Spatio-temporal Co-occurrence Research Articles

Neighbourhood benthic configuration reveals hidden co-occurrence social diversity.

Ecological interactions among benthic communities are crucial for shaping marine ecosystems. Understanding these interactions is essential for predicting how ecosystems will respond to environmental changes, invasive species, and conservation management. However, determining the prevalence of species interactions at the community scale is challenging. To overcome this challenge, we employ tools from social network analysis, specifically exponential random graph modelling (ERGM). Our approach explores the relationships among animal and plant organisms within their neighbourhoods. Inspired by companion planting in agriculture, we use spatiotemporal co-occurrence as a measure of mixed species interaction. In other words, the variety of community interactions based on co-occurrence defines what we call 'co-occurrence social diversity'. Our objective is to use ERGM to quantify the proportion of interactions at both the simple paired level and the more complex triangle level, enabling us to measure and compare co-occurrence social diversity. Applying our approach to the Spanish coastal zone across eight sites, five depths, and sunlit/shaded aspects, we discover that 80% of sessile communities, consisting of over a hundred species, exhibit co-occurrence social diversity, with 5% of species consistently forming associations with other species. These organism-level interactions probably have a significant impact on the overall character of the site. This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'.

Multiple GRAphs-oriented Random wAlk (MulGRA2) for social link prediction

Current link prediction methods in Location-Based Social Networks (LBSNs) fuse graphs derived from users' check-in data and their social links to form a single graph or network. Then, they learn node representations and link probabilities from a fused graph that undermines the distinctive characteristics of each user's spatiotemporal mobility and social links. Consequently, the input datasets are heavily contaminated with noise, which makes it challenging for these algorithms to make accurate predictions. Our study employs the proposed Multiple GRAphs-oriented Random wAlk (MulGRA2) to model graphs while maintaining the distinctive characteristics of the data to address the issue of noisy data in the learning process. Specifically, we use three graphs: a social graph constructed from social links data, a user co-occurrence graph derived from users' check-in data to capture spatiotemporal co-occurrence, and a user-location bipartite graph that links users to specific locations based on the same check-in data. After traversing all three graphs, it learns node representation and infers links effectively. Extensive experiments on both Foursquare and synthetic datasets demonstrate that our algorithm significantly improves link prediction performance. Furthermore, sensitivity analysis on various datasets confirms the robustness of our algorithm.

Feature reconstruction graph convolutional network for skeleton-based action recognition

Skeleton-based action recognition is an important task in computer vision. Recently, graph convolutional networks (GCNs) have been successfully applied to this task and achieved remarkable results. However, there are still some non-negligible limitations with existing GCN-based methods. First, the artificial predefined skeleton partition lacks the joint modeling for different types of edges. Second, most GCN models use interleaved deployment of spatial-only and temporal-only modules to achieve feature learning, which makes them ineffective in capturing spatiotemporal co-occurrence from action sequences. To tackle the above issues, we propose a novel feature reconstruction graph convolutional network (FR-GCN) for skeleton-based action recognition. The proposed FR-GC combines coarse-grained temporal and spatial features to reconstruct fine-grained spatiotemporal features, realizing simultaneous learning of temporal and spatial representations in a single module and significantly improving the capability of the model for spatiotemporal feature extraction. We also propose a topology partition enhancement module to achieve adaptive complementation among different types of edges. Moreover, an efficient multi-scale dual-domain temporal convolution is used to complete further temporal modeling. Compared with state-of-the-art methods, the proposed FR-GCN achieves competitive results on both NTU RGB+D 60 dataset and NTU RGB+D 120 dataset. Especially under the cross-subject benchmarks of the two datasets, the proposed FR-GCN achieves new state-of-the-art performance.

