Projecting species distributions using fishery‐dependent data

Abstract

Many marine species are shifting their distributions in response to changing ocean conditions, posing significant challenges and risks for fisheries management. Species distribution models (SDMs) are used to project future species distributions in the face of a changing climate. Information to fit SDMs generally comes from two main sources: fishery‐independent (scientific surveys) and fishery‐dependent (commercial catch) data. A concern with fishery‐dependent data is that fishing locations are not independent of the underlying species abundance, potentially biasing predictions of species distributions. However, resources for fishery‐independent surveys are increasingly limited; therefore, it is critical we understand the strengths and limitations of SDMs developed from fishery‐dependent data. We used a simulation approach to evaluate the potential for fishery‐dependent data to inform SDMs and abundance estimates and quantify the bias resulting from different fishery‐dependent sampling scenarios in the California Current System (CCS). We then evaluated the ability of the SDMs to project changes in the spatial distribution of species over time and compare the time scale over which model performance degrades between the different sampling scenarios and as a function of climate bias and novelty. Our results show that data generated from fishery‐dependent sampling can still result in SDMs with high predictive skill several decades into the future, given specific forms of preferential sampling which result in low climate bias and novelty. Therefore, fishery‐dependent data may be able to supplement information from surveys that are reduced or eliminated for budgetary reasons to project species distributions into the future.

Building use‐inspired species distribution models: Using multiple data types to examine and improve model performance

“…In contrast, the fishery‐independent models exhibited generally lower evaluation metrics but were more broadly robust in their predictive performance and ecological realism, suggesting they may more accurately represent the realized environmental niche and geographic distribution of blue sharks beyond the footprint of the fishery. This distinction regarding the relative strengths of different data types may have even greater relevance for model projections to understand how species' distributions and their interactions with fisheries may shift under climate change (Karp et al, 2023).…”

“…While previous studies have suggested that fisherydependent and fishery-independent datasets can lead to consistent estimates of species' habitats (Karp et al, 2023;Pennino et al, 2016) F I G U R E 6 Proportion of presences (sensitivity, a) and "true" absences from the observer data (specificity, b) correctly predicted by each selected model (Table 4) and dataset combination. Model predictions were considered "correct" when predicted suitability was greater than the 75% quantile for presence observations and less than the 25% quantile for absences in the observer data.…”

A will-o’-the wisp? On the utility of voluntary contributions of data and knowledge from the fishing industry to marine science

“…Finally, there are continuity issues in scientific data collection in relation to a changing world. For example, in the context of climate change, scientific surveys that are standardized to allow for time-series development of relative changes in fish stock populations may miss important changes in stock dynamics (Karp et al, 2022). Here, fishers'…”

“…What makes 'anecdotal' information considered to be less true useful for monitoring change is not necessarily that it is less true, but that it is regarded as not 'systematic' (Wilson, 2009). For example, stock assessment science tends to be based on large spatial scale units, discrete sampling techniques, and standardized sampling protocols, whereas experiential knowledge is often more localized and is based on different and often variable temporal scales and continuous sampling practices and technologies (Perry and Ommer, 2003;Wilson, 2009;Karp et al, 2022). These are some of the reasons why experiential knowledge is often considered unusable in fish stock or ecological assessment models; particularly those that are already data-rich (Mackinson and Nøttestad, 1998).…”

Capturing big fisheries data: Integrating fishers’ knowledge in a web-based decision support tool

“…Given the multitude benefits of using fishers' knowledge to inform policy, it begs the question why it's underutilized? For catch data, there is the issue of bias in samples for density estimates, as catch logs exclusively record instances of fishing activity, neglecting areas not targeted by fishers, which biases predictions of species distributions (Karp et al, 2022). Also, given the unsystematic way much of fishers' knowledge is handled, it is often neglected (Hind, 2015).…”