Efficiency of spatial sampling designs in estimating abundance and species richness of carabids at the landscape level

Thiele, Jan; Erley, Gunda Schulte auf’m; Glemnitz, Michael; Gabriel, Doreen

doi:10.1007/s10980-023-01605-1

Cited by 4 publications

(1 citation statement)

References 37 publications

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“…Perret et al (2022)), the total abundance or the total species richness of a taxon (e.g. Thiele et al (2023)) within a sampling area. However, only few virtual studies focused on efficient designs to accurately estimate the autocorrelation range of biodiversity variables.…”

Section: Introductionmentioning

confidence: 99%

Efficient sampling designs to assess biodiversity spatial autocorrelation : should we go fractal ?

Laroche

2022

Preprint

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Evaluating the autocorrelation range of species distribution in space is necessary for many applied ecological questions like implementing protected area networks or monitoring programs. The autocorrelation range can be inferred from observations, based on a spatial sampling design. However, there is a trade-off between estimating the autocorrelation range of a species distribution and estimating fixed effects affecting the mean species abundance or occupancy among sites. The random sampling design is considered as a good heuristic to estimate autocorrelation range, for it contains contrasted pairwise distances that cover a wide array of possible range values. The grid design is viewed as a better choice for estimating fixed effects, for it eliminates small pairwise distances that are more prone to pseudo-replication. Mixing random and grid (`hybrid' designs) has been presented as a way to navigate between both conflicting objectives. We postulated that fractal designs --- which have a self-similarity property and well-identified scales --- could make a compromise, for they preserve some regularity reminiscent of grid at each scale, but also browse a wide array of possible autocorrelation range values across scales. We used maximum likelihood estimation within an optimal design of experiments approach to compare the accuracy of hybrid and fractal designs at estimating the fixed intercept and the autocorrelation range of a spatial field of values. We found that hybrid designs were Pareto-optimal intermediary strategies between grid and random for small autocorrelation range values only, while classic grid design should always be preferred when autocorrelation is large. Fractal designs yielded Pareto-optimal stra\-tegies specifically good at estimating small autocorrelation ranges. However, they were generally not Pareto-optimal for higher values of autocorrelation range. At last, when the surveyed area could be changed, random designs were sufficient to reach the Pareto front in any context. Fractal designs seemed relevant when specifically aiming at improving the estimation of small autocorrelation ranges in a fixed surveyed area with a limited sampling budget, which is a quite circumscribed scenario. However, they may prove more clearly advantageous to analyse biodiversity patterns when covariates are included in the analysis and ecological processes differ among spatial scales.

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Section: Introductionmentioning

confidence: 99%

Efficient sampling designs to assess biodiversity spatial autocorrelation : should we go fractal ?

Laroche

2022

Preprint

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Quantifying agricultural land-use intensity for spatial biodiversity modelling: implications of different metrics and spatial aggregation methods

Roilo,

Paulus,

Alarcón-Segura

et al. 2024

Landsc Ecol

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Context Agricultural intensification is a major driver of farmland biodiversity declines. However, the relationship between land-use intensity (LUI) and biodiversity is complex and difficult to characterise, not least because of the difficulties in accurately quantifying LUI across heterogeneous agricultural regions. Objectives We investigated how the use of different LUI metrics and spatial aggregation methods can lead to large variations in LUI estimation across space and thus affect biodiversity modelling. Methods We used three spatial aggregation methods (square, hexagonal, and voronoi grids) to calculate ten commonly used LUI metrics describing three LUI dimensions: land use, land management and landscape structure. Using a virtual species approach, we compared how LUI values sampled at biodiversity monitoring sites vary across different metrics and grids. We modelled the distribution of three virtual species using Generalised Additive Models to test how omitting certain LUI dimensions from the models affected the model results. Results The density distributions of LUI values at the presence points of the virtual species were significantly different across metrics and grids. The predefined species-environment relationships characterising the environmental niches of two out of three virtual species remained undetected in models that omitted certain LUI dimensions. Conclusions We encourage researchers to consider the implications of using alternative grid types in biodiversity models, and to account for multiple LUI dimensions, for a more complete representation of LUI. Advances in remote sensing-derived products and increased accessibility to datasets on farm structure, land-use and management can greatly advance our understanding of LUI effects on biodiversity.

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Assessing the relationship between urban park spatial features and physical activity levels in Residents: A spatial analysis Utilizing drone remote sensing

Zhang,

Cao,

Wang

et al. 2024

Ecological Indicators

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Efficiency of spatial sampling designs in estimating abundance and species richness of carabids at the landscape level

Cited by 4 publications

References 37 publications

Efficient sampling designs to assess biodiversity spatial autocorrelation : should we go fractal ?

Efficient sampling designs to assess biodiversity spatial autocorrelation : should we go fractal ?

Quantifying agricultural land-use intensity for spatial biodiversity modelling: implications of different metrics and spatial aggregation methods

Assessing the relationship between urban park spatial features and physical activity levels in Residents: A spatial analysis Utilizing drone remote sensing

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