Carbon cycle extremes accelerate weakening of the land carbon sink in the late 21st century

Abstract. Increasing surface temperature could lead to enhanced evaporation, reduced soil moisture availability, and more frequent droughts and heat waves. The spatiotemporal co-occurrence of such effects further drives extreme anomalies in vegetation productivity and net land carbon storage. However, the impacts of climate change on extremes in net biospheric production (NBP) over longer time periods are unknown. Using the percentile threshold on the probability distribution curve of NBP anomalies, we computed negative and positive extremes in NBP. Here we show that due to climate warming, about 88 % of global regions will experience a larger magnitude of negative NBP extremes than positive NBP extremes toward the end of 2100, which accelerate the weakening of the land carbon sink. Our analysis indicates the frequency of negative extremes associated with declines in biospheric productivity was larger than positive extremes, especially in the tropics. While the overall impact of warming at high latitudes is expected to increase plant productivity and carbon uptake, high-temperature anomalies increasingly induce negative NBP extremes toward the end of the 21st century. Using regression analysis, we found soil moisture anomalies to be the most dominant individual driver of NBP extremes. The compound effect of hotness, dryness, and fire caused extremes at more than 50 % of the total grid cells. The larger proportion of negative NBP extremes raises a concern about whether the Earth is capable of increasing vegetation production with a growing human population and rising demand for plant material for food, fiber, fuel, and building materials. The increasing proportion of negative NBP extremes highlights the consequences not only of reduction in total carbon uptake capacity but also of conversion of land to a carbon source.

Progressive unsupervised video person re-identification with accumulative motion and tracklet spatial–temporal correlation

Person re-identification (re-id) methods based on supervised learning require a large number of manually labeled samples for training, resulting in poor scalability in actual re-id applications. Existing unsupervised video person re-id methods typically focus on extracting appearance features from pedestrian videos, ignoring motion information and the fact that people usually move in groups, i.e., pedestrian spatio–temporal co-occurrence patterns. The key factor for unsupervised video person re-id is to effectively exploit both spatio–temporal clues from video sequences and cross-camera tracklet association. In this work, we propose a progressive deep learning method for unsupervised person re-id via tracklet association with spatio–temporal correlation (TASTC). Specifically, we first divide uniformly each tracklet into multiple temporally localized slices according to a time pyramid structure. Then, an initial re-id model is trained based on a two-stream convolutional architecture, and the accumulative motion context information of temporally localized slices of the tracklets per camera is learned. Finally, combining accumulative motion and tracklet spatial–temporal correlation, we associate tracklets across cameras and update the re-id model. The above steps are iterated to optimize the re-id model progressively. Experiment results demonstrate that the proposed method significantly outperforms the current state-of-the-art unsupervised video person re-identification methods on three video-based benchmark datasets, ILIDS-VID, MARS, and DukeMTMC-VideoReID.

SPATIOTEMPORAL CO-OCCURRENCE PATTERNS OF DEMERSAL FISHES IN A SUBTROPICAL MARINE AND ESTUARINE ENVIRONMENT

In order to investigate spatiotemporal co-occurrence patterns of demersal fishes from the Paranaguá Estuarine Complex (PEC) and the adjacent continental shelf, state of Paraná, Southern Brazil, fish from nine sampling sites within the estuary and three additional sites in open sea (N = 12 sampling sites) were sampled at quarterly intervals between 2014 and 2016. The assemblage structure was evaluated by the C-Score index and null models developed through 5,000 randomizations. Species co-occurred as expected, but a general pattern in both temporal and spatial matrices was only detected in the Upper sector, which showed a random co-occurrence pattern. In contrast, fish species from the other sectors co-occurred less than expected by chance in some circumstances. According to the concepts related to the organization of communities on temporal scales, many communities tend to become disorganized after disturbances and then present a progressive increase in organization, that is, they display a random co-occurrence pattern, similar to initial response, and over time the species in the community segregate or aggregate in response to system stability restoration. Therefore, understanding how fish assemblages are structured at different spatial and temporal scales, and especially what processes drive such conformations, including assembly rules for communities, is crucial for ecosystem maintenance and understanding, as well as providing baselines for conservation and management policies, interventions, and practices in these environments.

