993 resultados para top predator


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Global biodiversity is eroding at an alarming rate, through a combination of anthropogenic disturbance and environmental change. Ecological communities are bewildering in their complexity. Experimental ecologists strive to understand the mechanisms that drive the stability and structure of these complex communities in a bid to inform nature conservation and management. Two fields of research have had high profile success at developing theories related to these stabilising structures and testing them through controlled experimentation. Biodiversity-ecosystem functioning (BEF) research has explored the likely consequences of biodiversity loss on the functioning of natural systems and the provision of important ecosystem services. Empirical tests of BEF theory often consist of simplified laboratory and field experiments, carried out on subsets of ecological communities. Such experiments often overlook key information relating to patterns of interactions, important relationships, and fundamental ecosystem properties. The study of multi-species predator-prey interactions has also contributed much to our understanding of how complex systems are structured, particularly through the importance of indirect effects and predator suppression of prey populations. A growing number of studies describe these complex interactions in detailed food webs, which encompass all the interactions in a community. This has led to recent calls for an integration of BEF research with the comprehensive study of food web properties and patterns, to help elucidate the mechanisms that allow complex communities to persist in nature. This thesis adopts such an approach, through experimentation at Lough Hyne marine reserve, in southwest Ireland. Complex communities were allowed to develop naturally in exclusion cages, with only the diversity of top trophic levels controlled. Species removals were carried out and the resulting changes to predator-prey interactions, ecosystem functioning, food web properties, and stability were studied in detail. The findings of these experiments contribute greatly to our understanding of the stability and structure of complex natural communities.

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Semiconductor nanowires are pseudo 1-D structures where the magnitude of the semiconducting material is confined to a length of less than 100 nm in two dimensions. Semiconductor nanowires have a vast range of potential applications, including electronic (logic devices, diodes), photonic (laser, photodetector), biological (sensors, drug delivery), energy (batteries, solar cells, thermoelectric generators), and magnetic (spintronic, memory) devices. Semiconductor nanowires can be fabricated by a range of methods which can be categorised into one of two paradigms, bottom-up or top-down. Bottom-up processes can be defined as those where structures are assembled from their sub-components in an additive fashion. Top-down fabrication strategies use sculpting or etching to carve structures from a larger piece of material in a subtractive fashion. This seminar will detail a number of novel routes to fabricate semiconductor nanowires by both bottom-up and top-down paradigms. Firstly, a novel bottom-up route to fabricate Ge nanowires with controlled diameter distributions in the sub-20 nm regime will be described. This route details nanowire synthesis and diameter control in the absence of a foreign seed metal catalyst. Additionally a top-down route to nanowire array fabrication will be detailed outlining the importance of surface chemistry in high-resolution electron beam lithography (EBL) using hydrogen silsesquioxane (HSQ) on Ge and Bi2Se3 surfaces. Finally, a process will be described for the directed self-assembly of a diblock copolymer (PS-b-PDMS) using an EBL defined template. This section will also detail a route toward selective template sidewall wetting of either block in the PS-b-PDMS system, through tailored functionalisation of the template and substrate surfaces.

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Intraspecific phenotypic variation in ecologically important traits is widespread and important for evolutionary processes, but its effects on community and ecosystem processes are poorly understood. We use life history differences among populations of alewives, Alosa pseudoharengus, to test the effects of intraspecific phenotypic variation in a predator on pelagic zooplankton community structure and the strength of cascading trophic interactions. We focus on the effects of differences in (1) the duration of residence in fresh water (either seasonal or year-round) and (2) differences in foraging morphology, both of which may strongly influence interactions between alewives and their prey. We measured zooplankton community structure, algal biomass, and spring total phosphorus in lakes that contained landlocked, anadromous, or no alewives. Both the duration of residence and the intraspecific variation in foraging morphology strongly influenced zooplankton community structure. Lakes with landlocked alewives had small-bodied zooplankton year-round, and lakes with no alewives had large-bodied zooplankton year-round. In contrast, zooplankton communities in lakes with anadromous alewives cycled between large-bodied zooplankton in the winter and spring and small-bodied zooplankton in the summer. In summer, differences in feeding morphology of alewives caused zooplankton biomass to be lower and body size to be smaller in lakes with anadromous alewives than in lakes with landlocked alewives. Furthermore, intraspecific variation altered the strength of the trophic cascade caused by alewives. Our results demonstrate that intraspecific phenotypic variation of predators can regulate community structure and ecosystem processes by modifying the form and strength of complex trophic interactions.

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The MHD wave instability in commercial cells for electrolytic aluminium production is often described using ‘shallow water’ models. The model [1] is extended for a variable height cathode bottom and anode top to account for realistic cell features. The variable depth of the two fluid layers affects the horizontal current density, the wave development and the stability threshold. Instructive examples for the 500 kA cell are presented.

