Optimizing sampling effort and information content of biodiversity surveys: a case study of alpine grassland

Highlights

• The optimal sampling design provides maximal information via minimal effort.

• The optimal size/number of sampling quadrats cannot be generalized for alpine grassland.

• Our approach is applicable to other study systems.

Abstract

Aims

Current rates of biodiversity loss do not allow for inefficient monitoring. Optimized monitoring maximizes the ratio between information and sampling effort (i.e., time and costs). Sampling effort increases with the number and size of sampling units. We hypothesize that an optimal size and number of sampling units can be determined providing maximal information via minimal effort. We apply an approach that identifies the optimal size and number of sampling quadrats. The approach can be adapted to any study system. Here we focus on alpine grassland, a diverse but threatened ecosystem.

Location

Gran Paradiso National Park, Italy.

Methods

We sampled nine 20 m × 20 m-plots. Each plot consisted of 100 2 m × 2 m-subplots. Species richness and Shannon diversity were quantified for different sizes and quantities of subplots. We simulated larger subplot sizes by unifying adjacent 2 m × 2 m-subplots. Shannon’s information entropy was used to quantify information content among richness and diversity values resulting from different subplot sizes and quantities. The optimal size and number of subplots is the lowest size and number of subplots returning maximal information. This optimal subplot size and number was determined by Mood’s median test and segmented linear regression, respectively.

Results

The information content among richness values increased with subplot size, irrespective of the number of subplots. Therefore, the largest subplot size available is the optimal size for information about richness. Information content among diversity values increased with subplot size if 18 or less subplots were considered, and decreased if at least 27 subplots were sampled. The subplot quantity consequently determined whether the smallest or largest subplot size available is the optimal size, and whether the optimal size can be generalized across richness and diversity. Given a 2 m × 2 m size, we estimated an optimal quantity of 54. Given a size of 4 m × 4 m, we estimated an optimal number of 36. The optimal number of plots can be generalized across both indices because it barely differed between the indices given a fixed subplot size.

Conclusions

The information content among richness and diversity values depends on the sampling scale. Shannon’s information entropy can be used to identify the optimal number and size of plots that return most information with least sampling effort. Our approach can be adapted to other study systems to create an efficient in-situ sampling design, which improves biodiversity monitoring and conservation under rapid environmental change.