Heterogeneous spatio-temporal relation learning network for facial action unit detection

• The Attention-based Feature Decoupling module is proposed to obtain AU-specific spatio-temporal feature vectors. • The Co-occurrence Knowledge Graph module is proposed to introduce the prior spatio-temporal co-occurrence between AUs. • The Spatio-Temporal Transformer is proposed to obtain the spatio-temporal cooccurrence pattern in a data-driven manner. • An end-to-end facial AU detection network based on heterogeneous spatiotemporal relation learning is proposed. Properties such as temporal relation and action relation of facial Action Units (AUs) make AU detection different from general multi-label classification tasks. Therefore, how to capture the spatial and temporal co-occurrence of AUs becomes the key to improving detection accuracy. Although many works on the spatial relation of AUs have been proposed in recent years, very few works have explored the temporal relation of AUs. In this paper, we propose a Heterogeneous Spatio-Temporal Relation learning network (HSTR-Net) to capture the temporal and spatial relations of AUs. The co-occurrence knowledge graph module guides the network to model spatio-temporal relations by introducing prior relation information, and the spatio-temporal Transformer module adaptively captures spatio-temporal relations through spatio-temporal feature interaction. To further model the temporal relation between AUs, the self-attention mechanism to fuse AU features in the time dimension. Extensive experiments are conducted on two challenging datasets, BP4D and DISFA, and experimental results show that our proposed HSTR-Net achieves comparable performance of the state-of-the-art in the field of AU detection. The code for our methods is available at https://github.com/saaaaalmon/HSTR-Net .

Discovering Spatio-Temporal Co-Occurrence Patterns of Crimes with Uncertain Occurrence Time

The discovery of spatio-temporal co-occurrence patterns (STCPs) among multiple types of crimes whose events frequently co-occur in neighboring space and time is crucial to the joint prevention of crimes. However, the crime event occurrence time is often uncertain due to a lack of witnesses. This occurrence time uncertainty further results in the uncertainty of the spatio-temporal neighborhood relationships and STCPs. Existing methods have mostly modeled the uncertainty of events under the independent and identically distributed assumption and utilized one-sided distance information to measure the distance between uncertain events. As a result, STCPs detected from a dataset with occurrence time uncertainty (USTCPs) are likely to be erroneously assessed. Therefore, this paper proposes a probabilistic-distance-based USTCP discovery method. First, the temporal probability density functions of crime events with uncertain occurrence times are estimated by considering the temporal dependence. Second, the spatio-temporal neighborhood relationships are constructed based on the spatial Euclidean distance and the proposed temporal probabilistic distance. Finally, the prevalent USTCPs are identified. Experimental comparisons performed on twelve types of crimes from X City Public Security Bureau in China demonstrate that the proposed method can more objectively express the occurrence time of crimes and more reliably identify USTCPs.

Revealing spatiotemporal interaction patterns behind complex cities.

Cities are typical dynamic complex systems that connect people and facilitate interactions. Revealing general collective patterns behind spatiotemporal interactions between residents is crucial for various urban studies, of which we are still lacking a comprehensive understanding. Massive cellphone data enable us to construct interaction networks based on spatiotemporal co-occurrence of individuals. The rank-size distributions of dynamic population of locations in all unit time windows are stable, although people are almost constantly moving in cities and hot-spots that attract people are changing over time in a day. A larger city is of a stronger heterogeneity as indicated by a larger scaling exponent. After aggregating spatiotemporal interaction networks over consecutive time windows, we reveal a switching behavior of cities between two states. During the "active" state, the whole city is concentrated in fewer larger communities, while in the "inactive" state, people are scattered in smaller communities. Above discoveries are universal over three cities across continents. In addition, a city stays in an active state for a longer time when its population grows larger. Spatiotemporal interaction segregation can be well approximated by residential patterns only in smaller cities. In addition, we propose a temporal-population-weighted-opportunity model by integrating a time-dependent departure probability to make dynamic predictions on human mobility, which can reasonably well explain the observed patterns of spatiotemporal interactions in cities.

