Classifying grey seal behaviour in relation to environmental variability and commercial fishing activity - a multivariate hidden Markov model

Intense scuba diving does not alter activity patterns of predatory reef fish: Evidence from a protected tourism hotspot

The rise of nature-based tourism has provided a new avenue for disturbing animal behaviour, especially in protected areas. One of the most important tourism sectors in aquatic environments is scuba diving, an activity considered sustainable given its non-extractive nature and capability of bringing relevant socio-economic benefits to local communities. However, knowledge about its impact on the activity patterns of aquatic animals is still scarce. Here, we used biotelemetry techniques to assess the importance of scuba diving in modulating the activity patterns of the dusky grouper (Epinephelus marginatus, Lowe, 1834), a marine predatory fish of high interest for fishing and tourism. We implemented Hidden Markov Models (HMMs) on high-resolution acceleration data using a temporal and spatial control while controlling for a set of environmental variables (i.e. photoperiod, time-of-day, moon phase, temperature, wave height, and intensity and direction of marine currents) within a multiple-use marine protected area, and diving tourism hot-spot, of the western Mediterranean Sea. Our results underlined the more decisive influence of environmental-related stressors on the activity patterns of the dusky grouper compared to the impact of scuba diving. A high heterogeneity existed in the response against most of the stressors, including the presence of scuba divers. Overall, the activity of dusky grouper was higher at night than at day, showing a positive relationship with wave height, water temperature, and current intensity and a negative one with the moon phase. Remarkably, our findings, based on novel biotelemetry tools, differed substantially from the common wisdom accepted for this species. In conclusion, there is no clear evidence of scuba divers influence on the general activity patterns of the dusky grouper. Beyond their relevance from an ecological perspective, these results provide useful insights for the sustainable management of coastal resources, suggesting that scuba diving, when properly carried out, can represent an important sector to foster for the blue growth of coastal communities.

Quantifying the behavioural consequences of shark ecotourism

Shark populations globally are facing catastrophic declines. Ecotourism has been posited as a potential solution to many of the issues facing shark conservation, yet increasingly studies suggest that such activity may negatively influence aspects of shark ecology and so further pressure declining populations. Here we combine UAV videography with deep learning algorithms, multivariate statistics and hidden Markov models (HMM) to quantitatively investigate the behavioural consequences of ecotourism in the whale shark (Rhincodon typus). We find that ecotourism increases the probability of sharks being in a disturbed behavioural state, likely increasing energetic expenditure and potentially leading to downstream ecological effects. These results are only recovered when fitting models that account for individual variation in behavioural responses and past behavioural history. Our results demonstrate that behavioural responses to ecotourism are context dependent, as the initial behavioural state is important in determining responses to human activity. We argue that models incorporating individuality and context-dependence should, wherever possible, be incorporated into future studies investigating the ecological impacts of shark ecotourism, which are only likely to increase in importance given the expansion of the industry and the dire conservation status of many shark species.

Linking fine-scale behaviour to the hydraulic environment shows behavioural responses in riverine fish

BACKGROUND

Fish migration has severely been impacted by dam construction. Through the disruption of fish migration routes, freshwater fish communities have seen an incredible decline. Fishways, which have been constructed to mitigate the problem, have been shown to underperform. This is in part due to fish navigation still being largely misunderstood. Recent developments in tracking technology and modelling make it possible today to track (aquatic) animals at very fine spatial (down to one meter) and temporal (down to every second) scales. Hidden Markov models are appropriate models to analyse behavioural states at these fine scales. In this study we link fine-scale tracking data of barbel (Barbus barbus) and grayling (Thymallus thymallus) to a fine-scale hydrodynamic model. With a HMM we analyse the fish's behavioural switches to understand their movement and navigation behaviour near a barrier and fishway outflow in the Iller river in Southern Germany.

METHODS

Fish were tracked with acoustic telemetry as they approached a hydropower facility and were presented with a fishway. Tracking resulted in fish tracks with variable intervals between subsequent fish positions. This variability stems from both a variable interval between tag emissions and missing detections within a track. After track regularisation hidden Markov models were fitted using different parameters. The tested parameters are step length, straightness index calculated over a 3-min moving window, and straightness index calculated over a 10-min window. The best performing model (based on a selection by AIC) was then expanded by allowing flow velocity and spatial velocity gradient to affect the transition matrix between behavioural states.

RESULTS

In this study it was found that using step length to identify behavioural states with hidden Markov models underperformed when compared to models constructed using straightness index. Of the two different straightness indices assessed, the index calculated over a 10-min moving window performed better. Linking behavioural states to the ecohydraulic environment showed an effect of the spatial velocity gradient on behavioural switches. On the contrary, flow velocity did not show an effect on the behavioural transition matrix.

