Links in the trophic chain: modeling functional relationships between in situ oceanography, krill, and blue whale distribution under different oceanographic regimes
Abstract:The response of marine predators to global climate change and shifting ocean conditions is tightly linked with their environment and prey. Environmental data are frequently used as proxies for prey availability in marine predator distribution models, as the ephemeral nature of prey makes sampling difficult. For this reason, the functional, ecological links between environment, prey, and predator are rarely described or explicitly tested. We used 3 years of vessel-based whale survey data paired with oceanograph… Show more
“…larval recruitment), the peak in adult euphausiid densities and the peak in whale abundance 17 , 20 , 78 . Refining our understanding of how temporal lags relate chlorophyll- a to euphausiid spatial patterns and then to blue whale distribution remains a pending task 79 , 80 , especially considering that euphausiid spatial ecology in the NCP is poorly understood 49 , 81 .…”
Defining priority areas and risk evaluation is of utmost relevance for endangered species` conservation. For the blue whale (Balaenoptera musculus), we aim to assess environmental habitat selection drivers, priority areas for conservation and overlap with vessel traffic off northern Chilean Patagonia (NCP). For this, we implemented a single-step continuous-time correlated-random-walk model which accommodates observational error and movement parameters variation in relation to oceanographic variables. Spatially explicit predictions of whales’ behavioral responses were combined with density predictions from previous species distribution models (SDM) and vessel tracking data to estimate the relative probability of vessels encountering whales and identifying areas where interaction is likely to occur. These estimations were conducted independently for the aquaculture, transport, artisanal fishery, and industrial fishery fleets operating in NCP. Blue whale movement patterns strongly agreed with SDM results, reinforcing our knowledge regarding oceanographic habitat selection drivers. By combining movement and density modeling approaches we provide a stronger support for purported priority areas for blue whale conservation and how they overlap with the main vessel traffic corridor in the NCP. The aquaculture fleet was one order of magnitude larger than any other fleet, indicating it could play a decisive role in modulating potential negative vessel-whale interactions within NCP.
“…larval recruitment), the peak in adult euphausiid densities and the peak in whale abundance 17 , 20 , 78 . Refining our understanding of how temporal lags relate chlorophyll- a to euphausiid spatial patterns and then to blue whale distribution remains a pending task 79 , 80 , especially considering that euphausiid spatial ecology in the NCP is poorly understood 49 , 81 .…”
Defining priority areas and risk evaluation is of utmost relevance for endangered species` conservation. For the blue whale (Balaenoptera musculus), we aim to assess environmental habitat selection drivers, priority areas for conservation and overlap with vessel traffic off northern Chilean Patagonia (NCP). For this, we implemented a single-step continuous-time correlated-random-walk model which accommodates observational error and movement parameters variation in relation to oceanographic variables. Spatially explicit predictions of whales’ behavioral responses were combined with density predictions from previous species distribution models (SDM) and vessel tracking data to estimate the relative probability of vessels encountering whales and identifying areas where interaction is likely to occur. These estimations were conducted independently for the aquaculture, transport, artisanal fishery, and industrial fishery fleets operating in NCP. Blue whale movement patterns strongly agreed with SDM results, reinforcing our knowledge regarding oceanographic habitat selection drivers. By combining movement and density modeling approaches we provide a stronger support for purported priority areas for blue whale conservation and how they overlap with the main vessel traffic corridor in the NCP. The aquaculture fleet was one order of magnitude larger than any other fleet, indicating it could play a decisive role in modulating potential negative vessel-whale interactions within NCP.
“…Throughout the world blue whales ( Balaenoptera musculus ) rely on coastal upwelling systems for foraging, where elevated primary production and prey density supports energetic needs 17 – 21 . As the largest animal on earth, blue whales have intense energetic demands 22 , 23 , and are selective predators of krill 24 – 26 .…”
Section: Introductionmentioning
confidence: 99%
“…Physical properties of the water column structure in the Kahurangi upwelling system, including cooler surface temperatures and sub-surface water column features ( e.g. mixed layer depth, thermocline temperature, thermocline strength), lead to enhanced krill availability, and consequently increased foraging opportunities for blue whales in the summertime 21 , 44 . Figure 1 Map of the study region within the South Taranaki Bight (STB) of New Zealand, with location denoted by the white rectangle on inset map in the upper right panel.…”
Understanding relationships between physical drivers and biological response is central to advancing ecological knowledge. Wind is the physical forcing mechanism in coastal upwelling systems, however lags between wind input and biological responses are seldom quantified for marine predators. Lags were examined between wind at an upwelling source, decreased temperatures along the upwelling plume’s trajectory, and blue whale occurrence in New Zealand’s South Taranaki Bight region (STB). Wind speed and sea surface temperature (SST) were extracted for austral spring–summer months between 2009 and 2019. A hydrophone recorded blue whale vocalizations October 2016-March 2017. Timeseries cross-correlation analyses were conducted between wind speed, SST at different locations along the upwelling plume, and blue whale downswept vocalizations (D calls). Results document increasing lag times (0–2 weeks) between wind speed and SST consistent with the spatial progression of upwelling, culminating with increased D call density at the distal end of the plume three weeks after increased wind speeds at the upwelling source. Lag between wind events and blue whale aggregations (n = 34 aggregations 2013–2019) was 2.09 ± 0.43 weeks. Variation in lag was significantly related to the amount of wind over the preceding 30 days, which likely influences stratification. This study enhances knowledge of physical-biological coupling in upwelling ecosystems and enables improved forecasting of species distribution patterns for dynamic management.
“…Individual-specific information would aid in determining appropriate protective measures, if these were to be considered. Marine top predators, such as blue whales, can be considered ecosystem sentinels (Hazen et al, 2019) and their movements and distributions can be directly related to prey availability, often driven by physical oceanography, both on local and global scales (Ladd et al, 2019;Barlow et al, 2020). As such, movement data from individual animals may provide insight into whether dynamic protective measures that are responsive to external drivers would be more effective than static areas of protection (Hartel et al, 2015;Dwyer et al, 2020).…”
Effective management of wild animal populations relies on an understanding of their spatio-temporal distributions. Passive acoustic monitoring (PAM) is a non-invasive method to investigate the distribution of free-ranging species that reliably produce sound. Critically endangered Antarctic blue whales (Balaenoptera musculus intermedia) (ABWs) co-occur with pygmy blue whales (B. m. brevicauda) (PBWs) around New Zealand. Nationally, both are listed as “data deficient” due to difficulties in access and visual sub-species identification. PAM was used to investigate the distributions of blue whales via sub-species specific song detections in central New Zealand. Propagation models, incorporating ambient noise data, enabled the comparison of detections among recording locations in different marine environments. ABW detections peaked during austral winter and spring, indicating that New Zealand, and the South Taranaki Bight (STB) in particular, is a migratory corridor for ABWs. Some ABW calls were also detected during the breeding season (September and October). PBW calls were highly concentrated in the STB, particularly between March and May, suggesting that an aggregation of PBWs may occur here. Therefore, the STB is of great importance for both sub-species of blue whale. PBW detections were absent from the STB during parts of austral spring, but PBWs were detected at east coast locations during this time. Detection area models were valuable when interpreting and comparing detections among recording locations. The results provide sub-species specific information required for management of critically endangered ABWs and highlight the relative importance of central New Zealand for both sub-species of blue whale.
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