Recent years have seen an increasing interest in individual behavioral variation. However, the implications of such variation for population dynamics are often unknown. We studied the dynamics of a bottlenose dolphin (Tursiops truncatus gephyreus) population from southern Brazil, where some individuals forage cooperatively with artisanal fishermen. We fitted mark‐recapture models to 10 yr of photo‐identification data to investigate the influence of this foraging specialization on dolphins’ population parameters, controlling for sex and ranging behavior. We estimated adult survival to be high (0.949 ± 0.015 SE), weakly influenced by home range size, sex or the frequency of interaction with fishermen. The slightly higher survival probability for individuals with smaller home ranges could stem from the benefits of reduced spatial requirements implied by the specialized foraging. Foraging also influenced the probability of resighting individuals, and there was no temporary or permanent emigration. Abundance fluctuated slightly over the years from 54 (95% CI = 49–59) to 60 (95% CI = 52–69) individuals, with no evident population trend. Despite such apparent population stability, we confirm this population remains small and geographically isolated which may threaten its viability and the viability of its unusual, localized foraging specialization. Our study also illustrates how accounting for individual variation can portray animal population dynamics more realistically.
Human Impacts on the Environment
Reactions of singing behavior of individual humpback whales (Megaptera novaeangliae) to a specific shipping noise were examined. Two autonomous recorders separated by 3.0 km were used for the acoustic monitoring of each individual song sequence. A passenger-cargo liner was operated once per day, and other large ship noise was excluded given the remote location of the Ogasawara Islands, 1000 km south of Tokyo. In total, locations of between 26 and 27 singers were measured acoustically using time arrival difference at both stereo recorders on the ship presence and absence days, respectively. Source level of the ship (157 dB rms re 1μPa) was measured separately in deep water. Fewer whales sang nearby, within 500 m, of the shipping lane. Humpback whales reduced sound production after the ship passed, when the minimum distance to the whale from the ship trajectory was 1200 m. In the Ogasawara water, humpback whales seemed to stop singing temporarily rather than modifying sound characteristics of their song such as through frequency shifting or source level elevation. This could be a cost effective adaptation because the propagation loss at 500 m from the sound source is as high as 54 dB. The focal ship was 500 m away within several minutes. Responses may differ where ship traffic is heavy, because avoiding an approaching ship may be difficult when many sound sources exist.
Aquatic ecosystems are under severe pressure. Human activities introduce an array of pressures that impact ecosystems and their components. In this study we focus on the aquatic domains of fresh, coastal and marine waters, including rivers, lakes and riparian habitats to transitional, coastal as well as shelf and oceanic habitats. In an environmental risk assessment approach, we identified impact chains that link 45 human activities through 31 pressures to 82 ecosystem components. In this linkage framework >22,000 activity-pressure-ecosystem component interactions were found across seven European case studies. We identified the environmental impact risk posed by each impact chain by first categorically weighting the interactions according to five criteria: spatial extent, dispersal potential, frequency of interaction, persistence of pressure and severity of the interaction, where extent, dispersal, frequency and persistence account for the exposure to risk (spatial and temporal), and the severity accounts for the consequence of the risk. After assigning a numerical score to each risk criterion, we came up with an overall environmental impact risk score for each impact chain. This risk score was analysed in terms of (1) the activities and pressures that introduce the greatest risk to European aquatic domains, and (2) the aquatic ecosystem components and realms that are at greatest risk from human activities. Activities related to energy production were relevant across the aquatic domains. Fishing was highly relevant in marine and environmental engineering in fresh waters. Chemical and physical pressures introduced the greatest risk to the aquatic realms. Ecosystem components that can be seen as ecotones between different ecosystems had high impact risk. We show how this information can be used in informing management on trade-offs in freshwater, coastal and marine resource use and aid decision-making.