Spatiotemporal co-occurrence of predators and prey in a neotropical mammal community in southern Mexico

AbstractPredator-prey interactions are one of the central themes in ecology due to their importance as a key mechanism in structuring biotic communities. In the predator-prey systems, the behaviours of persecution and avoidance impact on the ecosystem dynamics as much as the trophic interactions. We aimed to analyse the spatiotemporal co-occurrences between prey and predators in a community of medium- and large-sized mammals in southern Mexico. We predict prey will avoid sites where a predator previously passed. Contrarily, we expect a search behaviour by predators and a synchronization in activity patterns among them. We found prey does not occur either in time or space where predators have passed, suggesting an avoidance behaviour. Contrary to our expectations, we did not find significant search behaviours from predators to prey. Synchronization in the daily temporal overlap between predators was higher (Δ = 0.77–0.82) than with their prey (Δ = 0.43 – 0.81). The results suggest prey perceives the risk of predation and displays avoidance behaviour both spatially and temporally, which is consistent with the fear theory. This study provides a complementary approach to understanding the behaviour mechanism between predators and prey through camera-trapping or similar data of spatiotemporal co-occurrences.

Spatial and temporal variations in interspecific interaction: impact of a recreational landscape

Anthropogenic activities, such as outdoor recreation, have the potential to change complex interactions between wildlife and livestock, with further consequences for the management of both animals, the environment, and disease transmission. We present the interaction amongst wildlife, livestock, and outdoor recreationists as a three-way interaction. Little is known about how recreational activities alter the interaction between herbivores in areas extensively used for recreational purposes. We investigate how hiking activity affects spatio-temporal co-occurrence between domestic sheep (Ovis aries) and red deer (Cervus elaphus). We used camera traps to capture the spatio-temporal distribution of red deer and sheep and used the distance from the hiking path as a proxy of hiking activity. We used generalized linear models to investigate the spatial distribution of sheep and deer. We analysed the activity patterns of sheep and deer and then calculated their coefficients of temporal overlap for each camera trap location. We compared these coefficients in relation to the distance from the hiking path. Finally, we used a generalized linear mixed-model to investigate which factors influence the spatio-temporal succession between deer and sheep. We do not find that sheep and red deer spatially avoid each other. The coefficient of temporal overlap varied with distance from the hiking trail, with stronger temporal co-occurrence at greater distances from the hiking trail. Red deer were more likely to be detected further from the path during the day, which increased the temporal overlap with sheep in these areas. This suggests that hiking pressure influences spatio-temporal interactions between sheep and deer, leading to greater temporal overlap in areas further from the hiking path due to red deer spatial avoidance of hikers. This impact of recreationists on the wildlife and livestock interaction can have consequences for the animals’ welfare, the vegetation they graze, their management, and disease transmission.

3D Deep Heterogeneous Manifold Network for Behavior Recognition

With the broadening of application scenarios for Internet of Things, intelligent behavior recognition task has attracted more and more attention. Since human behavior is nonrigid motion with strong spatiotemporal topological association, modeling it directly with traditional Euclidean space-based methods may destroy its underlying nonlinearity. Based on the advantages of Riemannian manifold in describing 3D motion, we propose an end-to-end 3D behavior manifold feature learning framework composed of deep heterogeneous networks. This heterogeneous architecture aims to leverage the graph construction to guide manifold backbone network to mine more discriminative nonlinear spatiotemporal features. Therefore, we first model the nonlinear spatiotemporal co-occurrence of 3D behavior in the high-dimensional Riemannian manifold space. Secondly, we implement a non-Euclidean heterogeneous architecture on the Riemannian manifold so that the backbone network can learn deep spatiotemporal features while preserving the manifold topology. Finally, an end-to-end deep graph similarity-guided learning optimization mechanism is introduced to enable the overall model to fully utilize the complex similarity relationship between manifold features. We have verified our 3D deep heterogeneous manifold network on popular skeleton behavior datasets and achieved competitive results.

A titanosaurian sauropod with Gondwanan affinities in the latest Cretaceous of Europe.

