Machine learning in landscape ecological analysis: a review of recent approaches

Landscape Ecology, Journal Year: 2021, Volume and Issue: 37(5), P. 1227 - 1250

Published: Dec. 1, 2021

Language: Английский

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Comparing the performance of global, geographically weighted and ecologically weighted species distribution models for Scottish wildcats using GLM and Random Forest predictive modeling

Ecological Modelling, Journal Year: 2024, Volume and Issue: 492, P. 110691 - 110691

Published: April 8, 2024

Species distribution modeling has emerged as a foundational method to predict occurrence and suitability of species in relation environmental variables advance ecological understanding guide conservation planning. Recent research, however, shown that species-environmental relationships habitat model predictions are often nonstationary space, time context. This calls into question approaches assume global, stationary realized niche use predictive describe it. paper explores this issue by comparing the performance models for wildcat hybrid based on (1) global pooled data across individuals, (2) geographically weighted aggregation individual models, (3) ecologically (4) combinations geographical weighting. Our study system included GPS telemetry from 14 hybrids Scotland. We developed both using Generalized Linear Models (GLM) Random Forest machine learning compare these differing algorithms how they analyses. validated predicted four different ways. First, we used independent hold-out collared hybrids. Second, 8 additional previous were not training sample. Third, sightings sent public researchers expert opinion. Fourth, collected camera trap surveys between 2012 – 2021 various sources produce combined dataset showing where wildcats had been detected. results show validation individuals train provides highly biased assessment true other locations, with particular appearing perform exceptionally (and inaccurately) well when same models. Very obtained three sources. Each sets gave result terms best overall model. The average datasets suggested produced potential was an ensemble Model GLM suggests debate over whether which vs is superior or aggregated may be false choice. presented here prediction applies combination all framework.

Language: Английский

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Modeling the effects of climate change scenarios on the potential distribution of Vespa crabro Linnaeus, 1758 (Hymenoptera: Vespidae) in a Mediterranean biodiversity hotspot

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103006 - 103006

Published: Jan. 1, 2025

Language: Английский

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Open-source, environmentally dynamic machine learning models demonstrate behavior-dependent utilization of mixed-use landscapes by jaguars (Panthera onca)

Biological Conservation, Journal Year: 2025, Volume and Issue: 302, P. 110978 - 110978

Published: Jan. 24, 2025

Language: Английский

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Background sampling for multi-scale ensemble habitat selection modeling: Does the number of points matter?

Ecological Informatics, Journal Year: 2022, Volume and Issue: 72, P. 101914 - 101914

Published: Nov. 13, 2022

Ensemble habitat selection modeling is becoming a popular approach among ecologists to answer different questions. Since we are still in the early stages of development and application ensemble modeling, there remain many questions regarding performance parameterization. One important gap, which this paper addresses, how number background points used train models influences model. We an empirical presence-only dataset three selections scale-optimized using six algorithms (GLM, GAM, MARS, ANN, Random Forest, MaxEnt). tested four combinations component models: (a) equal numbers presences, (b) equaled ten times (c) 10,000 points, (d) optimized for each Among regression-based approaches, MARS performed best when built with points. machine learning models, RF presences AUC indicated that performing model was including while TSS increased as increased. found trained optimal outperformed ensembles same although differences were slight. When single method, can perform better than model, but fluctuates not properly selected. On other hand, provides consistently high accuracy regardless point sampling approach. Further, optimizing within provide improvement. suggest evaluating more across multiple species investigate might affect scenarios.

Language: Английский

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Not seeing the forest for the trees: Generalised linear model out-performs random forest in species distribution modelling for Southeast Asian felids

Ecological Informatics, Journal Year: 2023, Volume and Issue: 75, P. 102026 - 102026

