{
"title": "The potential for AI to revolutionize conservation: a horizon scan",
"authors": [
{
"given": "Sam A.",
"family": "Reynolds",
"affiliation_ids": [
1
],
"email": null,
"orcid": null,
"corresponding": false
},
{
"given": "Sara",
"family": "Beery",
"affiliation_ids": [
2
],
"email": null,
"orcid": null,
"corresponding": false
},
{
"given": "Neil",
"family": "Burgess",
"affiliation_ids": [
3,
4
],
"email": null,
"orcid": null,
"corresponding": false
},
{
"given": "Mark",
"family": "Burgman",
"affiliation_ids": [
5
],
"email": null,
"orcid": null,
"corresponding": false
},
{
"given": "Stuart H.M.",
"family": "Butchart",
"affiliation_ids": [
6,
7
],
"email": null,
"orcid": null,
"corresponding": false
},
{
"given": "Steven J.",
"family": "Cooke",
"affiliation_ids": [
8
],
"email": null,
"orcid": null,
"corresponding": false
},
{
"given": "David",
"family": "Coomes",
"affiliation_ids": [
9
],
"email": null,
"orcid": null,
"corresponding": false
},
{
"given": "Finn",
"family": "Danielsen",
"affiliation_ids": [
10
],
"email": null,
"orcid": null,
"corresponding": false
},
{
"given": "Enrico",
"family": "Di Minin",
"affiliation_ids": [
11,
12
],
"email": null,
"orcid": null,
"corresponding": false
},
{
"given": "América Paz",
"family": "Durán",
"affiliation_ids": [
13
],
"email": null,
"orcid": null,
"corresponding": false
},
{
"given": "Francis",
"family": "Gassert",
"affiliation_ids": [
14
],
"email": null,
"orcid": null,
"corresponding": false
},
{
"given": "Amy",
"family": "Hinsley",
"affiliation_ids": [
15
],
"email": null,
"orcid": null,
"corresponding": false
},
{
"given": "Sadiq",
"family": "Jaffer",
"affiliation_ids": [
16
],
"email": null,
"orcid": null,
"corresponding": false
},
{
"given": "Julia P.G.",
"family": "Jones",
"affiliation_ids": [
17
],
"email": null,
"orcid": null,
"corresponding": false
},
{
"given": "Binbin V.",
"family": "Li",
"affiliation_ids": [
18,
19
],
"email": null,
"orcid": null,
"corresponding": false
},
{
"given": "Oisin",
"family": "Mac Aodha",
"affiliation_ids": [
20
],
"email": null,
"orcid": null,
"corresponding": false
},
{
"given": "Anil",
"family": "Madhavapeddy",
"affiliation_ids": [
21
],
"email": null,
"orcid": null,
"corresponding": false
},
{
"given": "Stephanie A.L.",
"family": "O’Donnell",
"affiliation_ids": [
22
],
"email": null,
"orcid": null,
"corresponding": false
},
{
"given": "William M.",
"family": "Oxbury",
"affiliation_ids": [
23
],
"email": null,
"orcid": null,
"corresponding": false
},
{
"given": "Lloyd",
"family": "Peck",
"affiliation_ids": [
24
],
"email": null,
"orcid": null,
"corresponding": false
},
{
"given": "Nathalie",
"family": "Pettorelli",
"affiliation_ids": [
25
],
"email": null,
"orcid": null,
"corresponding": false
},
{
"given": "Jon Paul",
"family": "Rodríguez",
"affiliation_ids": [
26,
27,
28
],
"email": null,
"orcid": null,
"corresponding": false
},
{
"given": "Emily",
"family": "Shuckburgh",
"affiliation_ids": [
29
],
"email": null,
"orcid": null,
"corresponding": false
},
{
"given": "Bernardo",
"family": "Strassburg",
"affiliation_ids": [
30,
31
],
"email": null,
"orcid": null,
"corresponding": false
},
{
"given": "Hiromi",
"family": "Yamashita",
"affiliation_ids": [
32
],
"email": null,
"orcid": null,
"corresponding": false
},
{
"given": "Zhongqi",
"family": "Miao",
"affiliation_ids": [
33
],
"email": null,
"orcid": null,
"corresponding": false
},
{
"given": "William J.",
"family": "Sutherland",
"affiliation_ids": [
1
],
"email": null,
"orcid": null,
"corresponding": false
}
],
"affiliations": [
{
"id": 1,
"text": null
},
{
"id": 2,
"text": null
},
{
"id": 3,
"text": null
},
{
"id": 4,
"text": null
},
{
"id": 5,
"text": null
},
{
"id": 6,
"text": null
},
{
"id": 7,
"text": null
},
{
"id": 8,
"text": null
},
{
"id": 9,
"text": null
},
{
"id": 10,
"text": null
},
{
"id": 11,
"text": null
},
{
"id": 12,
"text": null
},
{
"id": 13,
"text": null
},
{
"id": 14,
"text": null
},
{
"id": 15,
"text": null
},
{
"id": 16,
"text": null
},
{
"id": 17,
"text": null
},
{
"id": 18,
"text": null
},
{
"id": 19,
"text": null
},
{
"id": 20,
"text": null
},
{
"id": 21,
"text": null
},
{
"id": 22,
"text": null
},
{
"id": 23,
"text": null
},
{
"id": 24,
"text": null
},
{
"id": 25,
"text": null
},
{
"id": 26,
"text": null
},
{
"id": 27,
"text": null
},
{
"id": 28,
"text": null
},
{
"id": 29,
"text": null
},
{
"id": 30,
"text": null
},
{
"id": 31,
"text": null
},
{
"id": 32,
"text": null
},
{
"id": 33,
"text": null
}
],
"journal": "Trends in Ecology & Evolution",
"volume": "xx",
"issue": "xx",
"pages": "1-17",
"doi": "10.1016/j.tree.2024.11.013",
"published_date": "2024",
"received_date": null,
"accepted_date": null,
"abstract": "Artificial Intelligence (AI) is an emerging tool that could be leveraged to identify the effective conservation solutions demanded by the urgent biodiversity crisis. We present the results of our horizon scan of AI applications likely to significantly benefit biological conservation. An international panel of conservation scientists and AI experts identified 21 key ideas. These included species recognition to uncover ‘dark diversity’, multimodal models to improve biodiversity loss predictions, monitoring wildlife trade, and addressing human–wildlife conflict. We consider the potential negative impacts of AI adoption, such as AI colonialism and loss of essential conservation skills, and suggest how the conservation field might adapt to harness the benefits of AI while mitigating its risks.",
"keywords": [],
"funding": [],
"references": [
{
"raw_index": 1,
"authors": [
{
"given": "W.J.",
"family": "Ripple"
}
],
"year": "2017",
"title": "World scientists’ warning to humanity: a second notice",
"container": "BioScience",
"volume": "67",
"issue": null,
"pages": "1026–1028",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 2,
"authors": [
{
"given": "L.J.",
"family": "Pollock"
}
],
"year": "2020",
"title": "Protecting biodiversity (in all its complexity): new models and methods",
"container": "Trends Ecol. Evol.",
"volume": "35",
"issue": null,
"pages": "1119–1128",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 3,