The origin of the last sauropod dinosaur communities in Europe and their evolution during the final 15 million years of the Cretaceous have become a complex phylogenetic and palaeobiogeographic puzzle characterized by the controversy on the alleged coexistence of immigrant, Gondwana-related taxa alongside relictual and insular clades. In this context, we describe a new titanosaurian sauropod dinosaur, Abditosaurus kuehnei gen. et sp. nov., from the Late Cretaceous (Maastrichtian) Tremp Group of Catalonia (Spain). Phylogenetic analyses recover Abditosaurus separately from other European titanosaurs, within a clade of otherwise South American and African saltasaurines. The affinity of the new taxon with southern landmasses is reinforced by spatiotemporal co-occurrence with Gondwanan titanosaurian oospecies in southern Europe. The large size and the lack of osteohistological features potentially related to insular dwarfism or size reduction support the idea that Abditosaurus belongs to an immigrant lineage, unequivocally distinct from some of the island dwarfs of the European archipelago. The arrival of the Abditosaurus lineage to the Ibero-Armorican Island is hypothesized to have occurred during the earliest Maastrichtian (70.6 Ma), probably as a result of a global and regional sea-level drop that reactivated ancient dispersal routes between Africa and Europe. The arrival of large-bodied titanosaurs to the European archipelago produced dramatic changes in its insular ecosystems and important evolutionary changes in its dinosaur faunas, especially with respect to the 'island rule' effect.

Landscape change shifts competitive dynamics between declining at-risk wolverines and range-expanding coyotes, compelling a new conservation focus

Conservation actions need to target ecological mechanisms of species declines to be effective, but these mechanisms are often opaque. Shifting balance of competition is one pathway to species declines. Competitive coexistence is maintained only where competitive pressures balance across space. As resources change and new resources are introduced or disappear across landscapes, so should competition outcomes. We tested how novel anthropogenic disturbances affect this competitive landscape in a region with a diverse large carnivore community. We modelled fine-resolution spatiotemporal co-occurrence of wolverine (Gulo gulo), an at-risk Nearctic facultative scavenger, and other competing carnivores. We posed hypotheses about the different interspecific interactions – competition, predation, and facilitation – that could affect wolverine distribution across a gradient of anthropogenic disturbance. We used an information-theoretic model-selection framework to weigh evidence for hypotheses about the outcomes of interspecific interactions, inferred from habitat selection in relation to co-occurrence or segregation with other carnivores. Wolverine occurrence in space and time was explained by anthropogenic disturbance features and coyote co-occurrence at sites, revealing that coyote occurrence is a synergistic factor with anthropogenic disturbance. Wolverine were generally segregated from coyotes and avoided linear features; however if wolverine and coyotes did co-occur, they were twice as likely to co-occur at sites with linear features. Thus linear features increased opportunity for coyotes—a generalist species thriving in human disturbed landscapes – to compete with wolverines. We suggest this threat of increased competition is a mechanism potentially contributing to broad-scale wolverine range recessions from increasingly disturbed areas. Landscape change manifests as more than just physical disturbances: it alters the ecological processes that structure communities. These processes contribute to declines of species that cannot adapt to the novel disturbance features. We emphasize competition as an overlooked outcome of landscape change that could inform better conservation decisions to stem species declines.

Metacommunity structure reveals that temperature affects the landscape compositional patterns of avian malaria and related haemosporidian parasites across elevations

