Dynamic occupancy modeling of temperate marine fish in area-based closures.

Jay Calvert,Chris Mcgonigle,Rory Quinn,Bradley Harris,Suresh Andrew Sethi,Jon Grabowski

doi:10.1002/ece3.4493

Abstract

Species distribution models (SDMs) are commonly used to model the spatial structure of species in the marine environment, however, most fail to account for detectability of the target species. This can result in underestimates of occupancy, where nondetection is conflated with absence. The site occupancy model (SOM) overcomes this failure by treating occupancy as a latent variable of the model and incorporates a detection submodel to account for variability in detection rates. These have rarely been applied in the context of marine fish and never for the multiseason dynamic occupancy model (DOM). In this study, a DOM is developed for a designated species of concern, cusk (Brosme brosme), over a four‐season period. Making novel use of a high‐resolution 3‐dimensional hydrodynamic model, detectability of cusk is considered as a function of current speed and algae cover. Algal cover on the seabed is measured from video surveys to divide the study area into two distinct regions: those with canopy forming species of algae and those without (henceforth bottom types). Modeled estimates of the proportion of sites occupied in each season are 0.88, 0.45, 0.74, and 0.83. These are significantly greater than the proportion of occupied sites measured from underwater video observations which are 0.57, 0.28, 0.43, and 0.57. Individual fish are detected more frequently with increasing current speed in areas lacking canopy and less frequently with increasing current speed in areas with canopy. The results indicate that, where possible, SDM studies for all marine species should take account of detectability to avoid underestimating the proportion of sites occupied at a given study area. Sampling closed areas or areas of conservation often requires the use of nonphysical, low impact sampling methods like camera surveys. These methods inherently result in detection probabilities less than one, an issue compounded by time‐varying features of the environment that are rarely accounted for marine studies. This work highlights the use of modeled hydrodynamics as a tool to correct some of this imbalance.

Highlights

The use of species distribution modelling as a tool for scientists and environmental managers has seen a substantial increase in the last three decades, driven by a growing demand for knowledge of species’ ranges and facilitated by increasingly powerful computing resources (Barbosa & Schneck2015)
This study takes a multifaceted approach to producing a dynamic occupancy model (DOM) for a species of temperate marine fish at a remote rocky outcrop in the central Gulf of Maine
The digital elevation model extracted from the multibeam echosounder (MBES) data was used for three purposes; 1) to derive terrain attributes to assess morphological complexity over the study site; 2) to create a surface of algae cover over the study site based on empirical extinction depths and; 3) as an input to a standalone hydrodynamic model

Summary

Introduction

The use of species distribution modelling as a tool for scientists and environmental managers has seen a substantial increase in the last three decades, driven by a growing demand for knowledge of species’ ranges and facilitated by increasingly powerful computing resources (Barbosa & Schneck2015). Given the time and cost of performing a systematic survey, especially in the marine environment, researchers often use archived datasets to perform modelling investigations In many cases, these datasets only contain records on species presence, resulting in a situation where no absence data are available (Elith et al 2011). These datasets only contain records on species presence, resulting in a situation where no absence data are available (Elith et al 2011) For this reason, presence-only models such as MaxEnt (Phillips et al 2006) have been developed that do not require absence data, but rather generate a large number of pseudo-absences from the study area

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