"authors": [
{
"given": "P.J.",
"family": "Ferraro"
}
],
"year": "2019",
"title": "Causal inference in coupled human and natural systems",
"container": "Proc. Natl. Acad. Sci. U. S. A.",
"volume": "116",
"issue": null,
"pages": "5311–5318",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 4,
"authors": [
{
"given": "A.",
"family": "Nuno"
}
],
"year": "2014",
"title": "Managing social-ecological systems under uncertainty: implementation in the real world",
"container": "Ecol. Soc.",
"volume": "19",
"issue": null,
"pages": "52",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 5,
"authors": [
{
"given": "F.",
"family": "Jiang"
}
],
"year": "2017",
"title": "Artificial intelligence in healthcare: past, present and future",
"container": "Stroke Vasc. Neurol.",
"volume": "2",
"issue": null,
"pages": "230–243",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 6,
"authors": [
{
"given": "A.S.",
"family": "Gill"
}
],
"year": "2019",
"title": "Artificial intelligence and international security: the long view",
"container": "Ethics Int. Aff.",
"volume": "33",
"issue": null,
"pages": "169–179",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 7,
"authors": [
{
"given": "M.I.",
"family": "Jordan"
},
{
"given": "T.M.",
"family": "Mitchell"
}
],
"year": "2015",
"title": "Machine learning: trends, perspectives, and prospects",
"container": "Science",
"volume": "349",
"issue": null,
"pages": "255–260",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 8,
"authors": [
{
"given": "L.",
"family": "Xu"
}
],
"year": "2023",
"title": "Enhanced persistent orientation of asymmetric-top molecules induced by cross-polarized terahertz pulses",
"container": "Phys. Rev. Res.",
"volume": "7",
"issue": null,
"pages": "013201",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 9,
"authors": [
{
"given": "A.D.",
"family": "Binley"
}
],
"year": "2024",
"title": "Making the most of existing data in conservation research",
"container": "Perspect. Ecol. Conserv.",
"volume": "22",
"issue": null,
"pages": "122–128",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 10,
"authors": [
{
"family": "Convention on Biological Diversity"
}
],
"year": "2022",
"title": "Kunming–Montreal Global Biodiversity Framework",
"container": null,
"volume": null,
"issue": null,
"pages": null,
"doi": null,
"type": "book"
},
{
"raw_index": 11,
"authors": [
{
"family": "Science in the Age of AI Working Group"
}
],
"year": "2024",
"title": "Science in the Age of AI: How Artificial Intelligence Is Changing the Nature and Method of Scientific Research",
"container": null,
"volume": null,
"issue": null,
"pages": null,
"doi": null,
"type": "book"
},
{
"raw_index": 12,
"authors": [
{
"given": "M.",
"family": "Krenn"
}
],
"year": "2022",
"title": "On scientific understanding with artificial intelligence",
"container": "Nat. Rev. Phys.",
"volume": "4",
"issue": null,
"pages": "761–769",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 13,
"authors": [
{
"given": "E.",
"family": "Cole"
}
],
"year": "2023",
"title": "Spatial implicit neural representations for global-scale species mapping",
"container": "ArXiv",
"volume": null,
"issue": null,
"pages": null,
"doi": "10.48550/arXiv.2306.02564",
"type": "other"
},
{
"raw_index": 14,
"authors": [
{
"given": "D.",
"family": "Silvestro"
}
],
"year": "2022",
"title": "Improving biodiversity protection through artificial intelligence",
"container": "Nat. Sustain.",
"volume": "5",
"issue": null,
"pages": "415–424",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 15,
"authors": [
{
"given": "T.",
"family": "Simlai"
},
{
"given": "C.",
"family": "Sandbrook"
}
],
"year": "2021",
"title": "Digital surveillance technologies in conservation and their social implications",
"container": "Conservation Technology",
"volume": null,
"issue": null,
"pages": "239–254",
"doi": null,
"type": "chapter"
},
{
"raw_index": 16,
"authors": [
{
"given": "P.",
"family": "Spelda"
},
{
"given": "V.",
"family": "Stritecky"
}
],
"year": "2020",
"title": "The future of human-artificial intelligence nexus and its environmental costs",
"container": "Futures",
"volume": "117",
"issue": null,
"pages": "102531",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 17,
"authors": [
{
"given": "E.",
"family": "García-Roselló"
}
],
"year": "2023",
"title": "The biased distribution of existing information on biodiversity hinders its use in conservation, and we need an integrative approach to act urgently",
"container": "Biol. Conserv.",
"volume": "283",
"issue": null,
"pages": "110118",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 18,
"authors": [
{
"given": "S.",
"family": "Christin"
}
],
"year": "2019",
"title": "Applications for deep learning in ecology",
"container": "Methods Ecol. Evol.",
"volume": "10",
"issue": null,
"pages": "1632-1644",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 19,
"authors": [
{
"given": "Y.",
"family": "Xian"
}
],
"year": "2017",
"title": "Zero-shot learning – the good, the bad and the ugly",
"container": "2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
"volume": null,
"issue": null,
"pages": "3077–3086",
"doi": null,
"type": "chapter"
},
{
"raw_index": 20,
"authors": [
{
"given": "Z.",
"family": "Xi"
}
],
"year": "2023",
"title": "The rise and potential of large language model based agents: a survey",
"container": "ArXiv",
"volume": null,
"issue": null,
"pages": null,
"doi": "10.48550/arXiv.2309.07864",
"type": "other"
},
{
"raw_index": 21,
"authors": [
{
"given": "R.",
"family": "Bommasani"
}
],
"year": "2021",
"title": "On the opportunities and risks of foundation models",
"container": "ArXiv",
"volume": null,
"issue": null,
"pages": null,
"doi": "10.48550/arXiv.2108.07258",
"type": "other"
},
{
"raw_index": 22,
"authors": [
{
"given": "M.",
"family": "Oquab"
}
],
"year": "2023",
"title": "DINOv2: learning robust visual features without supervision",
"container": "ArXiv",
"volume": null,
"issue": null,
"pages": null,
"doi": "10.48550/arXiv.2304.07193",
"type": "other"
},
{
"raw_index": 23,
"authors": [
{
"given": "T.",
"family": "Chen"
}
],