Based on a validated underwater oil spill model and the hydrodynamic background provided by an unstructured grid, finite-volume, coastal ocean model (FVCOM), a series of numerical experiments are conducted to study the impact of error in ocean dynamical background currents on the 3D transport of underwater spilled oil, in terms of three metrics including oil centroid position, sweeping area, and sweeping volume. Numerical result shows that a larger error in ocean dynamical background currents results in a larger model error expectation and uncertainty for all three metrics. As model time increases, the model error mainly increases and the error growth rate varies unevenly. The sensitivity of the oil spill model to background current error can be interpreted as an integrated result of the temporal and spatial variations of the background current and the movement of oil droplets of different sizes.
Bottom trawlers land around 19 million tons of fish and invertebrates annually, almost one-quarter of wild marine landings. The extent of bottom trawling footprint (seabed area trawled at least once in a specified region and time period) is often contested but poorly described. We quantify footprints using high-resolution satellite vessel monitoring system (VMS) and logbook data on 24 continental shelves and slopes to 1,000-m depth over at least 2 years. Trawling footprint varied markedly among regions: from <10% of seabed area in Australian and New Zealand waters, the Aleutian Islands, East Bering Sea, South Chile, and Gulf of Alaska to >50% in some European seas. Overall, 14% of the 7.8 million-km2 study area was trawled, and 86% was not trawled. Trawling activity was aggregated; the most intensively trawled areas accounting for 90% of activity comprised 77% of footprint on average. Regional swept area ratio (SAR; ratio of total swept area trawled annually to total area of region, a metric of trawling intensity) and footprint area were related, providing an approach to estimate regional trawling footprints when high-resolution spatial data are unavailable. If SAR was ≤0.1, as in 8 of 24 regions, there was >95% probability that >90% of seabed was not trawled. If SAR was 7.9, equal to the highest SAR recorded, there was >95% probability that >70% of seabed was trawled. Footprints were smaller and SAR was ≤0.25 in regions where fishing rates consistently met international sustainability benchmarks for fish stocks, implying collateral environmental benefits from sustainable fishing.
Reef ecosystems are amply distributed and ecologically relevant in Mexico, however, there is not an integrated inventory of these ecosystems, and information about uses and pressures is disperse. With the aim of generating updated information that allows to know the presence and distribution of the different types of reefs (coral, rocky-coral, rocky, and rocky with Macrocystis pyrifera), as well as to know the uses and pressures to which they are subjected. In this article we present an inventory of the 755 reef ecosystems known in Mexico based in a literature review of 194 documents and validated by informal interviews to key Mexican experts. Mexican reefs are distributed in seven regions identified for reef management purposes, according to the combination of eight maritime regionalization proposals of the Mexican seas. The main uses of reef ecosystems are fishing, tourism, nautical, and mining, which produce eight main pressures: pollution, habitat fragmentation, coral bleaching, overfishing, exotic species introduction, sedimentation, coral mortality, and coral diseases. These uses and pressures are distributed heterogeneously in the seven reef regions. The main conservation tool used by Mexican Federal Government to protect these reefs are the Marine Protected Areas (MPAs). Almost 45% of the listed reefs are within one of the 30 Mexican MPAs, being the coral reefs the ones that predominate in this protection scheme. In this research we present relevant information for the management of the reef ecosystems of Mexico, which support the debate on the analysis of public policies for their conservation.
Highly connected networks generally improve resilience in complex systems. We present a novel application of this paradigm and investigated the potential for anthropogenic structures in the ocean to enhance connectivity of a protected species threatened by human pressures and climate change. Biophysical dispersal models of a protected coral species simulated potential connectivity between oil and gas installations across the North Sea but also metapopulation outcomes for naturally occurring corals downstream. Network analyses illustrated how just a single generation of virtual larvae released from these installations could create a highly connected anthropogenic system, with larvae becoming competent to settle over a range of natural deep-sea, shelf and fjord coral ecosystems including a marine protected area. These results provide the first study showing that a system of anthropogenic structures can have international conservation significance by creating ecologically connected networks and by acting as stepping stones for cross-border interconnection to natural populations.
