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10 results for “coupled human and natural systems”
MCR LTER: Coral Reef: Coupled Natural-Human Systems: Survey of fish being sold on the roadside 2020-2021
This dataset includes the results of a survey on fish sold by the roadside in Moorea, French Polynesia. During 2020-2022, more than 7000 fish were identified and sized from photographs taken during the market surveys. These data were collected as part of CNH-L: Multiscale Dynamics of Coral Reef Fisheries: Feedbacks Between Fishing Practices, Livelihood Strategies, and Shifting Dominance of Coral and Algae (BCS-1714704) with additional support from the Moorea Coral Reef LTER (OCE- 1637396). This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 22-24354 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2023). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
MCR LTER: Coral Reef: Coupled Natural-Human Systems: GPS-enabled survey of fish in Moorea’s lagoons
This dataset includes counts and sizes of fish within Moorea’s lagoons, with more than 130,000 fish recorded between 2018 and 2021. In summer of each year observers swim through Moorea’s lagoons, observing a 5-m wide swath and recording species and sizes of fish encountered in every minute of swim (about 10 linear meters of sampling). The fish counts are limited to a fixed species list (57 taxa) which was chosen to capture most of the reef associated species targeted in Moorea’s fishery, as well as important herbivores. Only fish larger than 10 cm were recorded, a size chosen to match the small end of fish observed in the catch. The resulting counts were georeferenced based on simultaneously collecting GPS data. Data are presented here on the scale of a minute of sampling. This scale coincides with benthic data which was simultaneously collected but is presented in a different data set (EDI data package: knb-lter-mcr.4014.1) doi:10.6073/pasta/f1e4cbcd79ebbbb0b0cbb79ab2ff8901 These data were collected as part of CNH-L: Multiscale Dynamics of Coral Reef Fisheries: Feedbacks Between Fishing Practices, Livelihood Strategies, and Shifting Dominance of Coral and Algae (BCS-1714704) with additional support from the Moorea Coral Reef LTER (OCE- 1637396).
MCR LTER: Coral Reef: Coupled Natural-Human Systems: GPS Benthic Data
This dataset includes cover of benthic organisms and substrates in more than 600,000 photographic samples of Moorea’s lagoon habitats, collected between 2018 and 2021. In summer of each year swimmers towed downward facing cameras, taking 1.5 photos per second of habitats they passed over. The resulting pictures were processed through a computer vision algorithm (CoralNet), resulting in percent cover estimates in each photo of organisms such as coral and algae and substrates such as sand and rubble. These data were georeferenced based on simultaneously collecting GPS data. Data are presented here on the scale of the photograph, and are also aggregated to the scale of a minute of sampling and a transect of swimming. The latter scales are chosen to coincide with fish counting data which was simultaneously collected but is presented in a different data set (EDI data package ID: knb-lter-mcr.4013.1) doi:10.6073/pasta/ff3de88334edda65fef26de45ff26c1d These data were collected as part of CNH-L: Multiscale Dynamics of Coral Reef Fisheries: Feedbacks Between Fishing Practices, Livelihood Strategies, and Shifting Dominance of Coral and Algae (BCS-1714704) with additional support from the Moorea Coral Reef LTER (OCE- 1637396).
Code: The effects of model complexity on model output uncertainty in co-evolved coupled natural–human systems
<p>This is the code archive for the publication "The effects of model complexity on model output uncertainty in co-evolved coupled natural–human systems" in Earth's Future.</p> <p>Abstract:</p> <p>Studies have recently focused on using coupled natural–human systems (CNHS) to inform policymaking. However, model uncertainty can increase with model complexity and affect the variance of the model outcomes. Therefore, this study explores an uncertainty analysis of coupled hydrological and human decision models to better evaluate CNHS modeling properties. Five coupled models are proposed with different model complexities for human behavior settings (i.e., model structure and the number of calibrated parameters): one static, two adaptive, and two learning adaptive. Learning adaptive models (the most complex) have both a learning component (capturing long-term trends) and an adaptive component (capturing short-term variations), while adaptive models omit the learning component. The static model is the simplest, without learning or adaptive components. Applying the law of total variance, the model output uncertainty is decomposed into three sources: (1) climate change scenario uncertainty, (2) climate internal variability, and (3) different model configurations with parameter sets or model structures that are equally capable of producing similar outcomes. Our exploratory analysis demonstrated that model uncertainty would likely increase with model complexity given uncertain input data (e.g., climate forcing) and different model configurations; the inclusion of a learning mechanism in the human system can potentially offset the impact of the natural system on uncertainty through coupling natural and human systems. We also discuss other uncertainty sources, such as assumptions about model structure due to incomplete knowledge and metrics for calibration target selection for future studies.</p>
Assessing impacts of social-ecological diversity on resilience in a wetland coupled human and natural system: Data release
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Data from: Missing the people for the trees: identifying coupled natural-human system feedbacks driving the ecology of Lyme disease
