Marine and Freshwater Research最新文献

Satellite telemetry has revolutionised the study of animal movement, particularly for mobile marine animals, whose movements and habitat make consistent, long-term observation difficult.

Summarise the movements of Rio Lady, a mature female whale shark (Rhincodon typus), to characterise these movements, and to predict expected behaviour throughout the Gulf of Mexico (GOM).

Rio Lady was tracked using satellite telemetry for over 1600 days, generating over 1400 locations and travelling over 40,000 km. State–space and move persistence modelling enabled characterisation of behaviour, and machine learning (ML) enabled the development of habitat-suitability models to predict habitat utilisation, on the basis of location transmissions and their environmental covariates.

Rio Lady exhibited annually consistent patterns of movements among three regions within the GOM. Final ML models produced seasonally dynamic predictions of habitat use throughout the GOM.

The application of these methods to long-term location data exemplifies how long-term movement patterns and core areas can be discovered and predicted for marine animals.

Despite our limited dataset, our integrative approach advances methods to summarise and predict behaviour of mobile species and improve understanding of their ecology.

The Indo-Pacific tarpon (Megalops cyprinoides) is a diadromous species, for which limited information is available regarding demographics and exploitation levels.

We aimed to fill critical knowledge gaps on population dynamics and exploitation levels of M. cyprinoides along the south-western coast of India.

Structured length frequency-based population dynamics models were used to determine the growth, mortality and exploitation levels of the species.

Habitat-related variations in growth patterns were found, with positive allometric growth in the marine (b = 3.208), and negative allometric growth in freshwater (2.759) and estuarine (2.876) habitats. Different estimates of natural mortality suggested high fishing pressure, potentially threatening the long-term sustainability of local M. cyprinoides populations.

This study provides the first comprehensive estimates of demographic parameters of M. cyprinoides, and contributes significantly to our knowledge required for the management of Indo-Pacific tarpon fisheries. The critical need to address overfishing and habitat degradation to ensure long-term sustainability of local populations is highlighted.

Knowledge generated through this study will help inform improved conservation assessments of this ‘Data Deficient’ species, in addition to informing the development and implementation of conservation and management plans.

The rapid intensification of human impacts on rivers is a major threat for the provision of key ecosystem services for societies. In this context, many of these services rely on the ecological functions performed by macroinvertebrates. Therefore, understanding the relationship between human impacts and the functional diversity of macroinvertebrates is urgent.

We aimed at analysing the relationship between human impact and the functional diversity of macroinvertebrates in rivers of agricultural lands of Argentina.

We sampled seven rivers of central Argentina during three seasons (spring, autumn and winter). In each site, we measured pesticides, metals, chlorophyll-a, nutrients and the taxonomic and functional diversity of macroinvertebrates. We summarised the environmental information using principal-component analysis and assessed the relationship between the assemblage metrics with the first three principal components.

We found that functional metrics presented a negative relationship with the human-impact gradient in the three seasons. However, taxonomic metrics showed less sensitivity.

The functional diversity of macroinvertebrates, but not the taxonomic diversity, decreases with increasing human impacts on rivers.

Our result implies that functional metrics should be included in environmental monitoring in agricultural lands of Argentina.

A reliable bioindicator index of eutrophication is still lacking for monitoring zooplankton in saline, coastal lagoons. We aimed to establish plankton bioindicators of eutrophication that are not affected by salinity variation, which is essential for environmental monitoring of coastal lagoons.

Sampling was conducted for microprotozooplankton, chlorophyll-a, nutrients and sterols in eight tropical coastal lagoons with different saline and nutrient concentrations. A testate amoeba and rotifer bioindication index of eutrophication (T&R index) was proposed after establishing a list of indicator species on the basis of five statistical criteria and one bibliographic criterion.

Selected species, based on the six criteria, were Arcella hemisphaerica, Brachionus plicatilis, Galeripora (Arcella) discoides, Netzelia corona, Lecane closterocerca, Lepadela patella and Testudinela patina. Biodiversity measures, traditionally used in biomonitoring, varied negatively with salinity (r = −0.60 for Margalef richness and −0.59 for Shannon diversity) and were not correlated with nutrient concentrations. Conversely, the T&R index showed no correlation with salinity (r = 0.002) and was positively correlated with the trophic-state index (TSI) (r = 0.62).

Our results showed that specific patterns of indicator microprotozooplankton species can effectively diagnose organic pollution in coastal lagoons and their use is a more suitable approach than is the use of community indicators such as richness, evenness and diversity, traditionally used for that task.

The application of deep learning to monitor tropical freshwater fish assemblages and detect potential anthropogenic impacts is poorly understood.

This study aimed to compare the results between trained human observers and deep learning, using the fish monitoring program for impact detection at Ranger Uranium Mine as a case study.

Fish abundance (MaxN) was measured by trained observers and deep learning. Microsoft’s Azure Custom Vision was used to annotate, label and train deep learning models with fish imagery. PERMANOVA was used to compare method, year and billabong.

Deep learning model training on 23 fish taxa resulted in mean average precision, precision and recall of 83.6, 81.3 and 89.1%, respectively. PERMANOVA revealed significant differences between the two methods, but no significant interaction was observed in method, billabong and year.

These results suggest that the distribution of fish taxa and their relative abundances determined by deep learning and trained observers reflect similar changes between control and exposed billabongs over a 3-year period.

The implications of these method-related differences should be carefully considered in the context of impact detection, and further research is required to more accurately characterise small-growing schooling fish species, which were found to contribute significantly to the observed differences.

