Multiscale relationships between humpback whales and forage species hotspots within a large marine ecosystem

Fluctuations in prey abundance, composition, and distribution can impact predators, and when predators and fisheries target the same species, predators become essential to ecosystem-based management. Because of the difficulty in collecting concomitant predator-prey data at appropriate scales in patchy environments, few studies have identified strong linkages between cetaceans and prey, especially across large geographic areas. During summer 2018, a line-transect survey for cetaceans and coastal pelagic species was conducted over the continental shelf and slope of British Columbia, Canada, and the US West Coast, allowing for a large-scale investigation of predator-prey spatial relationships. We report on a case study of humpback whales (Megaptera novaeangliae) and their primary prey-Pacific herring (Clupea pallasii), northern anchovy (Engraulis mordax), and krill-using generalized additive models to explore the relationships between whale abundance on 10-km transect segments and prey metrics. Prey metrics included direct measures of biomass densities on segments and an original hotspot metric. For each prey species, segments in the upper fifth percentile for biomass density (across all segments) were designated hotspots, and whale counts on a segment were evaluated for their relationship to number of hotspot segments (species-specific and multispecies) within 25, 50, or 100 km. Whale abundance was not strongly related to direct measures of biomass densities, whereas models using hotspot metrics were more effective at describing variation in whale abundance, underscoring that evaluating prey at relevant and measurable scales is critical in patchy, dynamic marine environments. Our analysis highlighted differences in the distribution and prey availability for three humpback whale distinct population segments (DPSs) as defined under the US Endangered Species Act, including threatened and endangered DPSs that forage within the California Current Large Marine Ecosystem. These linkages provide insights into which prey species whales may be targeting in different regions and across multiple scales and, consequently, how climatic variability and anthropogenic risks may differentially impact these distinct predator-prey assemblages. By identifying scale-appropriate prey hotspots that co-occur with humpback whale aggregations, and with targeted, consistent prey sampling and estimations of potential consumption rates by whales, these findings can help inform the conservation and management of humpback whales within an ecosystem-based management framework.

Variability in prey field structure drives inter-annual differences in prey encounter by a marine predator, the little penguin

Understanding how marine predators encounter prey across patchy landscapes remains challenging due to difficulties in measuring the three-dimensional structure of pelagic prey fields at scales relevant to animal movement. We measured at-sea behaviour of a central-place forager, the little penguin (Eudyptula minor), over 5 years (2015-2019) using GPS and dive loggers. We made contemporaneous measurements of the prey field within the penguins' foraging range via boat-based acoustic surveys. We developed a prey encounter index by comparing estimates of acoustic prey density encountered along actual penguin tracks to those encountered along simulated penguin tracks with the same characteristics as real tracks but that moved randomly through the prey field. In most years, penguin tracks encountered prey better than simulated random movements greater than 99% of the time, and penguin dive depths matched peaks in the vertical distribution of prey. However, when prey was unusually sparse and/or deep, penguins had worse than random prey encounter indices, exhibited dives that mismatched depth of maximum prey density, and females had abnormally low body mass (5.3% lower than average). Reductions in prey encounters owing to decreases in the density or accessibility of prey may ultimately lead to reduced fitness and population declines in central-place foraging marine predators.

Incorporating Geographical Scale and Multiple Environmental Factors to Delineate the Breeding Distribution of Sea Turtles

Temperature is often used to infer how climate influences wildlife distributions; yet, other parameters also contribute, separately and combined, with effects varying across geographical scales. Here, we used an unoccupied aircraft system to explore how environmental parameters affect the regional distribution of the terrestrial and marine breeding habitats of threatened loggerhead sea turtles (Caretta caretta). Surveys spanned four years and ~620 km coastline of western Greece, encompassing low (<10 nests/km) to high (100–500 nests/km) density nesting areas. We recorded 2395 tracks left by turtles on beaches and 1928 turtles occupying waters adjacent to these beaches. Variation in beach track and inwater turtle densities was explained by temperature, offshore prevailing wind, and physical marine and terrestrial factors combined. The highest beach-track densities (400 tracks/km) occurred on beaches with steep slopes and higher sand temperatures, sheltered from prevailing offshore winds. The highest inwater turtle densities (270 turtles/km) occurred over submerged sandbanks, with warmer sea temperatures associated with offshore wind. Most turtles (90%) occurred over nearshore submerged sandbanks within 10 km of beaches supporting the highest track densities, showing the strong linkage between optimal marine and terrestrial environments for breeding. Our findings demonstrate the utility of UASs in surveying marine megafauna and environmental data at large scales and the importance of integrating multiple factors in climate change models to predict species distributions.