CONCLUSIONS

We found that behavioural switches were affected by the spatial velocity gradient caused by the attraction flow coming from the fishway. Insight into fish navigation and fish reactions to the ecohydraulic environment can aid in the construction of fishways and improve overall fishway efficiencies, thereby helping to mitigate the effects migration barriers have on the aquatic ecosystem.

Animal behaviour on the move: the use of auxiliary information and semi-supervision to improve behavioural inferences from Hidden Markov Models applied to GPS tracking datasets

BACKGROUND

State-space models, such as Hidden Markov Models (HMMs), are increasingly used to classify animal tracks into behavioural states. Typically, step length and turning angles of successive locations are used to infer where and when an animal is resting, foraging, or travelling. However, the accuracy of behavioural classifications is seldom validated, which may badly contaminate posterior analyses. In general, models appear to efficiently infer behaviour in species with discrete foraging and travelling areas, but classification is challenging for species foraging opportunistically across homogenous environments, such as tropical seas. Here, we use a subset of GPS loggers deployed simultaneously with wet-dry data from geolocators, activity measurements from accelerometers, and dive events from Time Depth Recorders (TDR), to improve the classification of HMMs of a large GPS tracking dataset (478 deployments) of red-billed tropicbirds (Phaethon aethereus), a poorly studied pantropical seabird.

METHODS

We classified a subset of fixes as either resting, foraging or travelling based on the three auxiliary sensors and evaluated the increase in overall accuracy, sensitivity (true positive rate), specificity (true negative rate) and precision (positive predictive value) of the models in relation to the increasing inclusion of fixes with known behaviours.

RESULTS

We demonstrate that even with a small informed sub-dataset (representing only 9% of the full dataset), we can significantly improve the overall behavioural classification of these models, increasing model accuracy from 0.77 ± 0.01 to 0.85 ± 0.01 (mean ± sd). Despite overall improvements, the sensitivity and precision of foraging behaviour remained low (reaching 0.37 ± 0.06, and 0.06 ± 0.01, respectively).

CONCLUSIONS

This study demonstrates that the use of a small subset of auxiliary data with known behaviours can both validate and notably improve behavioural classifications of state space models of opportunistic foragers. However, the improvement is state-dependant and caution should be taken when interpreting inferences of foraging behaviour from GPS data in species foraging on the go across homogenous environments.

Forecasting shifts in habitat suitability of three marine predators suggests a rapid decline in inter‐specific overlap under future climate change

Understanding how environmental and climate change can alter habitat overlap of marine predators has great value for the management and conservation of marine ecosystems. Here, we estimated spatiotemporal changes in habitat suitability and inter‐specific overlap among three marine predators: Baltic gray seals (Halichoerus grypus), harbor seals (Phoca vitulina), and harbor porpoises (Phocoena phocoena) under contemporary and future conditions. Location data (>200 tagged individuals) were collected in the southwestern region of the Baltic Sea; one of the fastest‐warming semi‐enclosed seas in the world. We used the maximum entropy (MaxEnt) algorithm to estimate changes in total area size and overlap of species‐specific habitat suitability between 1997–2020 and 2091–2100. Predictor variables included environmental and climate‐sensitive oceanographic conditions in the area. Sea‐level rise, sea surface temperature, and salinity data were taken from representative concentration pathways [RCPs] scenarios 6.0 and 8.5 to forecast potential climate change effects. Model output suggested that habitat suitability of Baltic gray seals will decline over space and time, driven by changes in sea surface salinity and a loss of currently available haulout sites following sea‐level rise in the future. A similar, although weaker, effect was observed for harbor seals, while suitability of habitat for harbor porpoises was predicted to increase slightly over space and time. Inter‐specific overlap in highly suitable habitats was also predicted to increase slightly under RCP scenario 6.0 when compared to contemporary conditions, but to disappear under RCP scenario 8.5. Our study suggests that marine predators in the southwestern Baltic Sea may respond differently to future climatic conditions, leading to divergent shifts in habitat suitability that are likely to decrease inter‐specific overlap over time and space. We conclude that climate change can lead to a marked redistribution of area use by marine predators in the region, which may influence local food‐web dynamics and ecosystem functioning.

