Lauren

Gillespie

Anthropogenic habitat loss and climate change are reducing species' geographic ranges, increasing extinction risk and losses of species' genetic diversity. Although preserving genetic diversity is key to maintaining species' adaptability, we lack predictive tools and global estimates of genetic diversity loss across ecosystems. We introduce a mathematical framework that bridges biodiversity theory and population genetics to understand the loss of naturally occurring DNA mutations with decreasing habitat. By analyzing genomic variation of 10,095 georeferenced individuals from 20 plant and animal species, we show that genome-wide diversity follows a mutations-area relationship power law with geographic area, which can predict genetic diversity loss from local population extinctions. We estimate that more than 10% of genetic diversity may already be lost for many threatened and nonthreatened species, surpassing the United Nations' post-2020 targets for genetic preservation.

Anthropogenic habitat destruction and climate change are altering the geographic distributions of plant communities. Although mapping vegetation changes is now possible at high resolution using remote sensing data and deep convolutional neural networks, these approaches have not been applied to model the distributions of thousands of plant species to understand spatial changes in biodiversity. To address the current lack of scalable and automatic tools to map plant species distributions at a fine-grained scale, we created a dataset of over half a million citizen science observations of 2,221 plant species across California paired with satellite images at 1 meter resolution from solely free and public sources. With this we trained a deep convolutional neural network, deepbiosphere , that predicts presences of plant species within 256 × 256 meter satellite images and outperforms common low-resolution species distribution models . We showcase the novelty and potential applications of this framework by visualizing high-resolution predictions of keystone species such as coastal redwoods, identifying spatio-temporal ecosystem changes from wildfires and restoration management, and detecting urban biodiversity hotspots. Deep neural networks continuously trained on public remote sensing imagery and citizen science observations could enable cheap, automatic, and scalable monitoring of biodiversity and detect rapid anthropogenic impacts on ecosystems.

Anthropogenic habitat loss and climate change are reducing species' geographic ranges, increasing extinction risk and losses of species' genetic diversity. Although preserving genetic diversity is key to maintaining species' adaptability, we lack predictive tools and global estimates of genetic diversity loss across ecosystems. We introduce a mathematical framework that bridges biodiversity theory and population genetics to understand the loss of naturally occurring DNA mutations with decreasing habitat. By analyzing genomic variation of 10,095 georeferenced individuals from 20 plant and animal species, we show that genome-wide diversity follows a mutations-area relationship power law with geographic area, which can predict genetic diversity loss from local population extinctions. We estimate that more than 10% of genetic diversity may already be lost for many threatened and nonthreatened species, surpassing the United Nations' post-2020 targets for genetic preservation.