Integrating multiple data sources for assessing blue whale abundance and distribution in Chilean Northern Patagonia

Abstract

Aim Species distribution models are useful tools for depicting important habitat, assessing abundance and orienting conservation efforts. For small populations in poorly studied ecosystems, available data are often scarce and patchy. To overcome this limitation, we aim to evaluate the use of different data types within a hierarchical Bayesian framework with the goal of modelling the abundance and distribution of a small and highly migratory population of blue whale (BW, Balaenoptera musculus) summering in Chilean Northern Patagonian (CNP). Location CNP, Eastern South Pacific (ESP). Methods We constructed a Bayesian hierarchical species distribution Model (HSDM), combining a binomial N‐mixture model used to model BW groups counts in line‐transect data (2009, 2012 and 2014) with a logistic regression for modelling presence‐availability data (2009–2016), allowing both models to share covariate parameters for borrowing strength in estimations. Results Distance to areas of high chlorophyll‐a concentration during spring before summering season (AHCC‐s) was the most important and consistent explanatory variable for assessing BW abundance and distribution in CNP. Incorporating accessorial presence‐only data reduced uncertainty in parameters estimation when comparing with a model using only line‐transect data, although other covariates of secondary importance failed to be retained in this model. Main conclusions Our results remark the capability of HSDM for integrating different data types providing a potential powerful tool when data are limited and heterogeneous. Results indicate that AHCC‐s, and possibly thermal fronts, could modulate BW abundance and distribution patterns in CNP. Preliminary model‐based delimitations of possible priority conservation areas for BW in CNP overlap with highly used vessel navigation routes and areas destined to aquaculture.

Defining priority areas for blue whale conservation and investigating overlap with vessel traffic in Chilean Patagonia, using a fast-fitting movement model

“…Previous studies on blue whale occurrence and movement patterns indicated that until the onset of austral autumn/winter migration, blue whales focus most of their activities within these productive coastal waters 12 – 15 . However, variations in how this population utilizes this region and other areas within the ESP appear to result from changes in prevailing oceanographic conditions 16 .…”

“…Species distribution models (SDM) have shown that austral spring chlorophyll- a concentration, prior to the whales’ arrival, and thermal fronts are important oceanographic proxies for describing the abundance and distribution patterns of blue whales within the NCP 16 . Krill, the primary prey of blue whales 17 , can take advantage of seasonally enhanced productivity for biomass production, with some time lag linking early life-history stages ( e.g.…”

“…Potential local threats affecting blue whales in NCP include collisions with vessels due to intense maritime traffic 16 , 32 , negative interactions with aquaculture and fisheries activities 33 – 35 , direct and indirect effects from poorly regulated whale-watching operations 36 , and general disturbance from noise and acoustic pollution 37 . As such, identifying priority areas for focusing conservation actions is of utmost relevance considering a population numbering the low hundreds with a very low potential biological removal from anthropogenic origin estimated at 1 individual every 1.8 years 16 for continued growth.…”

Environmental issues in Chilean salmon farming: a review

“…It is interesting to note that research on marine mammals in Patagonia should become more important for aquaculture activities in Chile because a new US trade rule requires countries to demonstrate that their fishery and aquaculture activities are equivalent in effectiveness to the US Marine Mammal Protection Act or risk losing the permit to export seafood products to the US market (Williams et al 2016;Bedriñana-Romano et al 2018).…”

Aerial-trained deep learning networks for surveying cetaceans from satellite imagery

“…There is tremendous interest in understanding if and how cetacean populations are recovering following the cessation of intense commercial whaling, yet their extensive ranges and high level of mobility combine with the challenges of research at sea to leave them poorly studied in many regions. While many cetacean species favor shelf edges and other zones of deep-water upwelling across ocean basins [1–4], most cetacean research is focused on coastal areas where populations may be concentrated at key times of the year and are logistically easier to survey. Far less work has been done to understand patterns of cetacean habitat use along distant continental shelf regions, and limited cetacean surveys in deep-water habitat may skew our understanding about preferred habitat [5].…”