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Diversity
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The Applications of Artificial Intelligence on the Conservation of Biodiversity
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The Applications of Artificial Intelligence on the Conservation of Biodiversity

A special issue of Diversity (ISSN 1424-2818). This special issue belongs to the section "Biodiversity Conservation".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 2294

Special Issue Editors

Dr. Xinhai Li

E-Mail Website
Guest Editor
Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
Interests: statistics; wildlife survey; conservation planning; animal movement; species distribution modelling; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Guiming Wang

E-Mail Website
Guest Editor
Department of Wildlife, Fisheries and Aquaculture, Mississippi State University, Mississippi, MS 39762, USA
Interests: avian migration; AI and machine learning; spatiotemporal dynamics in ecology; wildlife population ecology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biodiversity loss is one of the greatest environmental crises and challenges since the Anthropocene epoch. Globally joint conservation efforts are needed to revert the ongoing trend of biodiversity loss. Science-based decisions of global or regional biodiversity conservations have become more data driven with an increasing number of applications of innovative digitalized data acquisitions. Furthermore, Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized and automated important inferences from such data to facilitate timely decision-making processes with rapid and even “online” inferences. Such AI-assisted timely inferences are critically important for the conservations of biodiversity under the rapidly changing environments. Successful examples of applications of AI and ML in biodiversity conservation include, but are not limited to, identification of biodiversity hotspots and high-risk areas, monitoring of changes in biodiversity and organism abundances across space and time, detection of poverty regions, and reconstruction or prediction of wildlife movement trajectories using computer vision, deep learning, natural language processing, and robots. Machine Learning and AI have become popular tools for conservation biologists, ecologists, and natural resources managers. However, ML and AI by and large use black box approaches to data inferences. The current generation of AI technologies heavily rely on large amounts of training data, which are rarely structured following ecological processes. Therefore, we call for contributions to this Special Issue in two general categories: (1) innovative ideas of incorporating AI into the conceptual framework and theories of biodiversity conservation biology and ecology; and (2) case studies of innovative applications of ML, natural language processing, deep learning, and robotics in the monitoring and decision making of the conservation of genetic diversity, species diversity, and ecosystem diversity. We hope that this Special Issue provides a venue for applied data scientists, conservation biologists, and natural resource managers to work together to develop AI and ML technologies that improve the mechanistic understanding of mechanisms and processes underlying biodiversity losses and developing the optimal strategies for biodiversity conservations and sustainability of natural resources. Thank you in advance for your contributions to this Special Issue!

Dr. Xinhai Li
Prof. Dr. Guiming Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diversity is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • natural language processing
  • natural resource sustainability
  • conservation of biodiversity
  • biodiversity

Published Papers (2 papers)

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Research

19 pages, 5356 KiB  
Article
Application of Machine Learning in Ecological Red Line Identification: A Case Study of Chengdu–Chongqing Urban Agglomeration
by Juan Deng, Yu Xie, Ruilong Wei, Chengming Ye and Huajun Wang
Diversity 2024, 16(5), 300; https://0-doi-org.brum.beds.ac.uk/10.3390/d16050300 - 16 May 2024
Viewed by 382
Abstract
China’s Ecological Protection Red Lines (ERLs) policy has proven effective in constructing regional ecological security patterns and protecting ecological space. However, the existing methods for the identification of high conservation value areas (HCVAs) usually use physical models, whose parameters and processes are complex [...] Read more.
China’s Ecological Protection Red Lines (ERLs) policy has proven effective in constructing regional ecological security patterns and protecting ecological space. However, the existing methods for the identification of high conservation value areas (HCVAs) usually use physical models, whose parameters and processes are complex and only for a single service, affecting the ERL delineation. In this study, the data-driven machine learning (ML) models were innovatively applied to construct a framework for ERL identification. First, the One-Class Support Vector Machine (OC-SVM) was used to generate negative samples from natural reserves and ecological factors. Second, the supervised ML models were applied to predict the HCVAs by using samples. Third, by applying the same ecological factors, the traditional physical models were used to assess the ecological services of the study area for reference and comparison. Take Chengdu–Chongqing Urban Agglomeration (CY) as a case study, wherein data from 11 factors and 1822 nature reserve samples were prepared for feasibility verification of the proposed framework. The results showed that the area under the receiver operating characteristic curve (AUC) of all ML models was more than 97%, and random forest (RF) achieved the best performance at 99.57%. Furthermore, the land cover had great contributions to the HCVAs prediction, which is consistent with the land use pattern of CY. High-value areas are distributed in the surrounding mountains of CY, with lush vegetation. All of the above results indicated that the proposed framework can accurately identify HCVAs, and that it is more suitable and simpler than the traditional physical model. It can help improve the effectiveness of ERL delimitation and promote the implementation of ERL policies. Full article
(This article belongs to the Special Issue The Applications of Artificial Intelligence on the Conservation of Biodiversity)
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18 pages, 10035 KiB  
Article
Improved Wildlife Recognition through Fusing Camera Trap Images and Temporal Metadata
by Lei Liu, Chao Mou and Fu Xu
Diversity 2024, 16(3), 139; https://0-doi-org.brum.beds.ac.uk/10.3390/d16030139 - 23 Feb 2024
Cited by 1 | Viewed by 1188
Abstract
Camera traps play an important role in biodiversity monitoring. An increasing number of studies have been conducted to automatically recognize wildlife in camera trap images through deep learning. However, wildlife recognition by camera trap images alone is often limited by the size and [...] Read more.
Camera traps play an important role in biodiversity monitoring. An increasing number of studies have been conducted to automatically recognize wildlife in camera trap images through deep learning. However, wildlife recognition by camera trap images alone is often limited by the size and quality of the dataset. To address the above issues, we propose the Temporal-SE-ResNet50 network, which aims to improve wildlife recognition accuracy by exploiting the temporal information attached to camera trap images. First, we constructed the SE-ResNet50 network to extract image features. Second, we obtained temporal metadata from camera trap images, and after cyclical encoding, we used a residual multilayer perceptron (MLP) network to obtain temporal features. Finally, the image features and temporal features were fused in wildlife identification by a dynamic MLP module. The experimental results on the Camdeboo dataset show that the accuracy of wildlife recognition after fusing the image and temporal information is about 93.10%, which is an improvement of 0.53%, 0.94%, 1.35%, 2.93%, and 5.98%, respectively, compared with the ResNet50, VGG19, ShuffleNetV2-2.0x, MobileNetV3-L, and ConvNeXt-B models. Furthermore, we demonstrate the effectiveness of the proposed method on different national park camera trap datasets. Our method provides a new idea for fusing animal domain knowledge to further improve the accuracy of wildlife recognition, which can better serve wildlife conservation and ecological research. Full article
(This article belongs to the Special Issue The Applications of Artificial Intelligence on the Conservation of Biodiversity)
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