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1. Abundant mid-trophic pelagic fish often play a central role in marine ecosystems, both as links between zooplankton and top predators and as important fishery targets. In the North Sea, the lesser sandeel occupies this position, being the main prey of many bird, mammal and fish predators and the target of a major industrial fishery. However, since 2003, sandeel landings have decreased by > 50%, and many sandeel-dependent seabirds experienced breeding failures in 2004. 2. Despite the major economic implications, current understanding of the regulation of key constituents of this ecosystem is poor. Sandeel abundance may be regulated 'bottom-up' by food abundance, often thought to be under climatic control, or 'top-down' by natural or fishery predation. We tested predictions from these two hypotheses by combining unique long-term data sets (1973–2003) on seabird breeding productivity from the Isle of May, SE Scotland, and plankton and fish larvae from the Continuous Plankton Recorder survey. We also tested whether seabird breeding productivity was more tightly linked to sandeel biomass or quality (size) of individual fish. 3. The biomass of larval sandeels increased two- to threefold over the study period and was positively associated with proxies of the abundance of their plankton prey. Breeding productivity of four seabirds bringing multiple prey items to their offspring was positively related to sandeel larval biomass with a 1-year lag, indicating dependence on 1-year-old fish, but in one species bringing individual fish it was strongly associated with the size of adult sandeels. 4. These links are consistent with bottom-up ecosystem regulation and, with evidence from previous studies, indicate how climate-driven changes in plankton communities can affect top predators and potentially human fisheries through the dynamics of key mid-trophic fish. However, the failing recruitment to adult sandeel stocks and the exceptionally low seabird breeding productivity in 2004 were not associated with low sandeel larval biomass in 2003, so other mechanisms (e.g. predation, lack of suitable food after metamorphosis) must have been important in this case. Understanding ecosystem regulation is extremely important for predicting the fate of keystone species, such as sandeels, and their predators.

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Exploring climate and anthropogenic impacts on marine ecosystems requires an understanding of how trophic components interact. However, integrative end-to-end ecosystem studies (experimental and/or modelling) are rare. Experimental investigations often concentrate on a particular group or individual species within a trophic level, while tropho-dynamic field studies typically employ either a bottom-up approach concentrating on the phytoplankton community or a top-down approach concentrating on the fish community. Likewise the emphasis within modelling studies is usually placed upon phytoplankton-dominated biogeochemistry or on aspects of fisheries regulation. In consequence the roles of zooplankton communities (protists and metazoans) linking phytoplankton and fish communities are typically under-represented if not (especially in fisheries models) ignored. Where represented in ecosystem models, zooplankton are usually incorporated in an extremely simplistic fashion, using empirical descriptions merging various interacting physiological functions governing zooplankton growth and development, and thence ignoring physiological feedback mechanisms. Here we demonstrate, within a modelled plankton food-web system, how trophic dynamics are sensitive to small changes in parameter values describing zooplankton vital rates and thus the importance of using appropriate zooplankton descriptors. Through a comprehensive review, we reveal the mismatch between empirical understanding and modelling activities identifying important issues that warrant further experimental and modelling investigation. These include: food selectivity, kinetics of prey consumption and interactions with assimilation and growth, form of voided material, mortality rates at different age-stages relative to prior nutrient history. In particular there is a need for dynamic data series in which predator and prey of known nutrient history are studied interacting under varied pH and temperature regimes.

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Understanding long‐term, ecosystem‐level impacts of climate change is challenging because experimental research frequently focuses on short‐term, individual‐level impacts in isolation. We address this shortcoming first through an interdisciplinary ensemble of novel experimental techniques to investigate the impacts of 14‐month exposure to ocean acidification and warming (OAW) on the physiology, activity, predatory behaviour and susceptibility to predation of an important marine gastropod (Nucella lapillus). We simultaneously estimated the potential impacts of these global drivers on N. lapillus population dynamics and dispersal parameters. We then used these data to parameterize a dynamic bioclimatic envelope model, to investigate the consequences of OAW on the distribution of the species in the wider NE Atlantic region by 2100. The model accounts also for changes in the distribution of resources, suitable habitat and environment simulated by finely resolved biogeochemical models, under three IPCC global emissions scenarios. The experiments showed that temperature had the greatest impact on individual‐level responses, while acidification had a similarly important role in the mediation of predatory behaviour and susceptibility to predators. Changes in Nucella predatory behaviour appeared to serve as a strategy to mitigate individual‐level impacts of acidification, but the development of this response may be limited in the presence of predators. The model projected significant large‐scale changes in the distribution of Nucella by the year 2100 that were exacerbated by rising greenhouse gas emissions. These changes were spatially heterogeneous, as the degree of impact of OAW on the combination of responses considered by the model varied depending on local‐environmental conditions and resource availability. Such changes in macro‐scale distributions cannot be predicted by investigating individual‐level impacts in isolation, or by considering climate stressors separately. Scaling up the results of experimental climate change research requires approaches that account for long‐term, multiscale responses to multiple stressors, in an ecosystem context.