1. Infectious diseases are rapidly emerging and many are increasing in incidence across the globe. Processes of land-use change, notably habitat loss and fragmentation, have been widely implicated in emergence and spread of zoonoses such as Lyme disease, yet evidence remains equivocal. 2. Here we discuss and apply an innovative approach from the social sciences, instrumental variables, that seeks to tease out causality from observational data. Using this approach, we revisit the effect of forest fragmentation on Lyme disease incidence, focusing on human interaction with fragmented landscapes. Though human interaction with infected ticks is of clear and fundamental importance to human disease incidence, human activities that influence exposure have been nearly universally overlooked in the ecology literature. 3. Using county-level land-use and Lyme disease incidence data for ~800 counties from the northeastern United States over the span of a decade, we illustrate (1) human interaction with fragmented forest landscapes reliably predicts Lyme disease incidence, while ecological measures of forest fragmentation alone are unreliable predictors and (2) that identifying the effect of forest fragmentation on human disease requires addressing the feedback between Lyme disease risk and human decisions to avoid interaction with high-risk landscapes. 4. Synthesis and applications. The innovative approach and novel results help to clarify the equivocal literature on forest fragmentation and Lyme disease, and illustrate the key role that human behavior may be playing in the ecology of Lyme disease in North America. Accounting for human activity and behavior in the ecology of disease more broadly may result in improved understanding of both the ecological drivers of disease, as well as actionable intervention strategies to reduce disease burden in a changing world. For example, our model results have practical implications for land-use policy aimed at disease reduction. Our model suggests land use regulations that reduce parcel size would be an actionable approach for policy makers concerned about increasing Lyme disease incidence in the northeastern US.10-Oct-2018
Data Associated with Manuscript Titled "Development of a graphical resilience framework to understand a coupled human-natural system in a remote arid highland of Baja California Sur"
<p>This is a dataset in support of analyses of water chemistry and social networks described and interpreted in the associated manuscript titled "Development of a graphical resilience framework to understand a coupled human-natural system in a remote arid highland of Baja California Sur"</p>
Data from: Determinants of elephant foraging behavior in a coupled human-natural system: is brown the new green?
1. Crop raiding by wildlife poses major threats to both wildlife conservation and human wellbeing in agro-ecosystems worldwide. These threats are particularly acute in many parts of Africa, where crop raiders include globally threatened megafauna such as elephants, and where smallholder agriculture is a primary source of human livelihood. One framework for understanding herbivore feeding behavior, the forage-maturation hypothesis, predicts that herbivores should align their movements with intermediate forage biomass (i.e., peak green-up); this phenomenon is known as "surfing the green wave." Crop-raiding elephants, however, often consume not just foliage, but also fruits and tubers (e.g., maize, potatoes), which generally mature after seasonal peaks in photosynthetic activity. Thus, although elephants have been reported to surf the green wave in natural habitats, they may utilize a different strategy in cultivated landscapes by selecting crops that are "browning down." 2. We sought to understand the factors that underpin movement of elephants into agricultural landscapes. 3. In Mozambique's Gorongosa National Park, we used movement data from GPS-collared elephants, together with precipitation records, remotely sensed estimates of landscape greenness (NDVI), DNA-based diet analysis, measurements of plant nutritional quality, and survey-based metrics of crop availability to understand spatiotemporal variation in elephant crop-raiding behavior. 4. Elephants tracked peak NDVI while foraging inside the Park. During the dry season, however, when NDVI within the Park declined and availability of mature crops was high, crop-raiding increased dramatically, and elephants consistently selected crop plants that were browning down while foraging in cultivated landscapes. Crops contained significantly higher digestible energy than wild food plants, but comparable (and sometimes lower) levels of digestible protein, suggesting that this foraging strategy maximized energy rather than protein intake. 5. Our study is the first to combine GPS tracking data with high-resolution diet analysis and community-based reporting of crop availability to reveal fine-scale plasticity in foraging behavior of elephants at the human-wildlife interface. Our results extend the forage-maturation hypothesis by showing that elephants surf waves of plant brown-down in cultivated landscapes. These findings can aid efforts to reduce human-elephant conflict by enabling wildlife managers to prioritize mitigation actions in time and space with limited resources.
Data from: Determinants of elephant foraging behavior in a coupled human-natural system: is brown the new green?
Open the record for dataset details and reuse information.
Data from: Missing the people for the trees: identifying coupled natural-human system feedbacks driving the ecology of Lyme disease
Open the record for dataset details and reuse information.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.