Submarine power cables (SPC) have been in use since the mid-19th century, but environmental concerns about them are much more recent. With the development of marine renewable energy technologies, it is vital to understand their potential impacts. The commissioning of SPC may temporarily or permanently impact the marine environment through habitat damage or loss, noise, chemical pollution, heat and electromagnetic field emissions, risk of entanglement, introduction of artificial substrates, and the creation of reserve effects. While growing numbers of scientific publications focus on impacts of the marine energy harnessing devices, data on impacts of associated power connections such as SPC are scarce and knowledge gaps persist. The present study (1) examines the different categories of potential ecological effects of SPC during installation, operation and decommissioning phases and hierarchizes these types of interactions according to their ecological relevance and existing scientific knowledge, (2) identifies the main knowledge gaps and needs for research, and (3) sets recommendations for better monitoring and mitigation of the most significant impacts. Overall, ecological impacts associated with SPC can be considered weak or moderate, although many uncertainties remain, particularly concerning electromagnetic effects.
Some species may be more important in transferring the complex effects of multiple human stressors through marine food‐webs. Here we show a novel approach to help inform conservation management in identifying such species. Simulating changes in biomass between species from the interaction effects of ocean warming and ocean acidification, and fisheries to year 2050 on the south‐eastern Australian marine system, we constructed annual interaction effect networks (IEN's). Each IEN was composed of the species linked by either an additive (sum of the individual stressor response), synergistic (lower biomass compared with additive effects) or antagonistic (greater biomass compared with additive effects) response. Structurally, over the simulation period, the number of species and links in the synergistic IEN's increased and the network structure became more stable. The stability of the antagonistic IEN's decreased and became more vulnerable to the loss of species. In contrast, there was no change in the structural attributes of species linked by an additive response. Using indices of species importance common in food‐web and network theory, we identified the most important species within each IEN for transferring the interaction stressor effect on changes in biomass via local, intermediate and global interaction pathways. Mid trophic level mesopelagic fish species were most often identified as the key species within the synergistic IEN's and phytoplankton or zooplankton within the antagonistic IEN's. For the additive response commonly assumed in conservation management demersal fish species were identified by all of the indices. Apart from identifying the most important species, we also identified other important species for transferring the different interaction effects. Knowing the most important species for transferring synergistic or antagonistic responses may help inform conservation strategies for conserving ecosystems under increasing multiple stressor impacts.
Coastal ecosystems are exposed to multiple anthropogenic stressors such as fishing, pollution, and climate change. Ecosystem-based coastal management requires understanding where the combination of multiple stressors has large cumulative effects and where actions to address impacts are most urgently needed. However, the effects of multiple stressors on coastal and marine ecosystems are often non-linear and interactive. This complexity is not captured by commonly used spatial models for mapping human impacts. Flexible statistical and machine learning models like random forests have thus been used as an alternative modeling approach to identify important stressors and to make spatial predictions of their combined effects. However, tests of such models' prediction skill have been limited. Therefore, we tested how well ten statistical and machine learning methods predicted three ecological indicators of coastal marine ecosystem condition (kelp biodiversity, fish biomass, and rocky intertidal biodiversity) off California, USA. Spatial data representing anthropogenic stressors and ocean uses as well as natural gradients were used as predictors. The models' prediction errors were estimated by double spatial block cross-validation. The best models achieved mean squared errors about 25% lower than a null model for kelp biodiversity and fish biomass; none of the tested models worked well for rocky intertidal biodiversity. The models captured general trends, but not local variability of the indicators. For kelp biodiversity, the best performing method was principal components regression. For fish biomass, the best performing method was boosted regression trees. However, after tuning, this model did not include any interactions between stressors, and ridge regression (a constrained linear model) performed almost as well. While in theory flexible machine learning methods are required to represent the complex stressor-ecosystem state relationships revealed by experimental ecologists, with our data, this flexibility could not be harnessed because more flexible models overfitted due to small sample sizes and low signal-to-noise ratio. The main challenge for harnessing the flexibility of statistical and machine learning methods to link ecological indicators and anthropogenic stressors is obtaining more suitable data. In particular, better data describing the spatial and temporal distribution of human uses and stressors are needed. We conclude by discussing methodological implications for future research.