Abstract:Sediment plays a pivotal role in determining the physical, chemical and biological integrity of aquatic ecosystems. A range of factors influences the impacts of sediment pressures on aquatic biota, including concentration, duration of exposure, composition and particle size. In recognition of the need to assess environmental status for sediment and mitigate excessive sediment pressures on aquatic habitats, both water column and river substrate metrics have been proposed as river sediment targets. Water column metrics include light penetration, turbidity, sediment concentration summary statistics and sediment regimes. Substrate metrics include embeddedness, the fredle index and riffle stability. Identification of meaningful numeric targets along these lines has, however, been undermined by various issues including the uncertainty associated with toxicological dose-response profiles and the impracticalities of deploying statistically robust sampling strategies capable of supporting catchment-scale targets. Many of the thresholds reported are based on correlative relationships that fail to capture the specific mechanisms controlling sediment impacts on aquatic habitats and are stationary in nature. Temporal windows represented by the key life stages of specific species must be given greater emphasis. Given such issues and the need to support the revision of sediment targets for river catchment management, it is proposed that greater emphasis should be placed on developing generic modelling toolkits with the functionality for coupling current or future projected sediment regimes with biological response for a range of biota. Such tools should permit the identification of river catchment-specific targets within a national context, based on biological effect and incorporate sufficient flexibility for utilizing updated physical, chemical, biological and catchment attribute data. Confidence will continue to be required in compliance screening to ensure cost-effective management programmes for avoiding disproportionate investment in impacted river catchments.
Human-wildlife encounters are becoming increasingly frequent across the globe, often leading people to interact with and feed wild animals and impacting animal behaviour and ecology. Although the nature of human-wildlife interactions has been well documented across a number of species, we still have limited understanding as to why some individual animals interact more frequently with humans than others. Additionally, we lack a comprehensive understanding of how these interactions influence animal social networks. Using behavioural data from a group of moor macaque monkeys (Macaca maura), we used permutation-based linear regression analyses to understand how life history and social network factors jointly explain interindividual variation in tendency to interact with humans along a provincial road in South Sulawesi, Indonesia. As our study group spent only a portion of their time in proximity to humans, we also examined how social network structure changes in response to human presence by comparing social networks in the forest to those along the road. We found that sex, individual network position, and associate network position interact in complex ways to influence individual behaviour. Individual variation in tendency to be along the road caused social networks to become less cohesive when in proximity to humans. This study demonstrates that nuanced intragroup analyses are necessary to fully understand and address conservation issues relating to human-wildlife interactions.
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