Do fine-scale experiments underestimate predator consumption rates?

Understanding ecological processes across spatial scales helps link observations and predictions from experiments to ecological patterns occurring at coarser scales relevant to management and conservation. Using fish, we experimentally manipulated the size of arenas to test the spatial scaling of predator-prey interactions. We measured variation in predator consumption and prey behaviour (prey aggregation, spatial overlap with predators and movement) across arena sizes. Variation in prey behaviour across arena sizes was hypothesized to drive consumption patterns by altering prey vigilance and encounter rates with predators. Per capita consumption and movement were highest in the largest arena relative to the smallest and we observed a mismatch between where bass were present and the highest densities of prey across all arena sizes. We hypothesize more movement in largest arenas increased encounter rates and drove the observed increase in consumption with increasing arena size. Consumption estimates obtained in experimental studies may underestimate consumption, but understanding the mechanisms driving bias across scales helps predict the outcomes of predator-prey interactions in natural systems.

Consistent concentrations of critically endangered Balearic shearwaters in UK waters revealed by at-sea surveys

AIM

Europe's only globally critically endangered seabird, the Balearic shearwater (Puffinus mauretanicus), is thought to have expanded its postbreeding range northwards into UK waters, though its at sea distribution there is not yet well understood. This study aims to identify environmental factors associated with the species' presence, map the probability of presence of the species across the western English Channel and southern Celtic Sea, and estimate the number of individuals in this area.

LOCATION

The western English Channel and southern Celtic Sea.

METHODS

This study analyses strip transect data collected between 2013 and 2017 from vessel-based surveys in the western English Channel and southern Celtic Sea during the Balearic shearwater's postbreeding period. Using environmental data collected directly and from remote sensors both Generalized Additive Models and the Random Forest machine learning model were used to determine shearwater presence at different locations. Abundance was estimated separately using a density multiplication approach.

RESULTS

Both models indicated that oceanographic features were better predictors of shearwater presence than fish abundance. Seafloor aspect, sea surface temperature, depth, salinity, and maximum current speed were the most important predictors. The estimated number of Balearic shearwaters in the prediction area ranged from 652 birds in 2017 to 6,904 birds in 2014.

MAIN CONCLUSIONS

Areas with consistently high probabilities of shearwater presence were identified at the Celtic Sea front. Our estimates suggest that the study area in southwest Britain supports between 2% and 23% of the global population of Balearic shearwaters. Based on the timing of the surveys (mainly in October), it is probable that most of the sighted shearwaters were immatures. This study provides the most complete understanding of Balearic shearwater distribution in UK waters available to date, information that will help inform any future conservation actions concerning this endangered species.

Movement patterns of large juvenile loggerhead turtles in the Mediterranean Sea: Ontogenetic space use in a small ocean basin

