Identifying priority habitat for conservation and management of Australian humpback dolphins within a marine protected area

Spatial prioritization of dugong habitats in India can contribute towards achieving the 30 × 30 global biodiversity target

Indian coastal waters are critical for dugong populations in the western Indian Ocean. Systematic spatial planning of dugong habitats can help to achieve biodiversity conservation and area-based protection targets in the region. In this study, we employed environmental niche modelling to predict suitable dugong habitats and identify influencing factors along its entire distribution range in Indian waters. We examined data on fishing pressures collected through systematic interview surveys, citizen-science data, and field surveys to demarcate dugong habitats with varying risks. Seagrass presence was the primary factor in determining dugong habitat suitability across the study sites. Other variables such as depth, bathymetric slope, and Euclidean distance from the shore were significant factors, particularly in predicting seasonal suitability. Predicted suitable habitats showed a remarkable shift from pre-monsoon in Palk Bay to post-monsoon in the Gulf of Mannar, indicating the potential of seasonal dugong movement. The entire coastline along the Palk Bay-Gulf of Mannar region was observed to be at high to moderate risk, including the Gulf of Mannar Marine National Park, a high-risk area. The Andaman Islands exhibited high suitability during pre- and post-monsoon season, whereas the Nicobar Islands were highly suitable for monsoon season. Risk assessment of modelled suitable areas revealed that < 15% of high-risk areas across Andaman and Nicobar Islands and Palk Bay and Gulf of Mannar, Tamil Nadu, fall within the existing protected areas. A few offshore reef islands are identified under high-risk zones in the Gulf of Kutch, Gujarat. We highlight the utility of citizen science and secondary data in performing large-scale spatial ecological analysis. Overall, identifying synoptic scale 'Critical Dugong Habitats' has positive implications for the country's progress towards achieving the global 30 × 30 target through systematic conservation planning.

Modeling Spatio-Temporal Variations in the Habitat Utilization of Swordtip Squid (Uroteuthis edulis) in the East China Sea and Southern Yellow Sea

Accurately modeling the distribution of keystone species is of utmost importance to gain a comprehensive understanding of their complex ecological dynamics and to develop effective strategies for sustainable scientific management. In the coastal China ecosystem, the swordtip squid (Uroteuthis edulis) stands out as a keystone species with significant commercial and ecological value. Despite its importance, research on the ecological dynamics of this species remains limited and requires further investigation. To investigate the spatial and temporal variability in the distribution of U. edulis and identify the key environmental drivers in the East China Sea (ECS) and southern Yellow Sea across different seasons, we generated ensemble models using oceanographic variables and fishery-independent scientific survey data collected from 2016 to 2018. Our results revealed that U. edulis predominantly inhabited the central and southern regions of the ECS throughout the year. The primary environmental variables driving its distribution varied by season, with the sea surface temperature being the most important in spring, sea surface height in summer and autumn, and depth in winter. During summer and autumn, the suitable habitats of U. edulis were found to be largest and extended northwards towards the coastline. However, they migrated southwards to the waters near the edge of the ECS continental shelf with smaller suitable areas in the spring and winter. These results suggested that U. edulis exhibited season-specific habitat preferences and responded to changing environmental conditions throughout the year. The observed seasonal distribution patterns were likely influenced by the fluctuating mixture of waters (ocean currents) from different sources, with varying physical and chemical characteristics throughout the year. Our study provides baseline data for comprehending the population dynamics of U. edulis and highlights the significance of considering species' habitat preferences in a dynamic environment.

Habitat partitioning, co-occurrence patterns, and mixed-species group formation in sympatric delphinids

Numerous species have been reported to form mixed-species groups, however, little is known about the interplay between niche partitioning and mixed-species group formation. Furthermore, it is often unclear whether species come together by chance due to overlapping habitat preferences, by shared attraction to resources, or by attraction between them. We assessed habitat partitioning, co-occurrence patterns, and mixed-species group formation of sympatric Australian humpback (Sousa sahulensis) and Indo-Pacific bottlenose dolphins (Tursiops aduncus) around the North West Cape, Western Australia, with a joint species distribution model and temporal analyses of sighting data. Australian humpback dolphins preferred shallower and more nearshore waters than Indo-Pacific bottlenose dolphins, yet these species co-occurred more often than expected by chance given shared responses to environmental variables. Indo-Pacific bottlenose dolphins were sighted more often than Australian humpback dolphins during the afternoon, however, we did not find any temporal patterns in the occurrence of mixed-species groups. We propose that the positive association in the species' occurrence indicates the active formation of mixed-species groups. By evaluating habitat partitioning and co-occurrence patterns, this study provides direction for future work which should proceed to investigate the benefits that these species may gain from grouping with each other.