Metacommunity ecology studies how species compositional patterns and their distributions vary across local and regional scales and provides insights on processes driving the distribution of communities. Avian haemosporidians comprise a diverse and widely distributed parasite taxon; some studies have analyzed their alpha and beta diversity patterns. Yet, metacommunity structures of avian haemosporidians and thus relevant biotic and abiotic variables explaining such structures at the landscape scale (i.e., 10–200 km) have not been assessed. We studied the metacommunity structure of avian haemosporidian mtDNA cyt b lineages and the infected avian host assemblage across four different elevations in Central Veracruz, Mexico. We performed variation-partitioning analyses to evaluate the contribution of host-related traits and climatic variables to the metacommunity. We found a richness of 78 lineages within 38 infected species of birds. At the component community level, we observed that bird species infected with a lower number of parasite lineages (e.g., <3) represented a nested subset of those with a higher number of parasite lineages (e.g., >8) (i.e., nested structure). However, this nested pattern was due to the restricted spatiotemporal co-occurrence of hosts and parasites, given the high degree of turnover across elevations. Host-related traits (functional, transmission-associated, and phylogenetic relationships) only explained a small fraction of the variation (4.4%) in parasite lineage composition across avian hosts. At the habitat level, there was a group turnover by parasite genera across elevation (i.e., quasi-Clementsian structure), which was partly explained by climatic variables (mean annual temperature and annual diurnal range; 27.6%) that may constrain parasite reproduction and vector distribution across the environmental gradient. At the scale of our study, environmental conditions represented a more important driver of avian haemosporidian metacommunity structure than host-related traits, suggesting an important role of environmental filtering structuring parasite assemblages at the landscape level.

Co-occurrence of snow leopard, wolf and Siberian ibex under livestock encroachment into protected areas across the Mongolian Altai

In countries such as Mongolia, where globalization of the cashmere market has spurred herders to massively increase their livestock numbers, an important conservation concern is the effect of livestock encroachment on wildlife. This is especially important inside protected areas (PAs), which often represent the last refugia for threatened large mammals. We used camera-traps to sample four areas with different protection status across the Mongolian Altai Mountains, and targeted a predator-prey system composed of livestock, one large herbivore, the Siberian ibex, and two large carnivores, the snow leopard and the wolf. To determine the effect of livestock on habitat use by the wild species and their spatio-temporal co-occurrence we applied an occupancy framework explicitly developed for modelling interacting species. We recorded a widespread presence of domestic animals in the PAs, and observed avoidance of sites used by livestock by snow leopard and ibex, while wolves tended to co-occur with it. Snow leopard and ibex showed clear mutual co-occurrence, indicating a tight predator-prey relationship. Results provide evidence that, at the scale of sites sampled primarily to maximise snow leopard detections, grazing livestock interferes with wild species by inducing avoidance in snow leopards, and attraction in wolves. We suggest that (1) PAs management should enforce real grazing limitations on the ground, especially in the core areas of the parks; (2) new policies incorporating wildlife conservation into government subsidies to pastoralists should be envisaged, to prevent increasing displacement of snow leopards and ibex; (3) as wolves co-occurred with livestock, with the potential for human-wildlife conflicts, we encourage the use of a set of prevention techniques to mitigate livestock depredation.

Threading the needle: How humans influence predator–prey spatiotemporal interactions in a multiple‐predator system

Perceived predation risk and the resulting antipredator behavior varies across space, time, and predator identity. Communities with multiple predators that interact and differ in their use of space, time of activity, and hunting mode create a complex landscape for prey to avoid predation. Anthropogenic presence and disturbance have the potential to shift interactions among predators and prey and the where and when encounters occur. We examined how white-tailed deer (Odocoileus virginianus) fawn spatiotemporal antipredator behavior differed along an anthropogenic disturbance gradient that had black bears (Ursus americanus), coyotes (Canis latrans), bobcats (Lynx rufus), and humans present. We quantified 1) spatial co-occurrence in species distributions, 2) temporal overlap across the diel cycle, and 3) spatiotemporal associations between humans, bears, coyotes, bobcats, adult male deer, and fawns. We also examined how deer vigilance behavior changed across the anthropogenic disturbance gradient and survey duration. Anthropogenic disturbance influenced spatiotemporal co-occurrence across multiple scales, often increasing spatiotemporal overlap among species. In general, species' spatial co-occurrence was neutral or positive in anthropogenically disturbed environments. Bears and fawns, coyotes and adult male deer, and bobcats and fawns all had higher temporal overlap in the agriculture-development matrix sites. In addition, factors that influenced deer vigilance (e.g., distance to forest edge and predator relative abundance) in the agriculture-development matrix sites did not in the forest matrix site. By taking into account the different antipredator behaviors that can be detected and the different scales these behaviors might occur, we were able to gain a more comprehensive picture of how humans reduce available niche space for wildlife, creating the neutral and positive spatiotemporal associations between species that studies have been seeing in more disturbed areas.