Mechanisms that determine how, where, and when ontogenetic habitat shifts occur are mostly unknown in wild populations. Differences in size and environmental characteristics of ontogenetic habitats can lead to differences in movement patterns, behavior, habitat use, and spatial distributions across individuals of the same species. Knowledge of juvenile loggerhead turtles' dispersal, movements, and habitat use is largely unknown, especially in the Mediterranean Sea. Satellite relay data loggers were used to monitor movements, diving behavior, and water temperature of eleven large juvenile loggerhead turtles (Caretta caretta) deliberately caught in an oceanic habitat in the Mediterranean Sea. Hidden Markov models were used over 4,430 spatial locations to quantify the different activities performed by each individual: transit, low-, and high-intensity diving. Model results were then analyzed in relation to water temperature, bathymetry, and distance to the coast. The hidden Markov model differentiated between bouts of area-restricted search as low- and high-intensity diving, and transit movements. The turtles foraged in deep oceanic waters within 60 km from the coast as well as above 140 km from the coast. They used an average area of 194,802 km2, where most individuals used the deepest part of the Southern Tyrrhenian Sea with the highest seamounts, while only two switched to neritic foraging showing plasticity in foraging strategies among turtles of similar age classes. The foraging distribution of large juvenile loggerhead turtles, including some which were of the minimum size of adults, in the Tyrrhenian Sea is mainly concentrated in a relatively small oceanic area with predictable mesoscale oceanographic features, despite the proximity of suitable neritic foraging habitats. Our study highlights the importance of collecting high-resolution data about species distribution and behavior across different spatio-temporal scales and life stages for implementing conservation and dynamic ocean management actions.

Predator-prey interactions in a ladybeetle-aphid system depend on spatial scale

The outcome of species interactions may manifest differently at different spatial scales; therefore, our interpretation of observed interactions will depend on the scale at which observations are made. For example, in ladybeetle-aphid systems, the results from small-scale cage experiments usually cannot be extrapolated to landscape-scale field observations. To understand how ladybeetle-aphid interactions change across spatial scales, we evaluated predator-prey interactions in an experimental system. The experimental habitat consisted of 81 potted plants and was manipulated to facilitate analysis across four spatial scales. We also simulated a spatially explicit metacommunity model parallel to the experiment. In the experiment, we found that the negative effect of ladybeetles on aphids decreased with increasing spatial scales. This pattern can be explained by ladybeetles strongly suppressing aphids at small scales, but not colonizing distant patches fast enough to suppress aphids at larger scales. In the experiment, the positive effects of aphids on ladybeetles were strongest at three-plant scale. In a model scenario where predators did not have demographic dynamics, we found, consistent with the experiment, that both the effects of ladybeetles on aphids and the effects of aphids on ladybeetles decreased with increasing spatial scales. These patterns suggest that dispersal was the primary cause of ladybeetle population dynamics in our experiment: aphids increased ladybeetle numbers at smaller scales because ladybeetles stayed in a patch longer and performed area-restricted searches after encountering aphids; these behaviors did not affect ladybeetle numbers at larger spatial scales. The parallel experimental and model results illustrate how predator-prey interactions can change across spatial scales, suggesting that our interpretation of observed predator-prey dynamics would differ if observations were made at different scales. This study demonstrates how studying ecological interactions at a range of scales can help link the results of small-scale ecological experiments to landscape-scale ecological problems.

The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data

The recent increase in data accuracy from high resolution accelerometers offers substantial potential for improved understanding and prediction of animal movements. However, current approaches used for analysing these multivariable datasets typically require existing knowledge of the behaviors of the animals to inform the behavioral classification process. These methods are thus not well‐suited for the many cases where limited knowledge of the different behaviors performed exist. Here, we introduce the use of an unsupervised learning algorithm. To illustrate the method's capability we analyse data collected using a combination of GPS and Accelerometers on two seabird species: razorbills (Alca torda) and common guillemots (Uria aalge). We applied the unsupervised learning algorithm Expectation Maximization to characterize latent behavioral states both above and below water at both individual and group level. The application of this flexible approach yielded significant new insights into the foraging strategies of the two study species, both above and below the surface of the water. In addition to general behavioral modes such as flying, floating, as well as descending and ascending phases within the water column, this approach allowed an exploration of previously unstudied and important behaviors such as searching and prey chasing/capture events. We propose that this unsupervised learning approach provides an ideal tool for the systematic analysis of such complex multivariable movement data that are increasingly being obtained with accelerometer tags across species. In particular, we recommend its application in cases where we have limited current knowledge of the behaviors performed and existing supervised learning approaches may have limited utility.

