their stage‐specific mortality rates to weight dispersal predictions. The model's predictions indicate spatial heterogeneity in turtle distribution across their marine range, identify locations of increasing turtle abundance (notably along the U.S. coast), and provide valuable context for temporal variation in the stranding of young sea turtles across the Gulf of Mexico. Further effort to collect demographic, distribution and behavioral data that refine, complement and extend the utility of this modeling approach for sea turtles and other dispersive marine taxa is warranted. Finally, generating these spatially‐explicit predictions of turtle abundance required extensive international collaboration among scientists; our findings indicate that continued conservation of these sea turtle populations and the management of the numerous anthropogenic activities that operate in the northwestern Atlantic Ocean will require similar international coordination.

Highlights

The distribution of a species is of fundamental biological importance because it shapes processes ranging from metabolic and growth rates of individuals to speciation of populations (Lomolino et al 2006)
We explicitly accounted for temporal variation in the reproductive output of each species’ nesting populations by 1) weighting transport predictions by estimates of annual hatchling production in each nesting region and 2) estimating stage-specific mortality. This allowed us to predict annual spatial variation in the numerical abundance of oceanic-stage sea turtles across the North Atlantic for more than two decades. We show how these predictions can be used to provide context for available distribution data in young sea turtles that recently recruited to coastal areas by comparing model predictions to long-term datasets on sea turtle strandings across the Gulf of Mexico
We examined changes in the distribution and densities of the oceanic-stage of three sea turtle species from 1996 through 2017 from major turtle nesting regions in the western North Atlantic (Kemp’s ridley n = 3, green n = 8 and loggerhead n = 5) (Fig. 1, Supplementary material Appendix 1 Table A1, A2)

Summary

Introduction

The distribution of a species is of fundamental biological importance because it shapes processes ranging from metabolic and growth rates of individuals to speciation of populations (Lomolino et al 2006). Concerns typically include 1) unreliable predictions when building models with a small number of observations (especially for species with a wide environmental niche or geographic range); 2) the ‘static’ nature of predicted distributions; and 3) the weak linkages between ecological theory (e.g. mechanisms that drive distribution) and the construction of species distribution models (Hernandez et al 2006, Elith and Leathwick 2009, Franklin 2010). Alternative approaches exist that focus on the mechanisms that influence distribution which, at a basic level, results from reproductive output, movement and mortality of individual organisms (Lomolino et al 2006). While movement is considered in mechanistic models (Cabral et al 2017), it is typically parameterized as some variation on a random walk and accounting for underlying processes is rare (Holyoak et al 2008, Merow et al 2011, Petrovskii and Petrovska 2012)

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