Using Delta‐Generalized Additive Models to Predict Spatial Distributions and Population Abundance of Juvenile Pink Shrimp in Tampa Bay, Florida

Abstract

In this article, we present an approach based on generalized additive models (GAMs) to predict species’ distributions and abundance in Florida estuaries with habitat suitability modeling. Environmental data gathered by fisheries‐independent monitoring in Tampa Bay from 1998 to 2008 were interpolated to create seasonal habitat maps for temperature, salinity, and dissolved oxygen and annual maps for depth and bottom type. We used delta‐GAM models assuming either zero‐adjusted gamma or beta‐inflated‐at‐zero distributions to predict catch per unit effort (CPUE) from five habitat variables plus gear type for each estuarine species by life stage and season. Bottom type and gear type were treated as categorical predictors with reference parameterization. Three spline‐fitting procedures (the penalized B‐spline, cubic smoothing spline, and restricted cubic spline) were applied to the continuous predictors. Two additive, linear submodels on the log and logistic scales were used to predict CPUEs >0 and CPUEs = 0, respectively, across environmental gradients. The best overall model among those estimated was identified based on the lowest Akaike information criterion. A stepwise routine was used to omit continuous predictors that had little predictive power. The model developed was then applied to interpolated habitat data to predict CPUEs across the estuary using GIS. The statistical models, coupled with the use of GIS, provide a method for predicting spatial distributions and population numbers of estuarine species’ life stages. An example is presented for juvenile pink shrimp Farfantepenaeus duorarum during the summer in Tampa Bay, Florida. Received February 10, 2015; accepted August 11, 2015

Modeling and Mapping to Assess Spatial Distributions and Population Numbers of Fish and Invertebrate Species in the Lower Peace River and Charlotte Harbor, Florida

“…Equal areas (EA) for all of the HSM zones worked well (Rubec et al. ). However, in most cases the Optimum zone filled the rivers and extended into the estuary.…”

“…Habitat suitability modeling methods were recently developed using delta‐gamma GAMs linked to habitat grids that facilitate the spatial estimation of population numbers of species in estuaries (Rubec et al. ). An example describing the analytical methods was presented for juvenile pink shrimp Farfantepenaeu s duorarum in Tampa Bay in summer.…”

“…We are not aware of any published studies (other than Rubec et al. ) that have derived population number estimates from the predicted CPUE grids used to create HSM maps.…”

“…The zero-inflated nature of FIM catch data can bias catch rate estimates derived from simple statistical approaches-hence the present use of more complex deltatype generalized additive models (GAMs; Rigby and Stasinopoulos 2005;Stasinopoulos and Rigby 2007). Habitat suitability modeling methods were recently developed using delta-gamma GAMs linked to habitat grids that facilitate the spatial estimation of population numbers of species in estuaries (Rubec et al 2016a). An example describing the analytical methods was presented for juvenile pink shrimp Farfantepenaeus duorarum in Tampa Bay in summer.…”

Introduction to a Special Section: Spatial Analysis, Mapping, and Management of Marine Fisheries

“…Analytical tools such as delta-BRT are now available to support the spatial management of marine-estuarine species. Rubec et al (2016a) developed spatial models for 87 species life stages in Tampa Bay capable of dealing with FIM sampling data with an excess of zero catches. An analysis of juvenile pink shrimp summer data is presented as an example of the methods applied.…”