"year": "2020",
"title": "Big self-supervised models are strong semi-supervised learners",
"container": "Proceedings of the 34th International Conference on Neural Information Processing Systems",
"volume": null,
"issue": null,
"pages": "22243–22255",
"doi": null,
"type": "chapter"
},
{
"raw_index": 24,
"authors": [
{
"given": "W.J.",
"family": "Sutherland"
}
],
"year": "2014",
"title": "A horizon scan of global conservation issues for 2014",
"container": "Trends Ecol. Evol.",
"volume": "29",
"issue": null,
"pages": "15-22",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 25,
"authors": [
{
"given": "W.J.",
"family": "Sutherland"
}
],
"year": "2024",
"title": "A horizon scan of global biological conservation issues for 2024",
"container": "Trends Ecol. Evol.",
"volume": "39",
"issue": null,
"pages": "89–100",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 26,
"authors": [
{
"given": "N.",
"family": "Mukherjee"
}
],
"year": "2015",
"title": "The Delphi technique in ecology and biological conservation: applications and guidelines",
"container": "Methods Ecol. Evol.",
"volume": "6",
"issue": null,
"pages": "1097-1109",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 27,
"authors": [
{
"given": "W.J.",
"family": "Sutherland"
}
],
"year": "2019",
"title": "Ten years on: a review of the first global conservation horizon scan",
"container": "Trends Ecol. Evol.",
"volume": "34",
"issue": null,
"pages": "139–153",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 28,
"authors": [
{
"given": "G.",
"family": "Van Horn"
}
],
"year": "2018",
"title": "The iNaturalist Species Classification and Detection Dataset",
"container": "2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition",
"volume": null,
"issue": null,
"pages": "8769-8778",
"doi": null,
"type": "chapter"
},
{
"raw_index": 29,
"authors": [
{
"given": "H.",
"family": "Goëau"
}
],
"year": "2013",
"title": "Pl@ntNet mobile app",
"container": "Proceedings of the 21st ACM International Conference on Multimedia",
"volume": null,
"issue": null,
"pages": "423-424",
"doi": null,
"type": "chapter"
},
{
"raw_index": 30,
"authors": [
{
"given": "C.",
"family": "Chalmers"
}
],
"year": "2019",
"title": "Conservation AI: live stream analysis for the detection of endangered species using convolutional neural networks and drone technology",
"container": "ArXiv",
"volume": null,
"issue": null,
"pages": null,
"doi": "10.48550/arXiv.1910.07360",
"type": "other"
},
{
"raw_index": 31,
"authors": [
{
"given": "V.",
"family": "Pirotta"
}
],
"year": "2022",
"title": "Detecting illegal wildlife trafficking via real time tomography 3D X-ray imaging and automated algorithms",
"container": "Front. Conserv. Sci.",
"volume": "3",
"issue": null,
"pages": "757950",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 32,
"authors": [
{
"given": "T.A.",
"family": "de Lorm"
}
],
"year": "2023",
"title": "Optimizing the automated recognition of individual animals to support population monitoring",
"container": "Ecol. Evol.",
"volume": "13",
"issue": null,
"pages": "e10260",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 33,
"authors": [
{
"given": "B.",
"family": "Zhao"
}
],
"year": "2024",
"title": "Labeled data selection for category discovery",
"container": "ArXiv",
"volume": null,
"issue": null,
"pages": null,
"doi": "10.48550/arXiv.2406.04898",
"type": "other"
},
{
"raw_index": 34,
"authors": [
{
"given": "Y.",
"family": "Lu"
}
],
"year": "2024",
"title": "Accelerating the discovery of biodiversity by detecting ‘new’ species based on machine learning method",
"container": "Res. Sq.",
"volume": null,
"issue": null,
"pages": null,
"doi": "10.21203/rs.3.rs-3832815/v1",
"type": "other"
},
{
"raw_index": 35,
"authors": [
{
"given": "M.P.",
"family": "Dias"
}
],
"year": "2019",
"title": "Threats to seabirds: a global assessment",
"container": "Biol. Conserv.",
"volume": "237",
"issue": null,
"pages": "525-537",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 36,
"authors": [
{
"given": "N.",
"family": "Da Rocha"
}
],
"year": "2021",
"title": "Reduction in seabird mortality in Namibian fisheries following the introduction of bycatch regulation",
"container": "Biol. Conserv.",
"volume": "253",
"issue": null,
"pages": "108915",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 37,
"authors": [
{
"given": "S.",
"family": "Kahl"
}
],
"year": "2021",
"title": "BirdNET: A deep learning solution for avian diversity monitoring",
"container": "Ecol. Inform.",
"volume": "61",
"issue": null,
"pages": "101236",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 38,
"authors": [
{
"given": "R.",
"family": "Manzano-Rubio"
}
],
"year": "2022",
"title": "Low-cost open-source recorders and ready-to-use machine learning approaches provide effective monitoring of threatened species",
"container": "Ecol. Inform.",
"volume": "72",
"issue": null,
"pages": "101910",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 39,
"authors": [
{
"given": "B.",
"family": "Williams"
}
],
"year": "2024",
"title": "Unlocking the soundscape of coral reefs with artificial intelligence: pretrained networks and unsupervised learning win outbio",
"container": "BioRxiv",
"volume": null,
"issue": null,
"pages": null,
"doi": "10.1101/024.02.02.578582",
"type": "other"
},
{
"raw_index": 40,
"authors": [
{
"given": "J.",
"family": "Müller"
}
],
"year": "2023",
"title": "Soundscapes and deep learning enable tracking biodiversity recovery in tropical forests",
"container": "Nat. Commun.",
"volume": "14",
"issue": null,
"pages": "6191",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 41,
"authors": [
{
"given": "P.",
"family": "Linhart"
}
],
"year": "2022",
"title": "The potential for acoustic individual identification in mammals",
"container": "Mamm. Biol.",
"volume": "102",
"issue": null,
"pages": "667-683",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 42,
"authors": [
{
"given": "D.",
"family": "Snyder"
}
],
"year": "2018",
"title": "X-Vectors: robust DNN embeddings for speaker recognition",
"container": "2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",
"volume": null,