Robustness of optimal random searches in fragmented environments

The random search problem is a challenging and interdisciplinary topic of research in statistical physics. Realistic searches usually take place in nonuniform heterogeneous distributions of targets, e.g., patchy environments and fragmented habitats in ecological systems. Here we present a comprehensive numerical study of search efficiency in arbitrarily fragmented landscapes with unlimited visits to targets that can only be found within patches. We assume a random walker selecting uniformly distributed turning angles and step lengths from an inverse power-law tailed distribution with exponent μ. Our main finding is that for a large class of fragmented environments the optimal strategy corresponds approximately to the same value μ(opt)≈2. Moreover, this exponent is indistinguishable from the well-known exact optimal value μ(opt)=2 for the low-density limit of homogeneously distributed revisitable targets. Surprisingly, the best search strategies do not depend (or depend only weakly) on the specific details of the fragmentation. Finally, we discuss the mechanisms behind this observed robustness and comment on the relevance of our results to both the random search theory in general, as well as specifically to the foraging problem in the biological context.

Spatio-temporal dynamics of ocean conditions and forage taxa reveal regional structuring of seabird–prey relationships

Studies of predator–prey demographic responses and the physical drivers of such relationships are rare, yet essential for predicting future changes in the structure and dynamics of marine ecosystems. Here, we hypothesize that predator–prey relationships vary spatially in association with underlying physical ocean conditions, leading to observable changes in demographic rates, such as reproduction. To test this hypothesis, we quantified spatio-temporal variability in hydrographic conditions, krill, and forage fish to model predator (seabird) demographic responses over 18 years (1990–2007). We used principal component analysis and spatial correlation maps to assess coherence among ocean conditions, krill, and forage fish, and generalized additive models to quantify interannual variability in seabird breeding success relative to prey abundance. The first principal component of four hydrographic measurements yielded an index that partitioned “warm/weak upwelling” and “cool/strong upwelling” years. Partitioning of krill and forage fish time series among shelf and oceanic regions yielded spatially explicit indicators of prey availability. Krill abundance within the oceanic region was remarkably consistent between years, whereas krill over the shelf showed marked interannual fluctuations in relation to ocean conditions. Anchovy abundance varied on the shelf, and was greater in years of strong stratification, weak upwelling and warmer temperatures. Spatio-temporal variability of juvenile forage fish co-varied strongly with each other and with krill, but was weakly correlated with hydrographic conditions. Demographic responses between seabirds and prey availability revealed spatially variable associations indicative of the dynamic nature of “predator–habitat” relationships. Quantification of spatially explicit demographic responses, and their variability through time, demonstrate the possibility of delineating specific critical areas where the implementation of protective measures could maintain functions and productivity of central place foraging predators.

Predator-prey spatial game as a tool to understand the effects of protected areas on harvester-wildlife interactions

No-take reserves are sometimes implemented for sustainable population harvesting because they offer opportunities for animals to spatially avoid harvesters, whereas harvesters can benefit in return from the reserve spillover. Here, we used the framework of predator-prey spatial games to understand how protected areas shape spatial interactions between harvesters and target species and determine animal mortality. In these spatial games, the "predator" searches for "prey" and matches their habitat use, unless it meets spatial constraints offering the opportunity for prey to avoid the mortality source. However, such prey refuges could attract predators in the surroundings, which questions the potential benefits for prey. We located, in the Geneva Basin (France), hunting dogs and wild boar Sus scrofa L. during hunting seasons with global positioning systems and very-high-frequency collars. We quantified how the proximity of the reserve shaped the matching between both habitat uses using multivariate analyses and linked these patterns to animals' mortality with a Cox regression analysis. Results showed that habitat uses by both protagonists disassociated only when hunters were spatially constrained by the reserve. In response, hunters increased hunting efforts near the reserve boundary, which induced a higher risk exposure for animals settled over the reserve. The mortality of adult wild boar decreased near the reserve as the mismatch between both habitat uses increased. However the opposite pattern was determined for younger individuals that suffered from the high level of hunting close to the reserve. The predator-prey analogy was an accurate prediction of how the protected area modified spatial relationships between harvesters and target species. Prey-searching strategies adopted by hunters around reserves strongly impacted animal mortality and the efficiency of the protected area for this harvested species. Increasing reserve sizes and/or implementing buffer areas with harvesting limitations can dampen this edge effect and helps harvesters to benefit durably from source populations of reserves. Predator-prey spatial games therefore provide a powerful theoretical background for understanding wildlife-harvester spatial interactions and developing substantial application for sustainable harvesting.

