Research on cetacean foraging ecology is central to our understanding of their spatial and behavioral ecology. Yet, functional mechanisms by which cetaceans detect prey across different scales remain ...unclear. Here, I postulate that cetaceans utilize a scale‐dependent, multimodal sensory system to assess and increase prey encounters. I review the literature on cetacean sensory systems related to foraging ecology, and hypothesize the effective scales of each sensory modality to inform foraging opportunities. Next, I build two “scale‐of‐senses” schematics for the general groups of dolphins and baleen whales. These schematics illustrate the hypothetical interchange of sensory modalities used to locate and discriminate prey at spatial scales ranging from 0 m to 1,000 km: (1) vision, (2) audition (sound production and sound reception), (3) chemoreception, (4) magnetoreception, and somatosensory perception of (5) prey, or (6) oceanographic stimuli. The schematics illustrate how a cetacean may integrate sensory modalities to form an adaptive foraging landscape as a function of distance to prey. The scale‐of‐senses schematic is flexible, allowing for case‐specific application and enhancement with improved cetacean sensory data. The framework serves to improve our understanding of functional cetacean foraging ecology, and to develop new hypotheses, methods, and results regarding how cetaceans forage at multiple scales.
Kelp forest trophic cascades have been extensively researched, yet indirect effects to the zooplankton prey base and gray whales have not been explored. We investigate the correlative patterns of a ...trophic cascade between bull kelp and purple sea urchins on gray whales and zooplankton in Oregon, USA. Using generalized additive models (GAMs), we assess (1) temporal dynamics of the four species across 8 years, and (2) possible trophic paths from urchins to kelp, kelp as habitat to zooplankton, and kelp and zooplankton to gray whales. Temporal GAMs revealed an increase in urchin coverage, with simultaneous decline in kelp condition, zooplankton abundance and gray whale foraging time. Trophic path GAMs, which tested for correlations between species, demonstrated that urchins and kelp were negatively correlated, while kelp and zooplankton were positively correlated. Gray whales showed nuanced and site-specific correlations with zooplankton in one site, and positive correlations with kelp condition in both sites. The negative correlation between the kelp-urchin trophic cascade and zooplankton resulted in a reduced prey base for gray whales. This research provides a new perspective on the vital role kelp forests may play across multiple trophic levels and interspecies linkages.
Finding the right fit Derville, Solene; Torres, Leigh G.; Iovan, Corina ...
Diversity & distributions,
November 2018, Letnik:
24, Številka:
11
Journal Article
Recenzirano
Odprti dostop
Aim
Accurate predictions of cetacean distributions are essential to their conservation but are limited by statistical challenges and a paucity of data. This study aimed at comparing the capacity of ...various statistical algorithms to deal with biases commonly found in nonsystematic cetacean surveys and to evaluate the potential for citizen science data to improve habitat modelling and predictions. An endangered population of humpback whales (Megaptera novaeangliae) in their breeding ground was used as a case study.
Location
New Caledonia, Oceania.
Methods
Five statistical algorithms were used to model the habitat preferences of humpback whales from 1,360 sightings collected over 14 years of nonsystematic research surveys. Three different background sampling approaches were tested when developing models from 625 crowdsourced sightings to assess methods accounting for citizen science spatial sampling bias. Model evaluation was conducted through cross‐validation and prediction to an independent satellite tracking dataset.
Results
Algorithms differed in complexity of the environmental relationships modelled, ecological interpretability and transferability. While parameter tuning had a great effect on model performances, GLMs generally had low predictive performance, SVMs were particularly hard to interpret, and BRTs had high descriptive power but showed signs of overfitting. MAXENT and especially GAMs provided a valuable complexity trade‐off, accurate predictions and were ecologically intelligible. Models showed that humpback whales favoured cool (22–23°C) and shallow waters (0–100 m deep) in coastal as well as offshore areas. Citizen science models converged with research survey models, specifically when accounting for spatial sampling bias.
Main conclusions
Marine megafauna distribution models present specific challenges that may be addressed through integrative evaluation, independent testing and appropriately tuned statistical algorithms. Specifically, controlling overfitting is a priority when predicting cetacean distributions for large‐scale conservation perspectives. Citizen science data appear to be a powerful tool to describe cetacean habitat.
The nearshore waters of the Northern California Current support an important seasonal foraging ground for Pacific Coast Feeding Group (PCFG) gray whales. We examine gray whale distribution, habitat ...use, and abundance over 31 years (1992-2022) using standardized nearshore (< 5 km from shore) surveys spanning a large swath of the PCFG foraging range. Specifically, we generated density surface models, which incorporate detection probability into generalized additive models to assess environmental correlates of gray whale distribution and predict abundance over time. We illustrate the importance of coastal upwelling dynamics, whereby increased upwelling only yields higher gray whale density if interspersed with relaxation events, likely because this combination optimizes influx and retention of nutrients to support recruitment and aggregation of gray whale prey. Several habitat features influence gray whale distribution, including substrate, shelf width, prominent capes, and river estuaries. However, the influence of these features differs between regions, revealing heterogeneity in habitat preferences throughout the PCFG foraging range. Predicted gray whale abundance fluctuated throughout our study period, but without clear directional trends, unlike previous abundance estimates based on mark-recapture models. This study highlights the value of long-term monitoring, shedding light on the impacts of variable environmental conditions on an iconic nearshore marine predator.
