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25 results for “integrated species distribution model”
Data from: The 'golden kelp' Laminaria ochroleuca under global change: integrating multiple eco-physiological responses with species distribution models
1. The loss of marine foundation species, in particular kelps at temperate latitudes, has been linked to climatic drivers and co-occurring human perturbations. Ocean temperature and nutrients typically co-vary over local and regional scales and play a crucial role on kelp dynamics. Examining their independent and interactive effects on kelp physiological performance is essential to understand and predict patterns of kelp distribution, particularly under scenarios of global change. 2. Crossed combinations of ocean temperatures and availability of nutrients were experimentally tested on juveniles of the 'golden kelp', Laminaria ochroleuca, from the northwestern Iberian Peninsula. Eco-physiological responses included: survival, growth and total N content. Results were embedded into a Species Distribution Model (SDM), which relates presence records and climatic and non-climatic data to forecast distribution patterns of L. ochroleuca under different climate change scenarios. 3. Temperatures above 24.6 ºC were lethal irrespective of nutrients. Optimal growth of juvenile sporophytes occurred between 12 °C and 18 ºC and no nutrient limitation. The SDM, where ocean temperature was the main predictor of kelp distribution in line with temperature thresholds given by eco-physiological responses, suggests a future expansion towards northern latitudes and a retreat from the southern limit/boundary of the current distribution. 4. Synthesis. Range-shifting of the golden kelp can have severe ecological impacts at regional and local scales. The expansion or retraction of the species along the European coast seems to be modulated mainly by temperature, but nutrient availability would be key to maintain optimal physiological performance. Our work highlights that the combination of empirical and modelling approaches is accessible to researchers and crucial to build more robust predictions of ecological and biogeographic responses of habitat-forming species to forecasted environmental change.
Predictors for "Integrating High-Temporal-Resolution Climate Projections into Species Distribution Models"
<p>Predictors to train and predict SDMs</p>
Data from: The ‘golden kelp’ Laminaria ochroleuca under global change: integrating multiple eco-physiological responses with species distribution models
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Data from: Integrated species distribution models: combining presence-background data and site-occupany data with imperfect detection
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Data from: Integrating diverse data for robust species distribution models in a dynamic ocean
<p><strong>Aim: </strong>Species distribution models (SDMs) are an important tool for marine conservation and management, yet guidance on leveraging diverse data to build robust models is limited. While various approaches can be used to integrate different datasets, studies comparing their performance, particularly for highly migratory and mobile species, are scarce. Here, we assess whether a model-based integrative framework improves performance over traditional data pooling or ensemble approaches when synthesizing multiple data types.</p> <p><strong>Location: </strong>North Atlantic Ocean</p> <p><strong>Time Period: </strong>1993 - 2019</p> <p><strong>Major Taxa Studied: </strong>Blue shark (<em>Prionace glauca</em>)</p> <p><strong>Methods: </strong>We trained traditional, correlative SDMs and integrated SDMs (iSDMs) with three distinct data types: fishery-dependent marker tags, fishery observer records, and fishery-independent electronic tag data. We evaluated data pooling and ensemble approaches in a correlative SDM framework and compared performance to an iSDM approach designed to explicitly account for data-specific biases while retaining the strengths of each dataset.</p> <p><strong>Results: </strong>While each integration approach yielded robust models, model performance varied among data types, with all models predicting fishery-dependent data more accurately than fishery-independent data. Differences in performance were primarily attributed to each model's ability to explain the spatiotemporal dynamics of the training data. iSDMs that explicitly accounted for seasonal variability yielded the most accurate and ecologically realistic estimates. However, such approaches are computationally intensive and warrant identifying model purpose as an important step in the data-integration process.</p> <p><strong>Main Conclusions: </strong> Our findings reveal important trade-offs among the current techniques for integrating data in SDMs, including variability in accurately estimating species distributions, generating ecologically realistic predictions, and practical feasibility. With increasing access to growing and diverse data sources, maximizing our ability to leverage available data with robust analytical approaches will be instrumental in enhancing conservation and management efforts and for understanding current and future species distributions in a dynamic ocean.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.