Scale-dependent response diversity of seabirds to prey in the North Sea

Functional response diversity is defined as the diversity of responses to environmental change among species that contribute to the same ecosystem function. Because different ecological processes dominate on different spatial and temporal scales, response diversity is likely to be scale dependent. Using three extensive data sets on seabirds, pelagic fish, and zooplankton, we investigate the strength and diversity in the response of seabirds to prey in the North Sea over three scales of ecological organization. Two-stage analyses were used to partition the variance in the abundance of predators and prey among the different scales of investigation: variation from year to year, variation among habitats, and variation on the local patch scale. On the year-to-year scale, we found a strong and synchronous response of seabirds to the abundance of prey, resulting in low response diversity. Conversely, as different seabird species were found in habitats dominated by different prey species, we found a high diversity in the response of seabirds to prey on the habitat scale. Finally, on the local patch scale, seabirds were organized in multispecies patches. These patches were weakly associated with patches of prey, resulting in a weak response strength and a low response diversity. We suggest that ecological similarities among seabird species resulted in low response diversity on the year-to-year scale. On the habitat scale, we suggest that high response diversity was due to interspecific competition and niche segregation among seabird species. On the local patch scale, we suggest that facilitation with respect to the detection and accessibility of prey patches resulted in overlapping distribution of seabirds but weak associations with prey. The observed scale dependencies in response strength and diversity have implications for how the seabird community will respond to different environmental disturbances.

Fine-scale habitat modeling of a top marine predator: do prey data improve predictive capacity?

Predators and prey assort themselves relative to each other, the availability of resources and refuges, and the temporal and spatial scale of their interaction. Predictive models of predator distributions often rely on these relationships by incorporating data on environmental variability and prey availability to determine predator habitat selection patterns. This approach to predictive modeling holds true in marine systems where observations of predators are logistically difficult, emphasizing the need for accurate models. In this paper, we ask whether including prey distribution data in fine-scale predictive models of bottlenose dolphin (Tursiops truncatus) habitat selection in Florida Bay, Florida, U.S.A., improves predictive capacity. Environmental characteristics are often used as predictor variables in habitat models of top marine predators with the assumption that they act as proxies of prey distribution. We examine the validity of this assumption by comparing the response of dolphin distribution and fish catch rates to the same environmental variables. Next, the predictive capacities of four models, with and without prey distribution data, are tested to determine whether dolphin habitat selection can be predicted without recourse to describing the distribution of their prey. The final analysis determines the accuracy of predictive maps of dolphin distribution produced by modeling areas of high fish catch based on significant environmental characteristics. We use spatial analysis and independent data sets to train and test the models. Our results indicate that, due to high habitat heterogeneity and the spatial variability of prey patches, fine-scale models of dolphin habitat selection in coastal habitats will be more successful if environmental variables are used as predictor variables of predator distributions rather than relying on prey data as explanatory variables. However, predictive modeling of prey distribution as the response variable based on environmental variability did produce high predictive performance of dolphin habitat selection, particularly foraging habitat.

DENSITY-DEPENDENT MIGRATORY WAVES IN THE MARINE PELAGIC ECOSYSTEM

The migration of large aggregations of animals that sweep through the landscape is a phenomenon with large consequences in many ecosystems. It has been suggested that such migrations are mediated by resource depletion. Under this hypothesis it has been shown that simple foraging rules may generate density-dependent migratory waves (DDMW) in which the speed and amplitude increase with animal abundance. We tested these predictions on a 32-year data set of the spatial distribution of the two youngest age groups of a small pelagic schooling fish, the capelin (Mallotus villosus), by the end of their annual feeding migration in the Barents Sea. Our data suggest that the two age groups divided the Barents Sea by forming migratory waves that moved in opposite directions. The aggregation and spatial displacement of these waves increased with increasing age-specific abundance. However, possibly through social interactions, migratory pattern was modified by the abundance of the other age group.