Resources in the ocean are ephemeral, and effective management must therefore account for the dynamic spatial and temporal patterns of ecosystems and species of concern. We focus on the South ...Taranaki Bight (STB) of New Zealand, where upwelling generates productivity and prey to support an important foraging ground for blue whales that overlaps with anthropogenic pressure from industrial activities.
We incorporate regional ecological knowledge of upwelling dynamics, physical–biological coupling and associated lags in models to forecast sea surface temperature (SST) and net primary productivity (NPP) with up to 3 weeks lead time. Forecasted environmental layers are then implemented in species distribution models to predict suitable blue whale habitat in the STB. Models were calibrated using data from the austral summers of 2009–2019, and ecological forecast skill was evaluated by predicting to withheld data.
Boosted regression tree models skilfully forecasted SST (CV deviance explained = 0.969–0.970) and NPP (CV deviance explained = 0.738–0.824). The subsequent blue whale distribution forecast models had high predictive performance (AUC = 0.889), effectively forecasting suitable habitat on a daily scale with 1–3 weeks lead time.
The spatial location and extent of forecasted blue whale habitat were variable, with the proportion of petroleum and mineral permit areas that overlapped with daily suitable habitat ranging from 0% to 70%. Hence, the STB and these forecast models are well‐suited for dynamic management that could reduce anthropogenic threats to whales while decreasing regulatory burdens to industry users relative to a traditional static protected area.
Synthesis and applications. We develop and test ecological forecast models that predict sea surface temperature, net primary productivity and blue whale suitable habitat up to 3 weeks in the future within New Zealand's South Taranaki Bight region. These forecasts of whale distribution can be effectively applied for dynamic spatial management due to model foundation on quantified links and lags between physical forcing and biological responses. A framework to operationalize these forecasts through a user‐driven application is in development to proactively inform conservation management decisions. This framework is implemented through stakeholder engagement, allows flexibility based on management objectives, and is amenable to improvement as new knowledge and feedback are received.
We develop and test ecological forecast models that predict sea surface temperature, net primary productivity and blue whale suitable habitat up to 3 weeks in the future within New Zealand's South Taranaki Bight region. These forecasts of whale distribution can be effectively applied for dynamic spatial management due to model foundation on quantified links and lags between physical forcing and biological responses. A framework to operationalize these forecasts through a user‐driven application is in development to proactively inform conservation management decisions. This framework is implemented through stakeholder engagement, allows flexibility based on management objectives, and is amenable to improvement as new knowledge and feedback are received.
Identification and classification of behavior states in animal movement data can be complex, temporally biased, time-intensive, scale-dependent, and unstandardized across studies and taxa. Large ...movement datasets are increasingly common and there is a need for efficient methods of data exploration that adjust to the individual variability of each track. We present the Residence in Space and Time (RST) method to classify behavior patterns in movement data based on the concept that behavior states can be partitioned by the amount of space and time occupied in an area of constant scale. Using normalized values of Residence Time and Residence Distance within a constant search radius, RST is able to differentiate behavior patterns that are time-intensive (e.g., rest), time & distance-intensive (e.g., area restricted search), and transit (short time and distance). We use grey-headed albatross (Thalassarche chrysostoma) GPS tracks to demonstrate RST's ability to classify behavior patterns and adjust to the inherent scale and individuality of each track. Next, we evaluate RST's ability to discriminate between behavior states relative to other classical movement metrics. We then temporally sub-sample albatross track data to illustrate RST's response to less resolved data. Finally, we evaluate RST's performance using datasets from four taxa with diverse ecology, functional scales, ecosystems, and data-types. We conclude that RST is a robust, rapid, and flexible method for detailed exploratory analysis and meta-analyses of behavioral states in animal movement data based on its ability to integrate distance and time measurements into one descriptive metric of behavior groupings. Given the increasing amount of animal movement data collected, it is timely and useful to implement a consistent metric of behavior classification to enable efficient and comparative analyses. Overall, the application of RST to objectively explore and compare behavior patterns in movement data can enhance our fine- and broad- scale understanding of animal movement ecology.
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.