Published: Feb. 18, 2023

Species Distribution Models (SDMs) are a powerful tool to derive habitat suitability predictions relating species occurrence data with features. Two of the most frequently applied algorithms model species-habitat relationships Generalised Linear (GLM) and Random Forest (RF). The former is parametric regression providing functional models direct interpretability. latter machine learning non-parametric algorithm, more tolerant than other approaches in its assumptions, which has often been shown outperform algorithms. Other have developed produce robust SDMs, like training bootstrapping spatial scale optimisation. Using felid presence-absence from three study regions Southeast Asia (mainland, Borneo Sumatra), we tested performances SDMs by implementing four modelling frameworks: GLM RF bootstrapped non-bootstrapped data. With Mantel ANOVA tests explored how combinations influenced their predictive performances. Additionally, scale-optimisation responded species' size, taxonomic associations (species genus), area algorithm. We found that choice algorithm had strong effect determining differences between SDMs' predictions, while no effect. followed species, were main factors driving scales identified. trained showed higher performance, however, revealed significant only explaining variance observed sensitivity specificity and, when interacting bootstrapping, Percent Correctly Classified (PCC). Bootstrapping significantly explained specificity, PCC True Skills Statistics (TSS). Our results suggest there systematic identified produced vs. RF, but neither approach was consistently better other. divergent inconsistent abilities analysts should not assume inherently superior test multiple methods. implications for SDM development, revealing inconsistencies introduced on optimisation, selecting broader RF.

Language: Английский

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Exploring nonstationary limiting factors in species habitat relationships

Ecological Modelling, Journal Year: 2024, Volume and Issue: 490, P. 110663 - 110663

Published: Feb. 29, 2024

Species distribution modeling is widely used to quantify and predict species-environment relationships. Most past applications methods in species assume context independent stationary relationships between patterns of occurrence environmental variables. There has been relatively little research investigating dependence nonstationarity modeling. In this paper we explore spatially varying limiting factors using high resolution telemetry data from 14 individual wildcat hybrids distributed across geographical gradients Scotland. (1) We proposed that nonstationary would be indicated by significant association statistical measures variability predictors the predictive importance those (2) further most factor observed related spatial variation a lesser amount mean value variables within study sites. (3) Additionally, anticipated when there was relationship an its as predictor positive, such higher associated with variable (following theory factors). (4) Conversely, roughly evenly split positive negative relationships, given could become either they are highly abundant or value, rare low particular landscape, depending on nature for ecological variable. (5) Finally, hypothesized frequency supported differ among groups, were directly key resources more likely than have indirect impacts hybrid habitat selection foraging. Our results show assumptions global, associations not met many models, requiring explicit consideration scale paradigm. found both standard deviation strong whether will differentially important occurrence. confirmed it sampled data, abundant. The differed essential ecology

Language: Английский

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A multi-level, multi-scale comparison of LiDAR- and LANDSAT-based habitat selection models of Mexican spotted owls in a post-fire landscape

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103168 - 103168

Published: April 1, 2025

Language: Английский

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Pathwalker: A New Individual-Based Movement Model for Conservation Science and Connectivity Modelling

ISPRS International Journal of Geo-Information, Journal Year: 2022, Volume and Issue: 11(6), P. 329 - 329

Published: May 30, 2022

Understanding organism movement is at the heart of many ecological disciplines. The study landscape connectivity—the extent to which a facilitates movement—has grown become central focus spatial ecology and conservation science. Several computational algorithms have been developed model connectivity; however, major models in use today are limited by their lack flexibility simplistic assumptions behaviour. In this paper, we introduce new spatially-explicit, individual- process-based called Pathwalker, simulates connectivity through heterogeneous landscapes as function resistance, energetic cost movement, mortality risk, autocorrelation, directional bias towards destination, all multiple scales. We describe model’s structure parameters present statistical evaluations demonstrate influence these on resulting patterns. Written Python 3, Pathwalker works for any version 3 freely available download online. with greater compared dominant currently science, thereby, enabling more detailed predictions practice management. Moreover, provides highly capable simulation framework exploring theoretical methodological questions that cannot be addressed empirical data alone.

Language: Английский

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Towards an ecological mathematics

Interdisciplinary Science Reviews, Journal Year: 2024, Volume and Issue: 49(5), P. 476 - 497

Published: March 22, 2024

Mathematics plays a fundamental role in ecological research, yet its uses remain strikingly separate from advances the environmental social sciences and humanities. In this paper, I work to address impasse outline motivation scope for an ‘ecological mathematics’, approach doing mathematics research which foregrounds relationship, embodiment human difference. begin by tracing historical emergence of ecology, noting how life processes have been conceptualised way forces them fit ideals mathematical models transplanted physical sciences. then investigate cultural factors shaping evolution thought, eliciting malleability knowledge relates more-than-human world. This provides place rethink abstraction develop methods grounded concepts. Drawing on ethnographic perceptual accounts space time, with topological concepts both suggest new correspondence between these subjects, elaborating employing techniques enliven, rather than deaden, ecologies under study. The paper concludes important philosophical clarifications mathematics.

Language: Английский

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