Digital ecologies: Materialities, encounters, governance

Progress in Environmental Geography, Год журнала: 2022, Номер 2(1-2), С. 3 - 32

Опубликована: Дек. 26, 2022

Digital technologies increasingly mediate relations between humans and nonhumans in a range of contexts including environmental governance, surveillance, entertainment. Combining approaches from more-than-human digital geographies, we proffer ‘digital ecologies’ as an analytical framework for examining digitally-mediated human–nonhuman entanglement. We identify entanglement compelling basis which to articulate critique diverse situated contexts. Three questions guide this approach: What infrastructures give rise entanglement, with what material consequences? is at stake socially, politically, economically when encounters are digitised? And how enrolled programmes governance? develop our ecologies across three core conceptual themes wider interest geographers: (i) materialities, considering the enable connections their socioenvironmental impacts; (ii) encounters, political economic consequences convivial potentials digitising contact zones; (iii) questioning produce novel forms governance. affirm that mediations worlds can potentially cultivate environmentally progressive communities, just such note urgency these conversations.

Язык: Английский

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Connectivity modelling in conservation science: a comparative evaluation

Scientific Reports, Год журнала: 2022, Номер 12(1)

Опубликована: Окт. 6, 2022

Landscape connectivity, the extent to which a landscape facilitates flow of ecological processes such as organism movement, has grown become central focus applied ecology and conservation science. Several computational algorithms have been developed understand map many studies validated their predictions using empirical data. Yet at present, there is no published comparative analysis uses comprehensive simulation framework measure accuracy performance dominant methods in connectivity modelling. Given widespread usage models spatial science, thorough evaluation predictive abilities techniques essential for guiding appropriate effective application across different contexts. In this paper, we address by individual-based movement model Pathwalker simulate scenarios generated from wide range possible behaviours complexities. With simulated data, test three major models: factorial least-cost paths, resistant kernels, Circuitscape. Our study shows latter two these consistently perform most accurately nearly all cases, with varying substantially For majority applications, infer kernels be model, except when strongly directed towards known location. We conclude paper review interdisciplinary discussion current limitations future developments

Язык: Английский

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Improving landscape ecological network connectivity in urbanizing areas from dual dimensions of structure and function

Ecological Modelling, Год журнала: 2023, Номер 482, С. 110380 - 110380

Опубликована: Апрель 24, 2023

Язык: 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, Год журнала: 2024, Номер 492, С. 110691 - 110691

Опубликована: Апрель 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.

Язык: Английский

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A framework to quantitatively assess the influence of land use and land cover on coastal wetland hydrological connectivity from a landscape resistance perspective

The Science of The Total Environment, Год журнала: 2024, Номер 922, С. 171140 - 171140

Опубликована: Фев. 22, 2024

Язык: Английский

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Graph theory in ecological network analysis: A systematic review for connectivity assessment

Journal of Cleaner Production, Год журнала: 2024, Номер 472, С. 143504 - 143504

Опубликована: Авг. 30, 2024

Язык: Английский

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Exploring nonstationary limiting factors in species habitat relationships

Ecological Modelling, Год журнала: 2024, Номер 490, С. 110663 - 110663

Опубликована: Фев. 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

Язык: Английский

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A generalist species of highly specialized individuals?

Ecological Modelling, Год журнала: 2025, Номер 501, С. 111012 - 111012

Опубликована: Янв. 9, 2025

Язык: 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, Год журнала: 2025, Номер 302, С. 110978 - 110978

Опубликована: Янв. 24, 2025

Язык: Английский

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Accounting for spatiotemporal patterns of long‐term recursion in estimating local‐scale step selection

Methods in Ecology and Evolution, Год журнала: 2025, Номер unknown

Опубликована: Янв. 24, 2025

Abstract Step‐selection analysis (SSA) is ubiquitous for assessing local‐scale habitat selection by comparing relocations from telemetry data (used steps) to potential given the movement of animal (available steps). The case–control design SSA intended estimate at spatiotemporal scale these steps. However, long‐term behaviour associated with recurring use certain locations or resources may additionally impact decisions, potentially impacting estimates produced SSA. To determine recursive on local selection, we simulated trajectories in which animals exhibited patterns recursion. Based trajectories, evaluated step‐selection. Then, developed a new approach identify recursion points based time‐dependent kernel density including latitude, longitude and time day. this information, accounted relationship between available steps using adjust our sample Finally, apply response white‐tailed deer ( Odocoileus virginianus ) sources risk. Recursive resulted biased when previously established step‐selection methods. found that correction models were able produce accurate across differing scenarios. When applied an empirical set, methods revealed pattern avoidance human modified areas was masked previous approaches. Our results suggest have unappreciated effects Specifically, since used are specific places times, covariates must account variation spatial temporal behaviour. Unless properly for, will inhibit ability characterize fine‐scale behaviours such as predator foraging ecology.

Язык: Английский

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