"issue": null,
"pages": "5329–5333",
"doi": null,
"type": "chapter"
},
{
"raw_index": 43,
"authors": [
{
"given": "L.",
"family": "Bouffaut"
}
],
"year": "2022",
"title": "Eavesdropping at the speed of light: distributed acoustic sensing of baleen whales in the arctic",
"container": "Front. Mar. Sci.",
"volume": "9",
"issue": null,
"pages": "901348",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 44,
"authors": [
{
"given": "W.S.D.",
"family": "Wilcock"
}
],
"year": "2023",
"title": "Distributed acoustic sensing recordings of low-frequency whale calls and ship noise offshore Central Oregon",
"container": "JASA Express Lett.",
"volume": "3",
"issue": null,
"pages": "026002",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 45,
"authors": [
{
"given": "S.M.V.",
"family": "Parijs"
}
],
"year": "2009",
"title": "Management and research applications of real-time and archival passive acoustic sensors over varying temporal and spatial scales",
"container": "Mar. Ecol. Prog. Ser.",
"volume": "395",
"issue": null,
"pages": "21-36",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 46,
"authors": [
{
"given": "M.",
"family": "Sanguineti"
}
],
"year": "2021",
"title": "Real-time continuous acoustic monitoring of marine mammals in the Mediterranean Sea",
"container": "J. Mar. Sci. Eng.",
"volume": "9",
"issue": null,
"pages": "1389",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 47,
"authors": [
{
"given": "P.",
"family": "Helber"
}
],
"year": "2019",
"title": "EuroSAT: a novel dataset and deep learning benchmark for land use and land cover classification",
"container": "IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.",
"volume": "12",
"issue": null,
"pages": "2217-2226",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 48,
"authors": [
{
"given": "I.",
"family": "Duporge"
}
],
"year": "2021",
"title": "Using very-high-resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes",
"container": "Remote Sens. Ecol. Conserv.",
"volume": "7",
"issue": null,
"pages": "369–381",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 49,
"authors": [
{
"given": "H.C.",
"family": "Cubaynes"
},
{
"given": "P.T.",
"family": "Fretwell"
}
],
"year": "2022",
"title": "Whales from space dataset, an annotated satellite image dataset of whales for training machine learning models",
"container": "Sci. Data",
"volume": "9",
"issue": null,
"pages": "245",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 50,
"authors": [
{
"given": "N.",
"family": "Pettorelli"
}
],
"year": "2024",
"title": "Deep learning and satellite remote sensing for biodiversity monitoring and conservation",
"container": "Remote Sens. Ecol. Conserv.",
"volume": null,
"issue": null,
"pages": null,
"doi": "10.1002/rse2.415",
"type": "journal-article"
},
{
"raw_index": 51,
"authors": [
{
"given": "M.C.",
"family": "Lisaius"
}
],
"year": "2024",
"title": "Using Barlow twins to create representations from cloud-corrupted remote sensing time series",
"container": "IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.",
"volume": "17",
"issue": null,
"pages": "13162–13168",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 52,
"authors": [
{
"given": "J.",
"family": "Jakubik"
}
],
"year": "2023",
"title": "Foundation models for generalist geospatial artificial intelligence",
"container": "ArXiv",
"volume": null,
"issue": null,
"pages": null,
"doi": "10.48550/arXiv.2310.18660",
"type": "other"
},
{
"raw_index": 53,
"authors": [
{
"given": "S.",
"family": "Diao"
}
],
"year": "2024",
"title": "LMFlow: an extensible toolkit for finetuning and inference of large foundation models",
"container": "ArXiv",
"volume": null,
"issue": null,
"pages": null,
"doi": "10.48550/arXiv.2306.12420",
"type": "other"
},
{
"raw_index": 54,
"authors": [
{
"given": "M.",
"family": "Ortega Adarme"
}
],
"year": "2022",
"title": "Improving deforestation detection on tropical rainforests using Sentinel-1 data and convolutional neural networks",
"container": "Remote Sens.",
"volume": "14",
"issue": null,
"pages": "3290",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 55,
"authors": [
{
"given": "W.",
"family": "Ye"
}
],
"year": "2022",
"title": "Pine pest detection using remote sensing satellite images combined with a multi-scale attention-UNet model",
"container": "Ecol. Inform.",
"volume": "72",
"issue": null,
"pages": "101906",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 56,
"authors": [
{
"given": "S.",
"family": "López"
}
],
"year": "2020",
"title": "Landscape change in Southern Ecuador: an indicator-based and multi-temporal evaluation of land use and land cover in a mixed-use protected area",
"container": "Ecol. Indic.",
"volume": "115",
"issue": null,
"pages": "106357",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 57,
"authors": [
{
"given": "B.",
"family": "Mittelstadt"
}
],
"year": "2023",
"title": "To protect science, we must use LLMs as zero-shot translators",
"container": "Nat. Hum. Behav.",
"volume": "7",
"issue": null,
"pages": "1830–1832",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 58,
"authors": [
{
"given": "C.F.",
"family": "Brown"
}
],
"year": "2022",
"title": "Dynamic World, Near real-time global 10 m land use land cover mapping",
"container": "Sci. Data",
"volume": "9",
"issue": null,
"pages": "251",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 59,
"authors": [
{
"given": "S.V.",
"family": "Silva"
}
],
"year": "2022",
"title": "Global estimation and mapping of the conservation status of tree species using artificial intelligence",
"container": "Front. Plant Sci.",
"volume": "13",
"issue": null,
"pages": "839792",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 60,
"authors": [
{
"given": "N.",
"family": "Rieke"
}
],
"year": "2020",
"title": "The future of digital health with federated learning",
"container": "NPJ Digit. Med.",
"volume": "3",
"issue": null,
"pages": "119",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 61,
"authors": [
{
"given": "M.",
"family": "Nasr"
}
],
"year": "2023",
"title": "Scalable extraction of training data from (production) language models",