Assessing behaviour states of a forest carnivore in a road-dominated landscape using Hidden Markov Models

Anthropogenic infrastructures and land-use changes are major threats to animal movements across heterogeneous landscapes. Yet, the behavioural consequences of such constraints remain poorly understood. We investigated the relationship between the behaviour of the Common genet (Genetta genetta) and road proximity, within a dominant mixed forest-agricultural landscape in southern Portugal, fragmented by roads. Specifically, we aimed to: (i) identify and characterise the behavioural states displayed by genets and related movement patterns; and (ii) understand how behavioural states are influenced by proximity to main paved roads and landscape features. We used a multivariate Hidden Markov Model (HMM) to characterise the fine-scale movements (10-min fixes GPS) of seven genets tracked during 187 nights (mean 27 days per individual) during the period 2016–2019, using distance to major paved roads and landscape features as predictors. Our findings indicated that genet’s movement patterns were composed of three basic behavioural states, classified as “resting” (short step-lengths [mean = 10.6 m] and highly tortuous), “foraging” (intermediate step-lengths [mean = 46.1 m] and with a wide range in turning angle) and “travelling” (longer step-lengths [mean = 113.7 m] and mainly linear movements). Within the genet’s main activity-period (17.00 h-08.00 h), the movement model predicts that genets spend 36.7% of their time travelling, 35.4% foraging and 28.0% resting. The probability of genets displaying the travelling state was highest in areas far away from roads (> 500 m), whereas foraging and resting states were more likely in areas relatively close to roads (up to 500 m). Landscape features also had a pronounced effect on behaviour state occurrence. More specifically, travelling was most likely to occur in areas with lower forest edge density and close to riparian habitats, while foraging was more likely to occur in areas with higher forest edge density and far away from riparian habitats. The results suggest that, although roads represent a behavioural barrier to the movement of genets, they also take advantage of road proximity as foraging areas. Our study demonstrates that the HMM approach is useful for disentangling movement behaviour and understanding how animals respond to roadsides and fragmented habitats. We emphasise that road-engaged stakeholders need to consider movement behaviour of genets when targeting management practices to maximise road permeability for wildlife.

Prey encounters and spatial memory influence use of foraging patches in a marine central place forager

Given the patchiness and long-term predictability of marine resources, memory of high-quality foraging grounds is expected to provide fitness advantages for central place foragers. However, it remains challenging to characterize how marine predators integrate memory with recent prey encounters to adjust fine-scale movement and use of foraging patches. Here, we used two months of movement data from harbour seals (Phoca vitulina) to quantify the repeatability in foraging patches as a proxy for memory. We then integrated these data into analyses of fine-scale movement and underwater behaviour to test how both spatial memory and prey encounter rates influenced the seals' area-restricted search (ARS) behaviour. Specifically, we used one month's GPS data from 29 individuals to build spatial memory maps of searched areas and archived accelerometery data from a subset of five individuals to detect prey catch attempts, a proxy for prey encounters. Individuals were highly consistent in the areas they visited over two consecutive months. Hidden Markov models showed that both spatial memory and prey encounters increased the probability of seals initiating ARS. These results provide evidence that predators use memory to adjust their fine-scale movement, and this ability should be accounted for in movement models.

Maximum approximate likelihood estimation of general continuous-time state-space models

Continuous-time state-space models (SSMs) are flexible tools for analysing irregularly sampled sequential observations that are driven by an underlying state process. Corresponding applications typically involve restrictive assumptions concerning linearity and Gaussianity to facilitate inference on the model parameters via the Kalman filter. In this contribution, we provide a general continuous-time SSM framework, allowing both the observation and the state process to be non-linear and non-Gaussian. Statistical inference is carried out by maximum approximate likelihood estimation, where multiple numerical integration within the likelihood evaluation is performed via a fine discretization of the state process. The corresponding reframing of the SSM as a continuous-time hidden Markov model, with structured state transitions, enables us to apply the associated efficient algorithms for parameter estimation and state decoding. We illustrate the modelling approach in a case study using data from a longitudinal study on delinquent behaviour of adolescents in Germany, revealing temporal persistence in the deviation of an individual's delinquency level from the population mean.

Movement patterns and activity levels are shaped by the neonatal environment in Antarctic fur seal pups

Tracking studies of juveniles are rare compared to those of adults, and consequently little is known about the influence of intrinsic and extrinsic factors on activity during this critical life stage. We used hourly GPS data, collected from 66 Antarctic fur seal pups from birth until moulting, to investigate the explanatory power of multiple individual-based and environmental variables on activity levels. Pups were sampled from two nearby breeding colonies of contrasting density during two subsequent years, and a two-state hidden Markov model was used to identify modalities in their movement behaviour, specifically 'active' and 'inactive' states. We found that movement was typified by central place exploration, with active movement away from and subsequent return to a location of inactivity. The probability of such directed exploration was unaffected by several factors known to influence marine mammal movement including sex, body condition, and temperature. Compared to pups born at the high-density colony, pups at low-density were more active, increased their activity with age, and transitioned earlier into the tussock grass, which offers protection from predators and extreme weather. Our study illustrates the importance of extrinsic factors, such as colony of birth, to early-life activity patterns and highlights the adaptive potential of movement.

