Dataset Information

Estimating Alpha, Beta, and Gamma Diversity Through Deep Learning.

ABSTRACT: The reliable mapping of species richness is a crucial step for the identification of areas of high conservation priority, alongside other value and threat considerations. This is commonly done by overlapping range maps of individual species, which requires dense availability of occurrence data or relies on assumptions about the presence of species in unsampled areas deemed suitable by environmental niche models. Here, we present a deep learning approach that directly estimates species richness, skipping the step of estimating individual species ranges. We train a neural network model based on species lists from inventory plots, which provide ground truth data for supervised machine learning. The model learns to predict species richness based on spatially associated variables, including climatic and geographic predictors, as well as counts of available species records from online databases. We assess the empirical utility of our approach by producing independently verifiable maps of alpha, beta, and gamma plant diversity at high spatial resolutions for Australia, a continent with highly heterogeneous diversity patterns. Our deep learning framework provides a powerful and flexible new approach for estimating biodiversity patterns, constituting a step forward toward automated biodiversity assessments.

SUBMITTER: Andermann T

PROVIDER: S-EPMC9062518 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Estimating Alpha, Beta, and Gamma Diversity Through Deep Learning.

Andermann Tobias T Antonelli Alexandre A Barrett Russell L RL Silvestro Daniele D

Frontiers in plant science 20220419

The reliable mapping of species richness is a crucial step for the identification of areas of high conservation priority, alongside other value and threat considerations. This is commonly done by overlapping range maps of individual species, which requires dense availability of occurrence data or relies on assumptions about the presence of species in unsampled areas deemed suitable by environmental niche models. Here, we present a deep learning approach that directly estimates species richness, ...[more]

PMID: 35519811

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Project description:BackgroundBiodiversity, crucial for understanding ecosystems, encompasses species richness, composition, and distribution. Ecological and environmental factors, such as habitat type, resource availability, and climate conditions, play pivotal roles in shaping species diversity within and among communities, categorized into alpha (within habitat), beta (between habitats), and gamma (total regional) diversity. Hummingbird communities are influenced by habitat, elevation, and seasonality, making them an ideal system for studying these diversities, shedding light on mutualistic community dynamics and conservation strategies.MethodsOver a year-long period, monthly surveys were conducted to record hummingbird species and their visited flowering plants across four habitat types (oak forest, juniper forest, pine forest, and xerophytic shrubland) in Tlaxcala, Mexico. Three locations per habitat type were selected based on conservation status and distance from urban areas. True diversity measures were used to assess alpha, beta, and gamma diversity of hummingbirds and their floral resources. Environmental factors such as altitude and bioclimatic variables were explored for their influence on beta diversity.ResultsFor flowering plants, gamma diversity encompassed 34 species, with oak forests exhibiting the highest richness, while xerophytic shrublands had the highest alpha diversity. In contrast, for hummingbirds, 11 species comprised the gamma diversity, with xerophytic shrublands having the highest richness and alpha diversity. Our data reveal high heterogeneity in species abundance among habitats. Notably, certain floral resources like Loeselia mexicana and Bouvardia ternifolia emerge as key species in multiple habitats, while hummingbirds such as Basilinna leucotis, Selasphorus platycercus, and Calothorax lucifer exhibit varying levels of abundance and habitat preferences. Beta diversity analyses unveil habitat-specific patterns, with species turnover predominantly driving dissimilarity in composition. Moreover, our study explores the relationships between these diversity components and environmental factors such as altitude and climate variables. Climate variables, in particular, emerge as significant contributors to dissimilarity in floral resource and hummingbird communities, highlighting the influence of environmental conditions on species distribution.ConclusionsOur results shed light on the complex dynamics of hummingbird-flower mutualistic communities within diverse habitats and underscore the importance of understanding how habitat-driven shifts impact alpha, beta, and gamma diversity. Such insights are crucial for conservation strategies aimed at preserving the delicate ecological relationships that underpin biodiversity in these communities.

Dataset Information

Estimating Alpha, Beta, and Gamma Diversity Through Deep Learning.

Publications

Estimating Alpha, Beta, and Gamma Diversity Through Deep Learning.

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