Spatially balanced sampling methods are always more precise than random ones for estimating the size of aggregated populations

Perret, None Jan; Charpentier, Anne; Pradel, Roger; Papuga, Guillaume; Besnard, Aurélien

doi:10.1111/2041-210x.14015

Cited by 8 publications

(6 citation statements)

References 84 publications

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“…In either problem, all the sampling designs were thus expected to converge towards very similar performance as a s increased, and thus be a Pareto-optimal strategy. Regarding RRMSE( ν ), this phenomenon of convergence among designs had already been shown on simulations in a previous study (Perret et al, 2022).…”

Section: Resultssupporting

confidence: 71%

“…Regarding RRMSE(ν), this phenomenon of convergence among designs had already been shown on simulations in a previous study (Perret et al, 2022).…”

Section: Theoretical Analysis Of Asymptotic Errorssupporting

confidence: 69%

“…Regarding RRMSE(ν), this phenomenon of convergence among designs had already been shown on simulations in a previous study (Perret et al, 2022). The end of lines materialized with a square corresponds to the most regular design (x = 1 for fractal designs; p = 0 for hybrid designs).…”

Section: Theoretical Analysis Of Asymptotic Errorssupporting

confidence: 61%

“…This finding was robust to the effect considered, leading to the general guideline that grid designs should be favoured when the autocorrelation range of target response variable is larger than the feasible grid mesh size. In line with this advice, a recent simulation study (Perret et al, 2022) focusing on the problem of estimating the mean density of a population (analogous to v in problem 1 here) showed that when grid mesh size is equal to or moderately lower than the autocorrelation range of the density, switching to a random design generated the highest relative increase in error.…”

Section: Discussionmentioning

confidence: 64%

“…Many virtual studies have focused on evaluating the potential of various sampling strategies to estimate the mean population density of a species (e.g. 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.…”

Section: Introductionmentioning

confidence: 99%

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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: Resultssupporting

confidence: 71%

“…Regarding RRMSE(ν), this phenomenon of convergence among designs had already been shown on simulations in a previous study (Perret et al, 2022).…”

Section: Theoretical Analysis Of Asymptotic Errorssupporting

confidence: 69%

Section: Theoretical Analysis Of Asymptotic Errorssupporting

confidence: 61%

Section: Discussionmentioning

confidence: 64%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Laroche

2022

Preprint

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Estimating distribution and abundance of wide‐ranging species with integrated spatial models: Opportunities revealed by the first wolf assessment in south‐central Italy

Gervasi,

Aragno,

Salvatori

et al. 2024

Ecology and Evolution

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Estimating demographic parameters for wide‐ranging and elusive species living at low density is challenging, especially at the scale of an entire country. To produce wolf distribution and abundance estimates for the whole south‐central portion of the Italian wolf population, we developed an integrated spatial model, based on the data collected during a 7‐month sampling campaign in 2020–2021. Data collection comprised an extensive survey of wolf presence signs, and an intensive survey in 13 sampling areas, aimed at collecting non‐invasive genetic samples (NGS). The model comprised (i) a single‐season, multiple data‐source, multi‐event occupancy model and (ii) a spatially explicit capture‐recapture model. The information about species' absence was used to inform local density estimates. We also performed a simulation‐based assessment, to estimate the best conditions for optimizing sub‐sampling and population modelling in the future. The integrated spatial model estimated that 74.2% of the study area in south‐central Italy (95% CIs = 70.5% to 77.9%) was occupied by wolves, for a total extent of the wolf distribution of 108,534 km2 (95% CIs = 103,200 to 114,000). The estimate of total population size for the Apennine wolf population was of 2557 individuals (SD = 171.5; 95% CIs = 2127 to 2844). Simulations suggested that the integrated spatial model was associated with an average tendency to slightly underestimate population size. Also, the main contribution of the integrated approach was to increase precision in the abundance estimates, whereas it did not affect accuracy significantly. In the future, the area subject to NGS should be increased to at least 30%, while at least a similar proportion should be sampled for presence‐absence data, to further improve the accuracy of population size estimates and avoid the risk of underestimation. This approach could be applied to other wide‐ranging species and in other geographical areas, but specific a priori evaluations of model requirements and expected performance should be made.

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Stand-level sampling designs for bark stripping caused by red deer (Cervus elaphus L.): simulation studies based on nine fully censused stands

Hahn,

Vospernik

2024

Eur J Forest Res

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Precise assessment of bark stripping damage is of high economic importance, since bark stripping makes wood unusable for saw timber and it is important for compensation payments for game damage. Bark stripping is clustered and decreases with increasing tree diameter, so that common forest inventories, optimized for assessing timber production variables such as standing timber volume, do not provide adequately precise estimates of bark stripping damage. In this study we analysed different sampling designs (random sampling, systematic sampling), tree selection methods (fixed radius plot, angle count sampling) and number of plots and plot sizes (plot radius: 2–20 m; basal area factor: 1–6m2/ha) for bark stripping assessment. The analysis is based on simulation studies in 9 fully censused stands (9026 trees). Simulations were done for actually assessed damage and randomly distributed damage and each scenario was repeated 100 times with different random points or different random grid locations. Systematic sampling was considerably more precise than random sampling in both scenarios. Sampling intensities to attain a standard error of 10% ranged between 12 and 18% dependent on the plot size. For a given sampling intensity, precision increased with decreasing plot size or increasing basal area factor. This implies, however, a large number of plots to be measured, which is expensive, when travel costs are high. Differences between tree selection by fixed radius plots or angle count sampling were minor. For bark stripping damage, we recommend sampling with fixed radius plots with a radius of 4–6 m and the measurement of approximately 230 or 150 plots, respectively.

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Spatially balanced sampling methods are always more precise than random ones for estimating the size of aggregated populations

Cited by 8 publications

References 84 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 ?

Estimating distribution and abundance of wide‐ranging species with integrated spatial models: Opportunities revealed by the first wolf assessment in south‐central Italy

Stand-level sampling designs for bark stripping caused by red deer (Cervus elaphus L.): simulation studies based on nine fully censused stands

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