"container": "ArXiv",
"volume": null,
"issue": null,
"pages": null,
"doi": "10.48550/arXiv.2311.17035",
"type": "other"
},
{
"raw_index": 62,
"authors": [
{
"given": "R.",
"family": "Kulkarni"
},
{
"given": "E.",
"family": "Di Minin"
}
],
"year": "2023",
"title": "Towards automatic detection of wildlife trade using machine vision models",
"container": "Biol. Conserv.",
"volume": "279",
"issue": null,
"pages": "109924",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 63,
"authors": [
{
"given": "M.C.",
"family": "Isaac"
}
],
"year": "2024",
"title": "Status and trends in the international wildlife trade in Chameleons with a focus on Tanzania",
"container": "PLoS ONE",
"volume": "19",
"issue": null,
"pages": "e0300371",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 64,
"authors": [
{
"given": "E.",
"family": "Di Minin"
}
],
"year": "2021",
"title": "How to address data privacy concerns when using social media data in conservation science",
"container": "Conserv. Biol.",
"volume": "35",
"issue": null,
"pages": "437-446",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 65,
"authors": [
{
"given": "W.C.",
"family": "O’Malley"
}
],
"year": "2024",
"title": "Machine learning allows for large- scale habitat prediction of a wide-ranging carnivore across diverse ecoregions",
"container": "Landsc. Ecol.",
"volume": "39",
"issue": null,
"pages": "106",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 66,
"authors": [
{
"given": "J.",
"family": "Estopinan"
}
],
"year": "2024",
"title": "Modelling species distributions with deep learning to predict plant extinction risk and assess climate change impacts",
"container": "ArXiv",
"volume": null,
"issue": null,
"pages": null,
"doi": "10.48550/arXiv.2401.05470",
"type": "other"
},
{
"raw_index": 79,
"authors": [
{
"given": "O.",
"family": "Berger-Tal"
}
],
"year": "2024",
"title": "Leveraging AI to improve evidence synthesis in conservation",
"container": "Trends Ecol. Evol.",
"volume": "39",
"issue": null,
"pages": "548–557",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 67,
"authors": [
{
"given": "H.-T.",
"family": "Zhang"
}
],
"year": "2024",
"title": "A novel hybrid model for species distribution prediction using neural networks and Grey Wolf Optimizer algorithm",
"container": "Sci. Rep.",
"volume": "14",
"issue": null,
"pages": "11505",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 80,
"authors": [
{
"given": "A.",
"family": "Blaizot"
}
],
"year": "2022",
"title": "Using artificial intelligence methods for systematic review in health sciences: a systematic review",
"container": "Res. Synth. Methods",
"volume": "13",
"issue": null,
"pages": "353–362",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 68,
"authors": [
{
"given": "E.",
"family": "Gross"
}
],
"year": "2021",
"title": "A Future for All: The Need for Human- Wildlife Coexistence",
"container": null,
"volume": null,
"issue": null,
"pages": null,
"doi": "10.1038/s41598-024-62285-8",
"type": "book"
},
{
"raw_index": 81,
"authors": [
{
"given": "J.H.",
"family": "Elliott"
}
],
"year": "2014",
"title": "Living systematic reviews: an emerging opportunity to narrow the evidence-practice gap",
"container": "PLoS Med.",
"volume": "11",
"issue": null,
"pages": "e1001603",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 69,
"authors": [
{
"given": "J.S.",
"family": "Dertien"
}
],
"year": "2023",
"title": "Mitigating human–wildlife conflict and monitoring endangered tigers using a real-time camera-based alert system",
"container": "BioScience",
"volume": "73",
"issue": null,
"pages": "748–757",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 82,
"authors": [
{
"given": "R.D.",
"family": "Zenni"
}
],
"year": "2023",
"title": "Multi-lingual literature searches are needed to unveil global knowledge",
"container": "J. Appl. Ecol.",
"volume": "60",
"issue": null,
"pages": "380–383",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 83,
"authors": [
{
"given": "A.P.",
"family": "Christie"
}
],
"year": "2021",
"title": "The challenge of biased evidence in conservation",
"container": "Conserv. Biol.",
"volume": "35",
"issue": null,
"pages": "249–262",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 70,
"authors": [
{
"given": "D.",
"family": "Leclère"
}
],
"year": "2020",
"title": "Bending the curve of terrestrial biodiversity needs an integrated strategy",
"container": "Nature",
"volume": "585",
"issue": null,
"pages": "551–556",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 84,
"authors": [
{
"given": "S.",
"family": "Chowdhury"
}
],
"year": "2022",
"title": "Growth of non-English-language literature on biodiversity conservation",
"container": "Conserv. Biol. J. Soc. Conserv. Biol.",
"volume": "36",
"issue": null,
"pages": "e13883",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 71,
"authors": [
{
"given": "T.",
"family": "Newbold"
}
],
"year": "2020",
"title": "Non-linear changes in modelled terrestrial ecosystems subjected to perturbations",
"container": "Sci. Rep.",
"volume": "10",
"issue": null,
"pages": "14051",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 85,
"authors": [
{
"given": "A.J.",
"family": "Thirunavukarasu"
}
],
"year": "2023",
"title": "Large language models in medicine",
"container": "Nat. Med.",
"volume": "29",
"issue": null,
"pages": "1930–1940",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 72,
"authors": [
{
"given": "S.M.",
"family": "Díaz"
}
],
"year": "2019",
"title": "The Global Assessment Report on Biodiversity and Ecosystem Services: Summary for Policy Makers",
"container": null,
"volume": null,
"issue": null,
"pages": null,
"doi": null,
"type": "book"
},
{
"raw_index": 86,
"authors": [
{
"given": "C.",
"family": "Tyler"
}
],
"year": "2023",
"title": "AI tools as science policy advisers? The potential and the pitfalls",
"container": "Nature",
"volume": "622",
"issue": null,
"pages": "27–30",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 87,
"authors": [
{
"given": "R.",
"family": "Duffy"
}
],
"year": "2019",
"title": "Why we must question the militarisation of conservation",
"container": "Biol. Conserv.",