Corrigendum: In the Wrong Place at the Wrong Time: Identifying Spatiotemporal Co-occurrence of Bycaught Common Dolphins and Fisheries in the Bay of Biscay (NE Atlantic) From 2010 to 2019

Corrigendum: In the Wrong Place at the Wrong Time: Identifying Spatiotemporal Co-occurrence of Bycaught Common Dolphins and Fisheries in the Bay of Biscay (NE Atlantic) From 2010 to 2019

In the Wrong Place at the Wrong Time: Identifying Spatiotemporal Co-occurrence of Bycaught Common Dolphins and Fisheries in the Bay of Biscay (NE Atlantic) From 2010 to 2019

The first Unusual Mortality Event (UME) related to fishing activity along the Atlantic coast recorded by the French Stranding Network was in 1989: 697 small delphinids, mostly common dolphins, washed ashore, most of them with evidence of having been bycaught. Since then, UMEs of common dolphins have been observed nearly every year in the Bay of Biscay; unprecedented records were broken every year since 2016. The low and unequally distributed observation efforts aboard fishing vessels in the Bay of Biscay, as well as the lack of data on foreign fisheries necessitated the use of complementary data (such as stranding data) to elucidate the involvement of fisheries in dolphin bycatch. The aim of this work was to identify positive spatial and temporal correlations between the likely origins of bycaught stranded common dolphins (estimated from a mechanistic drift model) and fishing effort statistics inferred from Vessel Monitoring System (VMS) data on vessels &amp;gt;12 m long. Fisheries whose effort correlated positively with dolphin mortality areas after 2016 included French midwater trawlers, French Danish seiners, French gillnetters, French trammel netters, Spanish bottom trawlers, and Spanish gillnetters. For the French fleet only, logbook declarations, sales, and surveys carried out by Ifremer were integrated into fishing effort data. Six fleets were active in common dolphin bycatch areas at least twice between 2016 and 2019: gillnetters fishing hake, trammel netters fishing anglerfish, bottom pair trawlers fishing hake, midwater pair trawlers fishing sea bass and hake, and Danish seiners fishing whiting. Except for changes in hake landings in some fisheries, there were no notable changes in total fishing effort practice (gear or target species) based on the data required by the ICES and Council of the European Union that could explain the large increase in stranded common dolphins recorded along the French Atlantic coast after 2016. Small scale or unrecorded changes could have modified interactions between common dolphins and fisheries, but could not be detected through mandatory data-calls. The recent increase in strandings of bycaught common dolphins could have been caused by changes in their distribution and/or ecology, or changes in fishery practices that were undetectable through available data.

Spatiotemporal interactions of native and introduced salmonid top predators in a large lake: implications for species restoration

Animal interactions are an integral part of a community’s function, with influences ranging from the spatiotemporal habitat use of species to population effects to ecosystem management. Numerous non-native species are established or maintained through stocking in freshwater ecosystems with the potential to affect restoration of native species. Using acoustic telemetry, this study quantified the spatiotemporal co-occurrence of the native top-predator lake trout (Salvelinus namaycush) with non-native Chinook salmon (Oncorhynchus tshawytscha) in Lake Ontario over 2.5 years. Core home range overlap was observed during the summer, with depth acting as a mechanism of segregation, but with potential for interactions during vertical exploration. Fine-scale individual pairwise interactions confirmed the home range results. No horizontal overlap was observed during the winter and spring, but confidence was lower due to poor instrument coverage in deeper water, which the two species may frequent in these seasons. These results demonstrate the importance of depth in understanding fish interactions and highlight the usefulness of considering pairwise species interactions for understanding ecosystem community function to resource managers with multiple projects involving both native and non-native species.