| Popis: |
Quantifying the autocorrelation range of a species distribution in space is necessary for applied ecological questions like implementing protected area networks or monitoring programs. However, building a spatial sampling design to accurately estimate this range can conflict with other objectives such as estimating environmental effects acting upon species distribution. Mixing random sampling points and systematic grid (‘hybrid’ designs) may allow navigating between conflicting objectives: random sampling points induce contrasted pairwise distances that cover a wide array of possible autocorrelation range values while the grid limit small pairwise distances prone to pseudo-replication, thus improving the estimation of effects. Fractal designs (i.e. self-similar designs with well-identified scales) could also make a compromise by preserving some regularity reminiscent of grid at each scale but also browsing a wide array of possible autocorrelation range values across scales. Using maximum likelihood estimation in an optimal design of experiments approach, we compared errors of hybrid and fractal designs when estimating an effect acting upon a target spatial variable along with residual autocorrelation range. We found that Pareto-optimal sampling strategies depended on the feasible grid mesh size over the study area given the sampling budget. When the autocorrelation range was larger than the feasible mesh size, grid design was the best option on all criteria. When autocorrelation range was shorter, the choice of designs depended on the target effect. For effects driven by a monotonic environmental gradients across space, fractal designs outperformed hybrid designs on all criteria. For effects of non-linear covariates or simply the global mean of target variable over the study area, hybrid designs were more efficient. Therefore, a narrow niche for fractal designs exists. The interest of designs with a clear hierarchical structure like fractals may stand out more clearly when studying biological patterns with contrasted spatial structures across scales. |
