A Review and Categorization of Artificial Intelligence-Based Opportunities in Wildlife, Ocean and Land Conservation

Sustainability, Journal Year: 2022, Volume and Issue: 14(4), P. 1979 - 1979

Published: Feb. 9, 2022

The scholarly literature on the links between Artificial Intelligence and United Nations’ Sustainable Development Goals is burgeoning as climate change biotic crisis leading to mass extinction of species are raising concerns across globe. With a focus 14 (Life below Water) 15 Land), this paper explores opportunities applications in various domains wildlife, ocean land conservation. For purpose, we develop conceptual framework basis comprehensive review examples Intelligence-based approaches protect endangered species, monitor predict animal behavior patterns, track illegal or unsustainable wildlife trade. Our findings provide scholars, governments, environmental organizations, entrepreneurs with much-needed taxonomy real-life for tackling grand challenge rapidly decreasing biological diversity, which has severe implications global food security, nature, humanity.

Language: Английский

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Operationalizing Digitainability: Encouraging Mindfulness to Harness the Power of Digitalization for Sustainable Development

Sustainability, Journal Year: 2023, Volume and Issue: 15(8), P. 6844 - 6844

Published: April 18, 2023

Digitalization is globally transforming the world with profound implications. It has enormous potential to foster progress toward sustainability. However, in its current form, digitalization also continues enable and encourage practices numerous unsustainable impacts affecting our environment, ingraining inequality, degrading quality of life. There an urgent need identify such multifaceted holistically. Impact assessment digital interventions (DIs) leading essential specifically for Sustainable Development Goals (SDGs). Action required understand pursuit short-term gains achieving long-term value-driven sustainable development. We impact DIs on various actors diverse contexts. A holistic understanding will help us align visions development measures mitigate negative short impacts. The recently developed digitainability framework (DAF) unveils in-depth context-aware offers evidence-based profile SDGs at indicator level. This paper demonstrates how DAF can be instrumental guiding participatory action implementation practices. summarizes insights during Digitainable Spring School 2022 (DSS) “Sustainability Artificial Intelligence,” one whose goals was operationalize as a tool process collaboration active involvement professionals field guides formulation given DI. An evaluation within protocol benchmarks specific DI’s against SDG indicators framework. participating experts worked together DI gather analyze evidence by operationalizing DAF. four identified are follows: smart home technology (SHT) energy efficiency, blockchain food security, artificial intelligence (AI) land use cover change (LUCC), Big Data international law. Each expert groups addresses different using techniques data related criteria indicators. knowledge presented here could increase challenges opportunities provide structure developing implementing robust data-driven insights.

Language: Английский

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A framework to integrate innovations in invasion science for proactive management

Biological reviews/Biological reviews of the Cambridge Philosophical Society, Journal Year: 2022, Volume and Issue: 97(4), P. 1712 - 1735

Published: April 22, 2022

ABSTRACT Invasive alien species (IAS) are a rising threat to biodiversity, national security, and regional economies, with impacts in the hundreds of billions U.S. dollars annually. Proactive or predictive approaches guided by scientific knowledge essential keeping pace growing invasions under climate change. Although rapid development diverse technologies has produced tools potential greatly accelerate invasion research management, innovation far outpaced implementation coordination. Technological methodological syntheses urgently needed close gap facilitate interdisciplinary collaboration synergy among evolving disciplines. A broad review is necessary demonstrate utility relevance work fields generate actionable science for ongoing crisis. Here, we such advances relevant including remote sensing, epidemiology, big data analytics, environmental DNA (eDNA) sampling, genomics, others, present generalized framework distilling existing emerging into products proactive IAS management. This integrated workflow provides pathway scientists practitioners disciplines contribute applied biology coordinated, synergistic, scalable manner.

Language: Английский

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Machine Learning Models and Applications for Early Detection

Sensors, Journal Year: 2024, Volume and Issue: 24(14), P. 4678 - 4678

Published: July 18, 2024

From the various perspectives of machine learning (ML) and multiple models used in this discipline, there is an approach aimed at training for early detection (ED) anomalies. The anomalies crucial areas knowledge since identifying classifying them allows decision making provides a better response to mitigate negative effects caused by late any system. This article presents literature review examine which (MLMs) operate with focus on ED multidisciplinary manner and, specifically, how these work field fraud detection. A variety were found, including Logistic Regression (LR), Support Vector Machines (SVMs), trees (DTs), Random Forests (RFs), naive Bayesian classifier (NB), K-Nearest Neighbors (KNNs), artificial neural networks (ANNs), Extreme Gradient Boosting (XGB), among others. It was identified that MLMs as isolated models, categorized Single Base Models (SBMs) Stacking Ensemble (SEMs). under SBMs' SEMs' implementation achieved accuracies greater than 80% 90%, respectively. In detection, 90% reported authors. concludes applications, fraud, offer viable way identify classify robustly, high degree accuracy precision. are useful they can quickly process large amounts data detect suspicious transactions or activities, helping prevent financial losses.

Language: Английский

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Advances in remote sensing of freshwater fish habitat: A systematic review to identify current approaches, strengths and challenges

Fish and Fisheries, Journal Year: 2023, Volume and Issue: 24(5), P. 829 - 847

Published: June 24, 2023

Abstract Remote sensing technology offers the ability to derive information on freshwater fish habitats across broad geographic areas and has potential transform approaches monitoring. However, numerous platforms, sensors analytical software that are available may overwhelm those interested in utilizing this important thus limit its application uptake. Our review is intended shed light capacity of habitat monitoring by examining fundamental characteristics major remote technologies have been used for characterizing habitats, conducting a systematic literature studies characterize and, highlighting some key features, species regions, examined. Lastly, we identify relative strengths weaknesses various can be used, recommend future research could help improve use these technologies, provide series considerations who characterization.