Species distribution models (SDMs) are increasingly applied in conservation management to predict suitable habitat for poorly known populations. High predictive performance of SDMs is evident in ...validations performed within the model calibration area (interpolation), but few studies have assessed SDM transferability to novel areas (extrapolation), particularly across large spatial scales or pelagic ecosystems. We performed rigorous SDM validation tests on distribution data from three populations of a long-ranging marine predator, the grey petrel Procellaria cinerea, to assess model transferability across the Southern Hemisphere (25-65°S). Oceanographic data were combined with tracks of grey petrels from two remote sub-Antarctic islands (Antipodes and Kerguelen) using boosted regression trees to generate three SDMs: one for each island population, and a combined model. The predictive performance of these models was assessed using withheld tracking data from within the model calibration areas (interpolation), and from a third population, Marion Island (extrapolation). Predictive performance was assessed using k-fold cross validation and point biserial correlation. The two population-specific SDMs included the same predictor variables and suggested birds responded to the same broad-scale oceanographic influences. However, all model validation tests, including of the combined model, determined strong interpolation but weak extrapolation capabilities. These results indicate that habitat use reflects both its availability and bird preferences, such that the realized distribution patterns differ for each population. The spatial predictions by the three SDMs were compared with tracking data and fishing effort to demonstrate the conservation pitfalls of extrapolating SDMs outside calibration regions. This exercise revealed that SDM predictions would have led to an underestimate of overlap with fishing effort and potentially misinformed bycatch mitigation efforts. Although SDMs can elucidate potential distribution patterns relative to large-scale climatic and oceanographic conditions, knowledge of local habitat availability and preferences is necessary to understand and successfully predict region-specific realized distribution patterns.
From our traditional boat-based horizontal perspective, marine megafauna behavioral observations are typically limited to animal surfacings. Achieving an aerial perspective has been restricted to ...brief helicopter or airplane based observations that are costly, noisy and risky. The emergence of commercial small unmanned aerial systems (UAS) has significantly reduced these constraints, and provide a stable, relatively quiet and inexpensive platform that enables replicate observations for prolonged periods with minimal disturbance. The potential of UAS for behavioral observation appears immense, yet quantitative methods of video analysis and proof of utility as an observational tool are required. We use UAS footage of gray whales foraging in coastal waters of Oregon, USA to develop analysis methods, assess behavioral impacts caused by UAS, determine the change in observation time enabled by UAS, and describe unique behaviors observed via UAS. Boat-based behavioral observations from 53 gray whale sightings between May and October 2016 were compared to behavioral data extracted from video analysis of UAS flights during those sightings. We used a DJI Phantom 3 Pro or 4 Advanced, recorded video from an altitude ≥25m, and detected no behavioral response by whales to the UAS. Two experienced whale ethologists conducted UAS video behavioral analysis, including tabulation of whale behavior states and events, and whale surface time and whale visible time (total time the whale was visible including underwater). UAS provided three times more observational capacity than boat-based observations alone (300 vs. 103 minutes). When observation time is accounted for, UAS data provided greater observations of all primary behavior states (travel, forage, social, rest) relative to boat-based data, especially forage, which increased by three times. Furthermore, UAS enable documentation of multiple novel gray whale foraging tactics (e.g., headstands: n=58; side-swimming: n=17; jaw snapping and flexing: n= 10) and 33 social events (nursing, pair coordinated surfacings) not identified in the field. This study demonstrates the significant added value of UAS to marine megafauna behavior and ecological studies. With technological advances, robust study designs, and effective analytical tools, we foresee increased UAS applications to marine megafauna studies to elucidate foraging strategies, habitat associations, social patterns, and response to human disturbance.
Aim
Large marine predators, such as cetaceans and sharks, play a crucial role in maintaining biodiversity patterns and ecosystem function, yet few estimates of their spatial distribution exist. We ...aimed to determine the species richness of large marine predators and investigate their fine‐scale spatiotemporal distribution patterns to inform conservation management.
Location
The Hauraki Gulf/Tīkapa Moana/Te Moananui‐ā‐Toi, Aotearoa/New Zealand.
Methods
We conducted a replicate systematic aerial survey over 12 months. Flexible machine learning models were used to explore relationships between large marine predator occurrence (Bryde's whales, common and bottlenose dolphins, bronze whaler, pelagic and immature hammerhead sharks) and environmental and biotic variables, and predict their monthly distribution and associated spatially explicit uncertainty.
Results
We revealed that temporally dynamic variables, such as prey distribution and sea surface temperature, were important for predicting the occurrence of the study species and species groups. While there was variation in temporal and spatial distribution, predicted richness peaked in summer and was the highest in coastal habitats during that time, providing insight into changes in distributions over time and between species.
Main Conclusions
Temporal changes in distribution are not routinely accounted for in species distribution studies. Our approach highlights the value of multispecies surveys and the importance of considering temporally variable abiotic and biotic drivers for understanding biodiversity patterns when informing ecosystem‐scale conservation planning and dynamic ocean management.
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