River regulation affect freshwater species by disrupting the natural flow regime and connectivity.

Investigate the impact of river regulation on platypus populations on four regulated rivers within the northern Murray–Darling Basin.

Assessment of hydrology, live trapping downstream of large dams, multi-species environmental DNA surveys in upstream and downstream sections.

There were significant changes in flow seasonality and cold-water pollution as a result of river regulation. Upstream sections experienced prolonged periods of ceased flow, most recently during an extreme drought between 2017 and 2020. eDNA surveys detected platypuses downstream of all dams but failed to find evidence of them upstream in two rivers, indicating possible local extinctions. Capture of four platypuses in the Severn River and four, in very poor condition, in the Peel River, and none in the Gwydir River or Pike Creek–Dumaresq River. Significant differences in macroinvertebrate communities, implying possible impacts on platypus diet.

River regulation and habitat fragmentation affect platypus populations, namely disappearance from upstream sections, low downstream capture rates and the poor body condition.

Urgent need for catchment-scale river management strategies that preserve ecological functions and connectivity and improve resilience to protect and sustain platypus populations, indicating directions for future research and conservation efforts.

The prediction of freshwater quality is important for detecting pollution risks and assessing changes in freshwater ecosystems. As a high-precision interpolation method, Kriging was able to predict freshwater quality by using previously monitored data. However, how to select the key parameters, regression functions and correlation functions of Kriging method in the process of improving prediction accuracy is still a bottleneck.

This study aims to propose an adaptive weighted-average Kriging (AWAK) method to further enhance the accuracy of freshwater-quality predictions.

The AWAK method consists of four main steps. First, the key parameters influencing pollution indicators are selected by FPS method. Subsequently, six different Kriging candidate models are constructed by using regression and correlation functions with different characteristics. Then, an enhanced-likelihood function is used to determine the weights of the six Kriging candidate models. Finally, AWAK is built by weighted sum of these six Kriging models.

The AWAK outperformed traditional Kriging in predicting pH and dissolved oxygen, significantly reducing prediction errors.

By employing the AWAK method, this study successfully improved the accuracy of freshwater-quality predictions.

The introduction of the AWAK provides an effective approach in the field of freshwater ecology.

Disentangling mechanisms influencing the seasonal and spatial distribution of fish is essential to understanding population dynamics. In the south-western Atlantic, the sympatric mullets Mugil curema and M. rubrioculus are closely related and use habitat similarly. However, which processes allow their coexistence is unknown.

We tested the hypothesis that the two species exhibit temporally and spatially decoupled habitat-use patterns to allow sympatry due to different responses to environmental drivers.

Bayesian zero-inflated count models were used to unravel spatial and temporal distribution patterns of those species in a south-western Atlantic lagoon.

The two species display different distributions within the estuary, being spatially and temporally segregated, with M. curema mostly inhabiting the inner estuary and M. rubrioculus inhabiting coastal marine habitats.

This decoupling in habitat use might be driven by distinct responses to environmental variability: M. curema is influenced by factors such as temperature and dissolved oxygen, whereas M. rubrioculus is influenced by variations in salinity. We suggest that the studied species evolved divergent responses to environmental variation to allow coexistence.

This study suggests that environmental factors drive mullet abundance, and that zero-inflated count models incorporating those drivers are therefore useful in predicting their spatial and temporal distribution, as well as in standardising abundance trends for stock assessment efforts.

Metabolic balance determines whether an ecosystem acts as a source or sink of carbon dioxide (CO₂) and considering that a substantial portion of inland aquatic ecosystems act as a source of CO₂ to the atmosphere, it is important to highlight that there is still no agreement on how global change will affect the ecosystem metabolic-balance response. It then becomes more important to study the interactions between global-change drivers and aquatic metabolism.

Assess possible shifts in ecosystem metabolic balance owing to global-change factors.

Collapsed factorial designs and novel experimental units have been used to study responses to future conditions.

In the study site, bacterial production was not affected by an increased temperature alone; however, increased nutrient availability may unmask UV or CO₂ as a source of stress to bacteria. A synergistic effect between temperature and the combined effect of nutrients and CO₂ on primary producers was also found.

In future scenarios, some heterotrophic inland water ecosystems may shift from heterotrophic to autotrophic states and therefore act as CO₂ sinks.

This study provides a framework to support a deepening of knowledge on this topic.

Restored mangrove forests are threatened by the restricted range of species used. Xylocarpus granatum has excellent timber and could add value in mangrove plantings.

Forest structure and environmental variables were measured at 40 sites in northern Australia and Papua New Guinea. Tree growth was measured at 18 sites. Boosted regression tree modelling identified environmental predictor variables for above-ground biomass for X. granatum (AGB_X), mean annual increment in diameter at breast height (DBH) for X. granatum (MAI_X) and wood production (P_W).

To determine the environmental conditions under which X. granatum has the greatest biomass and growth.

Increasing stem density, basal area and AGB of other tree species were most important in limiting AGB_X. MAI_X (range 0.03–0.55 cm year⁻¹) was greatest when sediment redox potential was >100 mV and mean annual catchment rainfall was >4000 mm. MAI_X increased with an increasing mean minimum air temperature and sediment percentage silt and percentage phosphorus. P_W declined with the stem density of all species and increased with an increasing percentage silt and when sediment redox potential was >180 mV.

Under optimal conditions, X. granatum grows as fast as most mangrove species and could make an excellent, harvestable addition to the species mix used in restoration projects.