"volume": "232",
"issue": null,
"pages": "66–73",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 73,
"authors": [
{
"given": "A.",
"family": "Tzachor"
}
],
"year": "2023",
"title": "Digital twins: a stepping stone to achieve ocean sustainability?",
"container": "NPJ Ocean Sustain.",
"volume": "2",
"issue": null,
"pages": "16",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 88,
"authors": [
{
"given": "R.",
"family": "Debnath"
}
],
"year": "2023",
"title": "Harnessing human and machine intelligence for planetary-level climate action",
"container": "NPJ Clim. Action",
"volume": "2",
"issue": null,
"pages": "20",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 74,
"authors": [
{
"given": "D.A.",
"family": "Keith"
}
],
"year": "2022",
"title": "A function-based typology for Earth’s ecosystems",
"container": "Nature",
"volume": "610",
"issue": null,
"pages": "513–518",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 89,
"authors": [
{
"given": "D.W.S.",
"family": "Challender"
}
],
"year": "2022",
"title": "Mischaracterizing wildlife trade and its impacts may mislead policy processes",
"container": "Conserv. Lett.",
"volume": "15",
"issue": null,
"pages": "e12832",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 75,
"authors": [
{
"given": "D.",
"family": "Kochkov"
}
],
"year": "2024",
"title": "Neural general circulation models for weather and climate",
"container": "Nature",
"volume": "632",
"issue": null,
"pages": "1060–1066",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 76,
"authors": [
{
"given": "S.",
"family": "Maleki"
}
],
"year": "2020",
"title": "Wetland restoration prioritization using artificial neural networks",
"container": "Wetlands",
"volume": "40",
"issue": null,
"pages": "179–192",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 90,
"authors": [
{
"given": "F.",
"family": "Danielsen"
}
],
"year": "2021",
"title": "The concept, practice, application, and results of locally based monitoring of the environment",
"container": "BioScience",
"volume": "71",
"issue": null,
"pages": "484–502",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 77,
"authors": [
{
"given": "J.G.C.",
"family": "Ball"
}
],
"year": "2022",
"title": "Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation",
"container": "Methods Ecol. Evol.",
"volume": "13",
"issue": null,
"pages": "2622–2634",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 91,
"authors": [
{
"given": "S.",
"family": "Danziger"
}
],
"year": "2011",
"title": "Extraneous factors in judicial decisions",
"container": "Proc. Natl. Acad. Sci.",
"volume": "108",
"issue": null,
"pages": "6889–6892",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 78,
"authors": [
{
"given": "P.",
"family": "Potapov"
}
],
"year": "2022",
"title": "Global maps of cropland extent and change show accelerated cropland expansion in the twenty- first century",
"container": "Nat. Food",
"volume": "3",
"issue": null,
"pages": "19–28",
"doi": null,
"type": "journal-article"
},
{
"raw_index": 92,
"authors": [
{
"family": "R Core Team"
}
],
"year": "2018",
"title": "R: A Language and Environment for Statistical Computing",
"container": null,
"volume": null,
"issue": null,
"pages": null,
"doi": null,
"type": "book"
}
],
"_raw_references": [
"†https://www.skylight.global/",
"iihttps://arbimon.org/",
"iiihttps://sentinel.conservationxlabs.com/",
"ivhttps://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/",
"vhttps://github.blog/news-insights/product-news/github-copilot-is-generally-available-to-all-developers",
"vihttps://observation.org/",
"viihttps://lens.google/",
"viiihttps://merlin.allaboutbirds.org",
"ixhttps://news.mongabay.com/2018/06/this-tiny-camera-aims-to-catch-poachers-before-they-kill/",
"xhttps://commission.europa.eu/strategy-and-policy/priorities-2019-2024/european-green-deal\\_en",
"ixhttps://zenodo.org/records/13170385 1. Ripple, W.J. _et al._ (2017) World scientists’ warning to humanity: a second notice. _BioScience_ **67**, 1026–1028 2. Pollock, L.J. _et al._ (2020) Protecting biodiversity (in all its complexity): new models and methods. _Trends Ecol. Evol._ **35**, 1119–1128 3. Ferraro, P.J. _et al._ (2019) Causal inference in coupled human and natural systems. _Proc. Natl. Acad. Sci. U. S. A._ **116**, 5311–5318 4. Nuno, A. _et al._ (2014) Managing social-ecological systems under uncertainty: implementation in the real world. _Ecol. Soc._ **19**, 52 5. Jiang, F. _et al._ (2017) Artificial intelligence in healthcare: past, present and future. _Stroke Vasc. Neurol._ **2**, 230–243 6. Gill, A.S. (2019) Artificial intelligence and international security: the long view. _Ethics Int. Aff._ **33**, 169–179 7. Jordan, M.I. and Mitchell, T.M. (2015) Machine learning: trends, perspectives, and prospects. _Science_ **349**, 255–260 8. Xu, L. _et al._ (2023) Enhanced persistent orientation of asymmetric-top molecules induced by cross-polarized terahertz pulses. _Phys. Rev. Res._ **7**, 013201 9. Binley, A.D. _et al._ (2024) Making the most of existing data in conservation research. _Perspect. Ecol. Conserv._ **22**, 122–128 10. Convention on Biological Diversity (2022) _Kunming–Montreal Global Biodiversity Framework_, United Nations Environment Programme 11. Science in the Age of AI Working Group (2024) _Science in the Age of AI: How Artificial Intelligence Is Changing the Nature and Method of Scientific Research_, The Royal Society 12. Krenn, M. _et al._ (2022) On scientific understanding with artificial intelligence. _Nat. Rev. Phys._ **4**, 761–769 13. Cole, E. _et al._ (2023) Spatial implicit neural representations for global-scale species mapping. _ArXiv_, Published online June 5, 2023. https://doi.org/10.48550/arXiv.2306.02564 14. Silvestro, D. _et al._ (2022) Improving biodiversity protection through artificial intelligence. _Nat. Sustain._ **5**, 415–424 15. Simlai, T. and Sandbrook, C. (2021) Digital surveillance technolo- gies in conservation and their social implications. In _Conservation Technology_ (Wich, S.A. and Piel, A.K., eds), pp. 239–254, Oxford University Press 