A copula-based multivariate hidden Markov model for modelling momentum in football

We investigate the potential occurrence of change points—commonly referred to as “momentum shifts”—in the dynamics of football matches. For that purpose, we model minute-by-minute in-game statistics of Bundesliga matches using hidden Markov models (HMMs). To allow for within-state dependence of the variables, we formulate multivariate state-dependent distributions using copulas. For the Bundesliga data considered, we find that the fitted HMMs comprise states which can be interpreted as a team showing different levels of control over a match. Our modelling framework enables inference related to causes of momentum shifts and team tactics, which is of much interest to managers, bookmakers, and sports fans.

Individuality counts: A new comprehensive approach to foraging strategies of a tropical marine predator

Foraging strategies are of great ecological interest, as they have a strong impact on the fitness of an individual and can affect its ability to cope with a changing environment. Recent studies on foraging strategies show a higher complexity than previously thought due to intraspecific variability. To reliably identify foraging strategies and describe the different foraging niches they allow individual animals to realize, high-resolution multivariate approaches which consider individual variation are required. Here we dive into the foraging strategies of Galápagos sea lions (Zalophus wollebaeki), a tropical predator confronted with substantial annual variation in sea surface temperature. This affects prey abundance, and El Niño events, expected to become more frequent and severe with climate change, are known to have dramatic effects on sea lions. This study used high-resolution measures of depth, GPS position and acceleration collected from 39 lactating sea lion females to analyze their foraging strategies at an unprecedented level of detail using a novel combination of automated broken stick algorithm, hierarchical cluster analysis and individually fitted multivariate hidden Markov models. We found three distinct foraging strategies (pelagic, benthic, and night divers), which differed in their horizontal, vertical and temporal distribution, most likely corresponding to different prey species, and allowed us to formulate hypotheses with regard to adaptive values under different environmental scenarios. We demonstrate the advantages of our multivariate approach and inclusion of individual variation to reliably gain a deeper understanding of the adaptive value and ecological relevance of foraging strategies of marine predators in dynamic environments.

Uncovering ecological state dynamics with hidden Markov models

Ecological systems can often be characterised by changes among a finite set of underlying states pertaining to individuals, populations, communities or entire ecosystems through time. Owing to the inherent difficulty of empirical field studies, ecological state dynamics operating at any level of this hierarchy can often be unobservable or 'hidden'. Ecologists must therefore often contend with incomplete or indirect observations that are somehow related to these underlying processes. By formally disentangling state and observation processes based on simple yet powerful mathematical properties that can be used to describe many ecological phenomena, hidden Markov models (HMMs) can facilitate inferences about complex system state dynamics that might otherwise be intractable. However, HMMs have only recently begun to gain traction within the broader ecological community. We provide a gentle introduction to HMMs, establish some common terminology, review the immense scope of HMMs for applied ecological research and provide a tutorial on implementation and interpretation. By illustrating how practitioners can use a simple conceptual template to customise HMMs for their specific systems of interest, revealing methodological links between existing applications, and highlighting some practical considerations and limitations of these approaches, our goal is to help establish HMMs as a fundamental inferential tool for ecologists.

Sex-specific variation in the use of vertical habitat by a resident Antarctic top predator

Patterns of habitat use are commonly studied in horizontal space, but this does not capture the four-dimensional nature of ocean habitats (space, depth, and time). Deep-diving marine animals encounter varying oceanographic conditions, particularly at the poles, where there is strong seasonal variation in vertical ocean structuring. This dimension of space use is hidden if we only consider horizontal movement. To identify different diving behaviours and usage patterns of vertically distributed habitat, we use hidden Markov models fitted to telemetry data from an air-breathing top predator, the Weddell seal, in the Weddell Sea, Antarctica. We present evidence of overlapping use of high-density, continental shelf water masses by both sexes, as well as important differences in their preferences for oceanographic conditions. Males spend more time in the unique high-salinity shelf water masses found at depth, while females also venture off the continental shelf and visit warmer, shallower water masses. Both sexes exhibit a diurnal pattern in diving behaviour (deep in the day, shallow at night) that persists from austral autumn into winter. The differences in habitat use in this resident, sexually monomorphic Antarctic top predator suggest a different set of needs and constraints operating at the intraspecific level, not driven by body size.

