Modelling Species Distributions with Deep Learning to Predict Plant
Extinction Risk and Assess Climate Change Impacts
- URL: http://arxiv.org/abs/2401.05470v1
- Date: Wed, 10 Jan 2024 15:24:27 GMT
- Title: Modelling Species Distributions with Deep Learning to Predict Plant
Extinction Risk and Assess Climate Change Impacts
- Authors: Joaquim Estopinan, Pierre Bonnet, Maximilien Servajean, Fran\c{c}ois
Munoz, Alexis Joly
- Abstract summary: We evaluate a novel method for classifying the IUCN status of species.
Our method matches state-of-the-art classification performance while relying on flexible SDM-based features.
The proportion of threatened species is increasing globally, with critical rates in Africa, Asia and South America.
- Score: 2.874893537471256
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The post-2020 global biodiversity framework needs ambitious, research-based
targets. Estimating the accelerated extinction risk due to climate change is
critical. The International Union for Conservation of Nature (IUCN) measures
the extinction risk of species. Automatic methods have been developed to
provide information on the IUCN status of under-assessed taxa. However, these
compensatory methods are based on current species characteristics, mainly
geographical, which precludes their use in future projections. Here, we
evaluate a novel method for classifying the IUCN status of species benefiting
from the generalisation power of species distribution models based on deep
learning. Our method matches state-of-the-art classification performance while
relying on flexible SDM-based features that capture species' environmental
preferences. Cross-validation yields average accuracies of 0.61 for status
classification and 0.78 for binary classification. Climate change will reshape
future species distributions. Under the species-environment equilibrium
hypothesis, SDM projections approximate plausible future outcomes. Two extremes
of species dispersal capacity are considered: unlimited or null. The projected
species distributions are translated into features feeding our IUCN
classification method. Finally, trends in threatened species are analysed over
time and i) by continent and as a function of average ii) latitude or iii)
altitude. The proportion of threatened species is increasing globally, with
critical rates in Africa, Asia and South America. Furthermore, the proportion
of threatened species is predicted to peak around the two Tropics, at the
Equator, in the lowlands and at altitudes of 800-1,500 m.
Related papers
- AI-based Mapping of the Conservation Status of Orchid Assemblages at
Global Scale [2.874893537471256]
We introduce a new Deep Species Distribution Model trained on 1M occurrences of 14K orchid species to predict their assemblages at global scale and at kilometre resolution.
We show and analyze the variation of these indicators at World scale and in relation to currently protected areas in Sumatra island.
The highest level of threat is found at Madagascar and the neighbouring islands.
arXiv Detail & Related papers (2024-01-09T17:38:19Z) - LD-SDM: Language-Driven Hierarchical Species Distribution Modeling [9.620416509546471]
We focus on the problem of species distribution modeling using global-scale presence-only data.
To capture a stronger implicit relationship between species, we encode the taxonomic hierarchy of species using a large language model.
We propose a novel proximity-aware evaluation metric that enables evaluating species distribution models.
arXiv Detail & Related papers (2023-12-13T18:11:37Z) - SatBird: Bird Species Distribution Modeling with Remote Sensing and
Citizen Science Data [68.2366021016172]
We present SatBird, a satellite dataset of locations in the USA with labels derived from presence-absence observation data from the citizen science database eBird.
We also provide a dataset in Kenya representing low-data regimes.
We benchmark a set of baselines on our dataset, including SOTA models for remote sensing tasks.
arXiv Detail & Related papers (2023-11-02T02:00:27Z) - Spatial Implicit Neural Representations for Global-Scale Species Mapping [72.92028508757281]
Given a set of locations where a species has been observed, the goal is to build a model to predict whether the species is present or absent at any location.
Traditional methods struggle to take advantage of emerging large-scale crowdsourced datasets.
We use Spatial Implicit Neural Representations (SINRs) to jointly estimate the geographical range of 47k species simultaneously.
arXiv Detail & Related papers (2023-06-05T03:36:01Z) - Neuroevolution-based Classifiers for Deforestation Detection in Tropical
Forests [62.997667081978825]
Millions of hectares of tropical forests are lost every year due to deforestation or degradation.
Monitoring and deforestation detection programs are in use, in addition to public policies for the prevention and punishment of criminals.
This paper proposes the use of pattern classifiers based on neuroevolution technique (NEAT) in tropical forest deforestation detection tasks.
arXiv Detail & Related papers (2022-08-23T16:04:12Z) - Inter and Intra-Annual Spatio-Temporal Variability of Habitat
Suitability for Asian Elephants in India: A Random Forest Model-based
Analysis [1.370633147306388]
We develop a Random Forest model to estimate the species distribution of Asian elephants in India.
We observe that seasonal reduction in the suitable habitat may explain the patterns of Asian elephants and the increasing human-elephant conflict.
arXiv Detail & Related papers (2021-07-22T06:42:54Z) - Ensemble Forecasting of the Zika Space-TimeSpread with Topological Data
Analysis [13.838100337224075]
Zika virus is primarily transmitted through bites of infected mosquitoes of the species Aedes aegypti and Aedes albopictus.
The abundance of mosquitoes and mosquitoes, as a result, the prevalence of Zika virus infections are common in areas which have high precipitation, high temperature, and high population density.
We introduce new concept of cumulative Betti numbers and then integrate the cumulative Betti numbers as topological descriptors into three machine learning models.
arXiv Detail & Related papers (2020-09-24T16:42:19Z) - Solving Long-tailed Recognition with Deep Realistic Taxonomic Classifier [68.38233199030908]
Long-tail recognition tackles the natural non-uniformly distributed data in realworld scenarios.
While moderns perform well on populated classes, its performance degrades significantly on tail classes.
Deep-RTC is proposed as a new solution to the long-tail problem, combining realism with hierarchical predictions.
arXiv Detail & Related papers (2020-07-20T05:57:42Z) - Two-View Fine-grained Classification of Plant Species [66.75915278733197]
We propose a novel method based on a two-view leaf image representation and a hierarchical classification strategy for fine-grained recognition of plant species.
A deep metric based on Siamese convolutional neural networks is used to reduce the dependence on a large number of training samples and make the method scalable to new plant species.
arXiv Detail & Related papers (2020-05-18T21:57:47Z) - Survival Cluster Analysis [93.50540270973927]
There is an unmet need in survival analysis for identifying subpopulations with distinct risk profiles.
An approach that addresses this need is likely to improve characterization of individual outcomes.
arXiv Detail & Related papers (2020-02-29T22:41:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.