Contrasting climate velocity impacts in warm and cool locations show that effects of marine warming are worse in already warmer temperate waters

Abstract

Warming worse in already warmer temperate waters: a meta-analysis of the impact of climate trends and velocities on species of demersal marine shes Impacts of climate velocity on demersal sh biomass depend on initial climate conditions: a metaanalysis across species in the northeast Pacic

The shadow model: how and why small choices in spatially explicit species distribution models affect predictions

“…For our sablefish case study, we used the latter approach for all model fitting ( ). This package is useful for large and complex datasets that can be computationally taxing ( Anderson et al, 2020 ), it allows for many different response distributions, and optionally includes spatially ( e.g ., Barnett, Ward & Anderson, 2021 ) and temporally ( e.g ., English et al, 2022 ) varying coefficients. While not thoroughly explored in this paper, practitioners could use model selection tools to compare alternative models ( e.g ., with or without spatiotemporal processes, time-varying depth effect, etc .).…”

“…Data with zeros and positive continuous values can be represented with a single distribution, such as the Tweedie distribution ( Dunn & Smyth, 2005 , 2008 ), or the model may separately estimate effects on the probability of occurrence and density in a delta- or hurdle framework ( Pennington, 1983 ). For our SDMs developed for the sablefish case study, we used a Tweedie distribution ( Dunn & Smyth, 2005 , 2008 ) to account for zeros in our continuous response variable (catch per unit effort, CPUE, measured in kg per km 2 swept by the sampling gear) since it has been shown to perform well in previous applications to such data ( Anderson, Keppel & Edwards, 2019 ; Barnett, Ward & Anderson, 2021 ; Thorson et al, 2021 ; English et al, 2022 ). While the majority of hurdle models construct separate spatial fields for presence-absence and positive biomass density ( i.e ., processes assumed to be independent), an advantage of using the Tweedie distribution is that it involves modeling a single spatial field (processes dependent), simplifying interpretation (as an example of sharing elements across fields, see Martínez-Minaya et al, 2018 ).…”

“…As a demonstration of evaluating model structure in the sablefish case study, we performed a model comparison among four predictive process SDMs with different spatiotemporal processes. These models included (1) a model with only a spatial component and a covariate of depth, (2) spatiotemporal components that were independent and identically distributed over time , (3) spatiotemporal effects modeled as an AR(1) process (see equations 10 and 11 in English et al, 2022 ), and (4) only a spatial component, without a depth covariate. To compare the out-of-sample predictive ability of these candidate models, we used the 5-fold cross validation procedure described above and calculated the predictive log-density for each model with the Tweedie likelihood.…”

sdmTMB: An R Package for Fast, Flexible, and User-Friendly Generalized Linear Mixed Effects Models with Spatial and Spatiotemporal Random Fields

“…or in situations up to mid-resolution meshes (~500 knots), which in practice can cover fairly complex spatial processes on large datasets (e.g., Maureaud et al 2021;English et al 2022).…”

“…Speed-wise, sdmTMB (and by association VAST) were fastest up to at least 1000 mesh nodes at approximately Spatially and spatiotemporally explicit data are increasingly collected in ecology and have the power to reveal new ecological processes (e.g., Dinnage et al 2020;English et al 2022) and improve ecological management (Sofaer et al 2019). These data present statistical challenges to modelling them effectively and efficiently since appropriate models such as GLMMs with random fields are often computationally intensive and challenging to implement, interpret, and evaluate.…”