Fathoming sea turtles: monitoring strategy evaluation to improve conservation status assessments

Abstract

Population monitoring must be accurate and reliable to correctly classify population status. For sea turtles, nesting beach surveys are often the only population‐level surveys that are accessible. However, process and observation errors, compounded by delayed maturity, obscure the relationship between trends on the nesting beach and the population. We present a simulation‐based tool, monitoring strategy evaluation (MoSE), to test the relationships between monitoring data and assessment accuracy, using green sea turtles, Chelonia mydas, as a case study. To explore this first application of MoSE, we apply different treatments of population impacts to virtual true populations, and sample the nests or nesters, with observation error, to test if the observation data can be used to diagnose population status accurately. Based on the observed data, we examine population trend and compare it to the known values from the operating model. We ran a series of scenarios including harvest impacts, cyclical breeding probability, and sampling biases, to see how these factors impact accuracy in estimating population trend. We explored the necessary duration of monitoring for accurate trend estimation and the probability of a false trend diagnosis. Our results suggest that disturbance type and severity can have important and persistent effects on the accuracy of population assessments drawn from monitoring nesting beaches. The underlying population phase, age classes disturbed, and impact severity influenced the accuracy of estimating population trend. At least 10 yr of monitoring data is necessary to estimate population trend accurately, and >20 yr if juvenile age classes were disturbed and the population is recovering. In general, there is a greater probability of making a false positive trend diagnosis than a false negative, but this depends on impact type and severity, population phase, and sampling duration. Improving detection rates to 90% does little to lower probability of a false trend diagnosis with shorter monitoring spans. Altogether, monitoring strategies for specific populations may be tailored based on the impact history, population phase, and environmental drivers. The MoSE is an important framework for analysis through simulation that can comprehensively test population assessments for accuracy and inform policy recommendations regarding the best monitoring strategies.

Missing Data in Sea Turtle Population Monitoring: A Bayesian Statistical Framework Accounting for Incomplete Sampling

“…Status assessments are fundamental to species conservation, however, they are often hindered by a lack of long-term spatial and temporal data (Nichols and Williams, 2006;Jackson et al, 2008;Sims et al, 2008;Piacenza et al, 2019;Whiting et al, 2020). In the case of sea turtles, ground-derived counts of the number of clutches laid in a nesting season are most often used to derive population trends and abundance (Witherington et al, 2009;Stewart et al, 2011;Stokes et al, 2014;Omeyer et al, 2021), although such estimates are prone to variability in sampling effort within and among years and sites.…”

“…Obtaining complete nesting censuses for sea turtles is often challenging, resulting in spatial and temporal variation in monitoring efforts, which can hinder effective status assessments (Nichols and Williams, 2006;Jackson et al, 2008;Sims et al, 2008;Piacenza et al, 2019;Whiting et al, 2020). The observed variability in monitoring efficacy is most commonly due to the availability of sustained funding, as well as the logistical and financial constraints associated with undertaking comprehensive censuses over many months (Miller, 1997), at monitoring sites that are often located in remote and inaccessible areas.…”

“…Research over the last few decades has revealed that the Atlantic coast of Africa is a globally important region for sea turtles (Fretey, 2001;Formia et al, 2003), hosting several major nesting aggregations of leatherback (Dermochelys coriacea; Witt et al, 2009), olive ridley (Lepidochelys olivacea; Metcalfe et al, 2015), and green turtles (Chelonia mydas; Honarvar et al, 2016;Patrıćio et al, 2017). Despite this awareness, the status of documented rookeries across this region (with the exception of Guinea Bissau and Prıńcipe) remain classified as 'Data Deficient' (Broderick and Patrıćio, 2019) because a minimum of 10 years of data are necessary to accurately estimate population trends for assessment (Piacenza et al, 2019).…”

When can model‐based estimates replace surveys of wildlife populations that span many discrete management units?

“…In line with an interval‐based approach, if units are not spatially correlated and vary in population dynamics, it is typically better to sample more units less frequently (Rhodes & Jonzén, 2011). Time series length is also an important contributor to trend detection, and longer periods of monitoring will improve accuracy in detecting rates of population change through time (Ahrestani et al., 2013; Piacenza et al., 2019; Reynolds et al., 2011; Vallecillo et al., 2021). Additionally, larger population changes in a consistent direction (e.g.…”

Warm beach, warmer turtles: Using drone-mounted thermal infrared sensors to monitor sea turtle nesting activity

“…These projects monitor over 3,200 nesting beaches globally, with over 60 beaches to protect and survey sea turtle nesting beaches in Costa Rica alone (Fonseca et al, 2015; The State of the World's Sea Turtles [SWOT], 2022). This long-term, global effort has enabled scientists to assess population trends of many species, provided evidence of recovering populations, and elucidated challenges (Mazaris et al, 2017;Piacenza et al, 2019;Godley et al, 2020;Mortimer et al, 2020). These efforts, however, rely upon traditional laborintensive human patrollers, counting females, their nests, and/or tracks at night, usually relying upon volunteers, students, or young field scientists to gather the data (Shanker et al, 2003;Chacoń et al, 2007;Quesada-Rodrıǵuez et al, 2021).…”