Modeling present and future distribution of plankton populations in a coastal upwelling zone: the copepod Calanus chilensis as a study case

Predicting species distribution in the ocean has become a crucial task to assess marine ecosystem responses to ongoing climate change. In the Humboldt Current System (HCS), the endemic copepod Calanus chilensis is one of the key species bioindicator of productivity and water masses. Here we modeled the geographic distribution of Calanus chilensis for two bathymetric ranges, 0-200 and 200-400 m. For the 0-200 m layer, we used the Bayesian Additive Regression Trees (BART) method, whereas, for the 200-400 m layer, we used the Ensembles of Small Models (ESMs) method and then projected the models into two future scenarios to assess changes in geographic distribution patterns. The models were evaluated using the multi-metric approach. We identified that chlorophyll-a (0.34), Mixed Layer Depth (0.302) and salinity (0.36) explained the distribution of C. chilensis. The geographic prediction of the BART model revealed a continuous distribution from Ecuador to the southernmost area of South America for the 0-200 m depth range, whereas the ESM model indicated a discontinuous distribution with greater suitability for the coast of Chile for the 200-400 m depth range. A reduction of the distribution range of C. chilensis is projected in the future. Our study suggests that the distribution of C. chilensis is conditioned by productivity and mesoscale processes, with both processes closely related to upwelling intensity. These models serve as a tool for proposing indicators of changes in the ocean. We further propose that the species C. chilensis is a high productivity and low salinity indicator at the HCS. We recommend further examining multiple spatial and temporal scales for stronger inference.

Identifying High Stranding Risk Areas of the Yangtze Finless Porpoise via Remote Sensing and Hydrodynamic Modeling

Freshwater cetaceans that inhabit river basins with seasonally changing hydrological regimes have a higher risk of stranding, leading to increased mortality and population decline. In Poyang Lake, the stranding risk of the critically endangered Yangtze finless porpoise are high, due to the significant differences in hydrological and landscape conditions between the flood and dry seasons. However, this stranding information is not well recorded, resulting in poorly guided investigation and rescue efforts. We here employ remote sensing and hydrodynamic modeling to obtain four evaluation indicators in dry, normal, and flood scenarios in Poyang Lake. Results show that nearly 50% of the largest habitat range of the porpoises in the flood season will be land areas during the dry season, and that landscape fragmentation between land and water has increased over the past three decades. In all scenarios, the mean water depth of the habitat varied from 1.77 to 4.89 m from September–February. In the dry scenario, about 59% of the habitat experiences a water depth of <1 m within 15 days. The high stranding risk area is 284.54 km2 in the dry scenario, >251.04 km2 in the normal scenario, and >90.12 km2 in the flood scenario. High-stranding risk areas are located within city boundaries, where porpoise stranding cases have been recorded, especially in Duchang, which has the most high-risk areas in all scenarios. In all scenarios, the high stranding risk area has an average bathymetry of 7.81 m and an average water depth between 1.75 and 5.54 m. Our results can guide future investigations to establish sound stranding networks, and the methods proposed here are also applicable to studies of other freshwater cetaceans facing severe stranding risk.

Long-Term Conservation Effects of Protected Areas in Stochastic Population Dynamics

Terrestrial and marine protected areas are essential tools in mitigating anthropogenic impacts and promoting population persistence and resource sustainability. Adequately implemented protected areas (PAs) aim to promote conservation by increasing population size and reducing its variability. To resolve how these effects depend on PA features, I develop and analyze new models of stochastic processes that encompass the fluctuations generated by demographic or environmental stochasticity in PAs management. The stochastic model is built upon individual processes. In the model, density-independent mortality, migration between PAs and non-PAs, organism preference for PAs, and size characterize the features of the PA. The effect of PAs size is also examined. The long-term conservation effects are quantified using the coefficient of variation (CV) of population size in PAs, where a lower CV indicates higher robustness in stochastic variations. The results from this study demonstrate that sufficiently reduced density-independent mortality in PAs and high site preference for PAs and immigration rate into PAs are likely to decrease the CV. However, different types of stochasticity induce rather different consequences: under demographic stochasticity, the CV is always reduced because PAs increase the population size therein, but an increased population size by PAs does not always decrease the CV under environmental stochasticity. The deterministic dynamics of the model are investigated, facilitating effective management decisions.

Estimating the impact of climate change on the potential distribution of Indo-Pacific humpback dolphins with species distribution model

As IUCN critically vulnerable species,the Indo-Pacific humpback dolphins (Sousa chinensis) have attracted great public attention in recent years. The threats of human disturbance and environmental pollution to this population have been documented extensively. However, research on the sensitivity of this species to climate change is lacking. To understand the effect of climate change on the potential distribution of Sousa chinensis, we developed a weighted ensemble model based on 82 occurrence records and six predictor variables (e.g., ocean depth, distance to shore, mean temperature, salinity, ice thickness, and current velocity). According to the true skill statistic (TSS) and the area under the receiver operating characteristic curve (AUC), our ensemble model presented higher prediction precision than most of the single-algorithm models. It also indicated that ocean depth and distance to shore were the most important predictors in shaping the distribution patterns. The projections for the 2050s and 2100s from our ensemble model indicated a severe adverse impact of climate change on the Sousa chinensis habitat. Over 75% and 80% of the suitable habitat in the present day will be lost in all representative concentration pathway emission scenarios (RCPS) in the 2050s and 2100s, respectively. With the increased numbers of records of stranding and deaths of Sousa chinensis in recent years, strict management regulations and conservation plans are urgent to safeguard the current suitable habitats. Due to habitat contraction and poleward shift in the future, adaptive management strategies, including designing new reserves and adjusting the location and range of reserves according to the geographical distribution of Sousa chinensis, should be formulated to minimize the impacts of climate change on this species.