Language: Английский

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Machine Learning Models and Applications for Early Detection

Published: June 27, 2024

From the various perspectives of Machine Learning (ML) and multiple models used in this discipline, there is an approach aimed at training for Early Detection (ED) anoma-lies. The early detection anomalies crucial areas knowledge since identifying classifying them allows decision-making provides a better response to mitigate negative effects caused by late any system. This article presents literature review examine which machine learning (MLM) operate with focus on ED multidisci-plinary manner specifically how these work field fraud detection. A variety were found, including Logistic Regression (LR), Support Vector Machines (SVM), De-cision Trees (DT), Random Forests (RF), Naive Bayesian Classifier (NB), K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Extreme Gradient Boosting (XGB), among others. It was identified that MLMs as isolated models, categorized Single Base Models (SBM) Stacking Ensemble (SEM). under SBM SEM implementation achieved accuracies greater than 80% 90%, respectively. n detection, 90% reported authors. concludes applications, fraud, offer viable way identify classify robustly, high degree accuracy precision. are useful they can quickly process large amounts data detect suspicious transactions or activities, helping prevent financial losses.

Language: Английский

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Hybridization in the Anthropocene – how pollution and climate change disrupt mate selection in freshwater fish

Biological reviews/Biological reviews of the Cambridge Philosophical Society, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 2, 2024

ABSTRACT Chemical pollutants and/or climate change have the potential to break down reproductive barriers between species and facilitate hybridization. Hybrid zones may arise in response environmental gradients secondary contact formerly allopatric populations, or due introduction of non‐native species. In freshwater ecosystems, field observations indicate that changes water quality chemistry, pollution change, are correlated with an increased frequency Physical chemical disturbances can alter sensory environment, thereby affecting visual communication among fish. Moreover, multiple compounds (e.g. pharmaceuticals, metals, pesticides, industrial contaminants) impair fish physiology, potentially phenotypic traits relevant for mate selection pheromone production, courtship, coloration). Although warming waters led documented range shifts, is ubiquitous few studies tested hypotheses about how these stressors hybridization what this means biodiversity conservation. Through a systematic literature review across disciplines (i.e. ecotoxicology evolutionary biology), we evaluate biological interactions, toxic mechanisms, roles physical change) disrupting preferences inducing interspecific Our study indicates change‐driven impact crucial choice thus could fishes ecosystems. To inform future conservation management, emphasize importance further research identify choice, understand mechanisms behind determine concentrations at which they occur, assess their on individuals, species, diversity Anthropocene.

Language: Английский

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Automated Detection of Koalas with Deep Learning Ensembles

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(10), P. 2432 - 2432

Published: May 19, 2022

Effective management of threatened and invasive species requires regular reliable population estimates. Drones are increasingly utilised by ecologists for this purpose as they relatively inexpensive. They enable larger areas to be surveyed than traditional methods many species, particularly cryptic such koalas, with less disturbance. The development robust accurate detection is required effectively use the large volumes data generated survey method. enhanced predictive computational power deep learning ensembles represents a considerable opportunity ecological community. In study, we investigate potential built from multiple convolutional neural networks (CNNs) detect koalas low-altitude, drone-derived thermal data. approach uses detectors combinations YOLOv5 models Detectron2. achieved strong balance between probability precision when tested on ground-truth radio-collared koalas. Our results also showed that greater diversity in ensemble composition can enhance overall performance. We found main impediment higher was false positives but expect these will continue reduce tools geolocating detections improved. ability construct different sizes allow improved alignment algorithms used characteristics problems. Ensembles efficient scaled suit settings, platforms hardware availability, making them capable adaption novel applications.

Language: Английский

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ARAapp: filling gaps in the ecological knowledge of spiders using an automated and dynamic approach to analyze systematically collected community data

Database, Journal Year: 2024, Volume and Issue: 2024

Published: Jan. 1, 2024

The ARAMOB data repository compiles meticulously curated spider community datasets from systematical collections, ensuring a high standard of quality. These are enriched with crucial methodological that enable the to be aligned in time and space, facilitating synthesis across studies, respectively, collections. To streamline analysis these species-specific context, suite tailored ecological tools named ARAapp has been developed. By harnessing capabilities ARAapp, users can systematically evaluate species housed within repository, elucidating intricate relationships range parameters such as vertical stratification, habitat occurrence, niche (moisture shading) phenological patterns. Database URL: is available at www.aramob.de/en.

Language: Английский

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Comparação e avaliação de técnicas de aprendizado de máquina para modelagem de distribuição de espécies na região da Bacia Amazônica.

Published: May 8, 2024

Uma das técnicas mais utilizadas para o monitoramento da biodiversidade é a Modelagem de Distribuição Espécies.Através dela, possível identificar as variáveis que influenciam na ocorrência uma espécie e seu nicho ecológico.Com desenvolvimento modelos Aprendizado Máquina apresentam acurácia elevada, essa abordagem passou ser amplamente adotada.Entretanto, existem desafios relacionados à aplicação dessas por conta incertezas relacionadas classe negativa, do desbalanceamento entre classes

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