16. Spelda, P. and Stritecky, V. (2020) The future of human-artificial intelligence nexus and its environmental costs. _Futures_ **117**, 102531 17. García-Roselló, E. _et al._ (2023) The biased distribution of existing information on biodiversity hinders its use in conservation, and we need an integrative approach to act urgently. _Biol. Conserv._ 283, 110118 18. Christin, S. _et al._ (2019) Applications for deep learning in ecology. _Methods Ecol. Evol._ 10, 1632-1644 19. Xian, Y. _et al._ (2017) Zero-shot learning – the good, the bad and the ugly. In _2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)_, pp. 3077–3086, IEEE 20. Xi, Z. _et al._ (2023) The rise and potential of large language model based agents: a survey. _ArXiv_, Published online September 14, 2023. https://doi.org/10.48550/arXiv.2309.07864 21. Bommasani, R. _et al._ (2021) On the opportunities and risks of foundation models. _ArXiv_, Published online August 16, 2021. https://doi.org/10.48550/arXiv.2108.07258 22. Oquab, M. _et al._ (2023) DINOv2: learning robust visual features without supervision. _ArXiv_, Published online April 14, 2023. https://doi.org/10.48550/arXiv.2304.07193 23. Chen, T. _et al._ (2020) Big self-supervised models are strong semi-supervised learners. In _Proceedings of the 34th International Conference on Neural Information Processing Systems_, pp. 22243–22255, NIPS 24. Sutherland, W.J. _et al._ (2014) A horizon scan of global conservation issues for 2014. _Trends Ecol. Evol._ 29, 15-22 25. Sutherland, W.J. _et al_. (2024) A horizon scan of global biological conservation issues for 2024. Trends Ecol. Evol. 39, 89–100 26. Mukherjee, N. _et al._ (2015) The Delphi technique in ecology and biological conservation: applications and guidelines. _Methods Ecol. Evol._ 6, 1097-1109 27. Sutherland, W.J. _et al._ (2019) Ten years on: a review of the first global conservation horizon scan. _Trends Ecol. Evol._ 34, 139–153 28. Van Horn, G. _et al._ (2018) The iNaturalist Species Classification and Detection Dataset. In _2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition_, pp. 8769-8778, IEEE 29. Goëau, H. _et al._ (2013) Pl@ntNet mobile app. In _Proceedings of the 21st ACM International Conference on Multimedia_, pp. 423-424 30. Chalmers, C. _et al_. (2019) Conservation AI: live stream analysis for the detection of endangered species using convolutional neural networks and drone technology. _ArXiv_. Published online October 16, 2019. https://doi.org/10.48550/arXiv.1910.07360 31. Pirotta, V. _et al._ (2022) Detecting illegal wildlife trafficking via real time tomography 3D X-ray imaging and automated algorithms. _Front. Conserv. Sci._ **3**, 757950 32. de Lorm, T.A. _et al._ (2023) Optimizing the automated recognition of individual animals to support population monitoring. _Ecol. Evol._ 13, e10260 33. Zhao, B. _et al._ (2024) Labeled data selection for category discovery. ArXiv, Published online June 7, 2024. http://dx.doi.org/10.48550/ arXiv.2406.04898 34. Lu, Y. _et al_. (2024) Accelerating the discovery of biodiversity by detecting ‘new’ species based on machine learning method. Res. Sq., Published online January 9, 2024. https://doi.org/10. 21203/rs.3.rs-3832815/v1 35. Dias, M.P. _et al._ (2019) Threats to seabirds: a global assessment. _Biol. Conserv._ 237, 525-537 36. Da Rocha, N. _et al._ (2021) Reduction in seabird mortality in Namibian fisheries following the introduction of bycatch regulation. _Biol. Conserv._ 253, 108915 37. Kahl, S. _et al._ (2021) BirdNET: A deep learning solution for avian diversity monitoring. _Ecol. Inform._ 61, 101236 38. Manzano-Rubio, R. _et al._ (2022) Low-cost open-source recorders and ready-to-use machine learning approaches provide effective monitoring of threatened species. _Ecol. Inform._ **72**, 101910 39. Williams, B. _et al_. (2024) Unlocking the soundscape of coral reefs with artificial intelligence: pretrained networks and unsupervised learning win outbio. _BioRxiv_, Published online May 8, 2024. https://doi.org/10.1101/024.02.02.578582 40. Müller, J. _et al._ (2023) Soundscapes and deep learning enable tracking biodiversity recovery in tropical forests. _Nat. Commun._ 14, 6191 41. Linhart, P. _et al._ (2022) The potential for acoustic individual identification in mammals. _Mamm. Biol._ 102, 667-683 42. Snyder, D. _et al._ (2018) X-Vectors: robust DNN embeddings for speaker recognition. In _2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)_, pp. 5329–5333, IEEE 43. Bouffaut, L. _et al._ (2022) Eavesdropping at the speed of light: distributed acoustic sensing of baleen whales in the arctic. _Front. Mar. Sci._ 9, 901348 44. Wilcock, W.S.D. _et al._ (2023) Distributed acoustic sensing recordings of low-frequency whale calls and ship noise offshore Central Oregon. _JASA Express Lett._ 3, 026002 45. Parijs, S.M.V. _et al._ (2009) Management and research applications of real-time and archival passive acoustic sensors over varying temporal and spatial scales. _Mar. Ecol. Prog. Ser._ 395, 21-36 46. Sanguineti, M. _et al._ (2021) Real-time continuous acoustic monitoring of marine mammals in the Mediterranean Sea. _J. Mar. Sci. Eng._ 9, 1389 47. Helber, P. _et al._ (2019) EuroSAT: a novel dataset and deep learning benchmark for land use and land cover classification. _IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens._ **12**, 2217-2226 48. Duporge, I. _et al._ (2021) Using very-high-resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes. _Remote Sens. Ecol. Conserv. 