Hidden Markov Models reveal a clear human footprint on the movements of highly mobile African wild dogs

Large carnivores have experienced considerable range contraction, increasing the importance of movement across human-altered landscapes between small, isolated populations. African wild dogs (Lycaon pictus) are exceptionally wide-ranging, and recolonization is an important element of their persistence at broad scales. The competition-movement-connection hypothesis suggests that adaptations to move through areas that are unfavorable due to dominant competitors might promote the ability of subordinate competitors (like wild dogs) to move through areas that are unfavorable due to humans. Here, we used hidden Markov models to test how wild dog movements were affected by the Human Footprint Index in areas inside and outside of South Luangwa National Park. Movements were faster and more directed when outside the National Park, but slowed where the human footprint was stronger. Our results can be directly and quantitatively applied to connectivity planning, and we use them to identify ways to better understand differences between species in recent loss of connectivity.

Foraging behaviour of a continental shelf marine predator, the grey seal (Halichoerus grypus), is associated with in situ, subsurface oceanographic conditions

BACKGROUND

The heterogeneous oceanographic conditions of continental shelf ecosystems result in a three-dimensionally patchy distribution of prey available to upper-trophic level predators. The association of bio-physical conditions with movement patterns of large marine predators has been demonstrated in diverse taxa. However, obtaining subsurface data that are spatio-temporally relevant to the decisions made by benthically-foraging species can be challenging.

METHODS

Between 2009 and 2015, grey seals were captured on Sable Island, Nova Scotia, Canada during summer and fall and instrumented with high-resolution archival GPS tags. These tags recorded location data as well as depth (m), temperature (°C), and light level measurements during dives, until animals returned to the haulout site to breed. Hidden Markov models were used to predict apparent foraging along movement tracks for 79 individuals (59 females, 20 males) every 3 h. In situ measurements were used to estimate chlorophyll-a concentration (mg m^- 3) and temperature within the upper-water column (50 m) and temperature and depth at the bottom of dives. As chlorophyll-a could only be estimated from 10:00 to 14:00 AST for dive depths ≥50 m, we formulated two generalized linear mixed-effects models to test the association of predicted grey seal behavioural states with oceanographic conditions and phytoplankton biomass: the first representing conditions of the upper-water column likely to influence primary productivity, and a second model including environmental conditions encountered by grey seals at the bottom of dives, when seals were more likely to be foraging.

RESULTS

Predicted grey seal behavioural states were associated with fine-scale chlorophyll-a concentrations and other environmental conditions they encountered across the continental shelf. In the Water Column Model, season had no influence on the probability of observing apparent foraging, but chlorophyll-a, upper-water column temperature, and sex did, with females having a greater probability of foraging than males. In the Bottom Conditions Model, again season had no influence on the probability of apparent foraging, but females were over twice as likely as males to be foraging.

CONCLUSIONS

The results of this study highlight the value of in situ measurements of oceanographic properties that can be collected at high temporal resolution by animal-borne data loggers. These data provide insight into how inferred behavioural decisions made by large marine predators, such as the grey seal, may be influenced by fine-scale oceanographic conditions.

Ecological costs of climate change on marine predator-prey population distributions by 2050

Identifying and quantifying the effects of climate change that alter the habitat overlap of marine predators and their prey population distributions is of great importance for the sustainable management of populations. This study uses Bayesian joint models with integrated nested Laplace approximation (INLA) to predict future spatial density distributions in the form of common spatial trends of predator-prey overlap in 2050 under the "business-as-usual, worst-case" climate change scenario. This was done for combinations of six mobile marine predator species (gray seal, harbor seal, harbor porpoise, common guillemot, black-legged kittiwake, and northern gannet) and two of their common prey species (herring and sandeels). A range of five explanatory variables that cover both physical and biological aspects of critical marine habitat were used as follows: bottom temperature, stratification, depth-averaged speed, net primary production, and maximum subsurface chlorophyll. Four different methods were explored to quantify relative ecological cost/benefits of climate change to the common spatial trends of predator-prey density distributions. All but one future joint model showed significant decreases in overall spatial percentage change. The most dramatic loss in predator-prey population overlap was shown by harbor seals with large declines in the common spatial trend for both prey species. On the positive side, both gannets and guillemots are projected to have localized regions with increased overlap with sandeels. Most joint predator-prey models showed large changes in centroid location, however the direction of change in centroids was not simply northwards, but mostly ranged from northwest to northeast. This approach can be very useful in informing the design of spatial management policies under climate change by using the potential differences in ecological costs to weigh up the trade-offs in decisions involving issues of large-scale spatial use of our oceans, such as marine protected areas, commercial fishing, and large-scale marine renewable developments.