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The decisions animals make about how long to wait between activities can determine the success of diverse behaviours such as foraging, group formation or risk avoidance. Remarkably, for diverse animal species, including humans, spontaneous patterns of waiting times show random ‘burstiness’ that appears scale-invariant across a broad set of scales. However, a general theory linking this phenomenon across the animal kingdom currently lacks an ecological basis. Here, we demonstrate from tracking the activities of 15 sympatric predator species (cephalopods, sharks, skates and teleosts) under natural and controlled conditions that bursty waiting times are an intrinsic spontaneous behaviour well approximated by heavy-tailed (power-law) models over data ranges up to four orders of magnitude. Scaling exponents quantifying ratios of frequent short to rare very long waits are species-specific, being determined by traits such as foraging mode (active versus ambush predation), body size and prey preference. A stochastic–deterministic decision model reproduced the empirical waiting time scaling and species-specific exponents, indicating that apparently complex scaling can emerge from simple decisions. Results indicate temporal power-law scaling is a behavioural ‘rule of thumb’ that is tuned to species’ ecological traits, implying a common pattern may have naturally evolved that optimizes move–wait decisions in less predictable natural environments.

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Aim: Ecological niche modelling can provide valuable insight into species' environmental preferences and aid the identification of key habitats for populations of conservation concern. Here, we integrate biologging, satellite remote-sensing and ensemble ecological niche models (EENMs) to identify predictable foraging habitats for a globally important population of the grey-headed albatross (GHA) Thalassarche chrysostoma. Location: Bird Island, South Georgia; Southern Atlantic Ocean. Methods: GPS and geolocation-immersion loggers were used to track at-sea movements and activity patterns of GHA over two breeding seasons (n = 55; brood-guard). Immersion frequency (landings per 10-min interval) was used to define foraging events. EENM combining Generalized Additive Models (GAM), MaxEnt, Random Forest (RF) and Boosted Regression Trees (BRT) identified the biophysical conditions characterizing the locations of foraging events, using time-matched oceanographic predictors (Sea Surface Temperature, SST; chlorophyll a, chl-a; thermal front frequency, TFreq; depth). Model performance was assessed through iterative cross-validation and extrapolative performance through cross-validation among years. Results: Predictable foraging habitats identified by EENM spanned neritic (<500 m), shelf break and oceanic waters, coinciding with a set of persistent biophysical conditions characterized by particular thermal ranges (3–8 °C, 12–13 °C), elevated primary productivity (chl-a > 0.5 mg m−3) and frequent manifestation of mesoscale thermal fronts. Our results confirm previous indications that GHA exploit enhanced foraging opportunities associated with frontal systems and objectively identify the APFZ as a region of high foraging habitat suitability. Moreover, at the spatial and temporal scales investigated here, the performance of multi-model ensembles was superior to that of single-algorithm models, and cross-validation among years indicated reasonable extrapolative performance. Main conclusions: EENM techniques are useful for integrating the predictions of several single-algorithm models, reducing potential bias and increasing confidence in predictions. Our analysis highlights the value of EENM for use with movement data in identifying at-sea habitats of wide-ranging marine predators, with clear implications for conservation and management.

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Aim: Ecological niche modelling can provide valuable insight into species' environmental preferences and aid the identification of key habitats for populations of conservation concern. Here, we integrate biologging, satellite remote-sensing and ensemble ecological niche models (EENMs) to identify predictable foraging habitats for a globally important population of the grey-headed albatross (GHA) Thalassarche chrysostoma. Location: Bird Island, South Georgia; Southern Atlantic Ocean. Methods: GPS and geolocation-immersion loggers were used to track at-sea movements and activity patterns of GHA over two breeding seasons (n = 55; brood-guard). Immersion frequency (landings per 10-min interval) was used to define foraging events. EENM combining Generalized Additive Models (GAM), MaxEnt, Random Forest (RF) and Boosted Regression Trees (BRT) identified the biophysical conditions characterizing the locations of foraging events, using time-matched oceanographic predictors (Sea Surface Temperature, SST; chlorophyll a, chl-a; thermal front frequency, TFreq; depth). Model performance was assessed through iterative cross-validation and extrapolative performance through cross-validation among years. Results: Predictable foraging habitats identified by EENM spanned neritic (<500 m), shelf break and oceanic waters, coinciding with a set of persistent biophysical conditions characterized by particular thermal ranges (3–8 °C, 12–13 °C), elevated primary productivity (chl-a > 0.5 mg m−3) and frequent manifestation of mesoscale thermal fronts. Our results confirm previous indications that GHA exploit enhanced foraging opportunities associated with frontal systems and objectively identify the APFZ as a region of high foraging habitat suitability. Moreover, at the spatial and temporal scales investigated here, the performance of multi-model ensembles was superior to that of single-algorithm models, and cross-validation among years indicated reasonable extrapolative performance. Main conclusions: EENM techniques are useful for integrating the predictions of several single-algorithm models, reducing potential bias and increasing confidence in predictions. Our analysis highlights the value of EENM for use with movement data in identifying at-sea habitats of wide-ranging marine predators, with clear implications for conservation and management.

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