7_, 369–381 49. Cubaynes, H.C. and Fretwell, P.T. (2022) Whales from space dataset, an annotated satellite image dataset of whales for training machine learning models. _Sci. Data_ **9**, 245 50. Pettorelli, N. _et al._ (2024) Deep learning and satellite remote sensing for biodiversity monitoring and conservation. _Remote Sens. Ecol. Conserv._, Published online June 17, 2024. https:// doi.org/10.1002/rse2.415 51. Lisaius, M.C. _et al._ (2024) Using Barlow twins to create representations from cloud-corrupted remote sensing time series. _IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens._ 17, 13162–13168 52. Jakubik, J. _et al._ (2023) Foundation models for generalist geospatial artificial intelligence. _ArXiv_, Published online October 28, 2023. https://doi.org/10.48550/arXiv.2310.18660 53. Diao, S. _et al._ (2024) LMFlow: an extensible toolkit for finetuning and inference of large foundation models. _ArXiv_, Published online May 5, 2024. https://doi.org/10.48550/arXiv.2306.12420 54. Ortega Adarme, M. _et al._ (2022) Improving deforestation detection on tropical rainforests using Sentinel-1 data and convolutional neural networks. _Remote Sens_. **14**, 3290 55. Ye, W. _et al._ (2022) Pine pest detection using remote sensing satellite images combined with a multi-scale attention-UNet model. _Ecol. Inform._ 72, 101906 56. López, S. _et al._ (2020) Landscape change in Southern Ecuador: an indicator-based and multi-temporal evaluation of land use and land cover in a mixed-use protected area. _Ecol. Indic._ 115, 106357 57. Mittelstadt, B. _et al._ (2023) To protect science, we must use LLMs as zero-shot translators. _Nat. Hum. Behav._ 7, 1830–1832 58. Brown, C.F. _et al_. (2022) Dynamic World, Near real-time global 10 m land use land cover mapping. _Sci. Data_ 9, 251 59. Silva, S.V. _et al._ (2022) Global estimation and mapping of the conservation status of tree species using artificial intelligence. Front. Plant Sci. 13, 839792 60. Rieke, N. _et al._ (2020) The future of digital health with federated learning. _NPJ Digit. Med._ 3, 119 61. Nasr, M. _et al._ (2023) Scalable extraction of training data from (production) language models. _ArXiv_, Published online November 28, 2023. https://doi.org/10.48550/arXiv.2311.17035 62. Kulkarni, R. and Di Minin, E. (2023) Towards automatic detection of wildlife trade using machine vision models. _Biol. Conserv._ **279**, 109924. 63. Isaac, M.C. _et al._ (2024) Status and trends in the international wildlife trade in Chameleons with a focus on Tanzania. PLoS ONE 19, e0300371 64. Di Minin, E. _et al._ (2021) How to address data privacy concerns when using social media data in conservation science. _Conserv. Biol._ 35, 437-446 65. O’Malley, W.C. _et al._ (2024) Machine learning allows for large- scale habitat prediction of a wide-ranging carnivore across diverse ecoregions. _Landsc. Ecol._ 39, 106 66. Estopinan, J. _et al_. (2024) Modelling species distributions with deep learning to predict plant extinction risk and assess climate change impacts. _ArXiv_, Published online January 10, 2024. https://doi.org/10.48550/arXiv.2401.05470 79. Berger-Tal, O. _et al._ (2024) Leveraging AI to improve evidence synthesis in conservation. _Trends Ecol. Evol._ 39, 548–557 67. Zhang, H.-T. _et al._ (2024) A novel hybrid model for species distribution prediction using neural networks and Grey Wolf Optimizer algorithm. _Sci. Rep._ 14, 11505 80. Blaizot, A. _et al._ (2022) Using artificial intelligence methods for systematic review in health sciences: a systematic review. _Res. Synth. Methods_ 13, 353–362 68. Gross, E. _et al._ (2021) _A Future for All: The Need for Human- Wildlife Coexistence_, World Wildlife Fund https://doi.org/10. 1038/s41598-024-62285-8 81. Elliott, J.H. _et al._ (2014) Living systematic reviews: an emerging opportunity to narrow the evidence-practice gap. _PLoS Med._ 11, e1001603 69. Dertien, J.S. _et al._ (2023) Mitigating human–wildlife conflict and monitoring endangered tigers using a real-time camera-based alert system. _BioScience_ 73, 748–757 82. Zenni, R.D. _et al._ (2023) Multi-lingual literature searches are needed to unveil global knowledge. _J. Appl. Ecol._ 60, 380–383 83. Christie, A.P. _et al._ (2021) The challenge of biased evidence in conservation. _Conserv. Biol._ 35, 249–262 70. Leclère, D. _et al._ (2020) Bending the curve of terrestrial biodiversity needs an integrated strategy. _Nature_ 585, 551–556 84. Chowdhury, S. _et al._ (2022) Growth of non-English-language literature on biodiversity conservation. _Conserv. Biol. J. Soc. Conserv. Biol._ 36, e13883 71. Newbold, T. _et al._ (2020) Non-linear changes in modelled terrestrial ecosystems subjected to perturbations. _Sci. Rep._ 10, 14051 85. Thirunavukarasu, A.J. _et al._ (2023) Large language models in medicine. _Nat. Med._ 29, 1930–1940 72. Díaz, S.M. _et al._ (2019) _The Global Assessment Report on Biodiversity and Ecosystem Services: Summary for Policy Makers_, Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services 86. Tyler, C. _et al._ (2023) AI tools as science policy advisers? The potential and the pitfalls. _Nature_ 622, 27–30 87. Duffy, R. _et al._ (2019) Why we must question the militarisation of conservation. _Biol. Conserv._ 232, 66–73 73. Tzachor, A. _et al._ (2023) Digital twins: a stepping stone to achieve ocean sustainability? _NPJ Ocean Sustain._ 2, 16 88. Debnath, R. _et al._ (2023) Harnessing human and machine intelligence for planetary-level climate action. _NPJ Clim. Action_ 2, 20 74. Keith, D.A. _et al._ (2022) A function-based typology for Earth’s ecosystems. _Nature_ 610, 513–518 89. Challender, D.W.S. _et al._ (2022) Mischaracterizing wildlife trade and its impacts may mislead policy processes. _Conserv. Lett._ 15, e12832 75. Kochkov, D. _et al._ (2024) Neural general circulation models for weather and climate. _Nature_ 632, 1060–1066 76. Maleki, S. _et al._ (2020) Wetland restoration prioritization using artificial neural networks. _Wetlands_ 40, 179–192 90. Danielsen, F. _et al._ (2021) The concept, practice, application, and results of locally based monitoring of the environment. _BioScience_ 71, 484–502 77. Ball, J.G.C. _et al._ (2022) Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation. _Methods Ecol. Evol._ 13, 2622–2634 91. Danziger, S. _et al._ (2011) Extraneous factors in judicial decisions. _Proc. Natl. Acad. Sci._ 108, 6889–6892 78. Potapov, P. _et al._ (2022) Global maps of cropland extent and change show accelerated cropland expansion in the twenty- first century. _Nat. Food_ 3, 19–28 92. R Core Team (2018) _R: A Language and Environment for Statistical Computing_, R Foundation for Statistical Computing"
]
}