A probabilistic sampling strategy for estimating plant density in Posidonia oceanica meadows

Abstract

Marine and coastal ecosystems, such as seagrasses, mangroves, and coral reefs, provide a range of essential provisioning, regulating and cultural ecosystem services. Recent United Nations guidelines on ecosystem accounting (SEEA EA) emphasise the need for biophysical data as the foundation for compiling ecosystem accounts and conducting economic evaluations for developing indicators and informing policies and interventions. However, data availability on marine ecosystems is limited with respect to terrestrial ones. Moreover, the collection of biophysical data on marine ecosystem extent and condition required for ecosystem accounting (EA) is often not aligned with existing habitat monitoring strategies. This study aims to address the scarcity of spatial data on marine ecosystems and facilitate the integration of current monitoring strategies with the scope of EA. We propose the application of design-based inference for the estimation, mapping, and monitoring of key ecological attributes of marine ecosystems. We focus on the habitat of Posidonia oceanica, an endemic seagrass of the Mediterranean Sea, but the proposed strategy is adaptable to other ecosystems. The benefits of appropriate probabilistic sampling schemes for assessing P. oceanica are explored via simulation testing. The performance of different sample schemes in artificial populations reveals that reliable estimates of density (as well as their precision) can be obtained even with low sample sizes. The empirical viability of our methodology is exemplified using data collected on a meadow located in an Italian Marine Protected Area (Puglia region, Southern Italy).

Keywords: Posidonia oceanica; Design-based inference; Ecosystem accounting; Simulation study.

Fig. 1

Population 1. The 87.5 million points uniformly distributed over the rectangular area of size 35 hectares

Fig. 2

Population 2. The 87.5 million points distributed according to a trended spatial pattern over the rectangular area of size 35 hectares

Fig. 3

Population 3. The 87.5 million points distributed according to a clustered spatial pattern over the rectangular area of size 35 hectares

Fig. 4

Population 4. The 87.5 million points distributed according to a striped spatial pattern over the rectangular area of size 35 hectares

Figure 5

a Population 1. Density map of the regular population. b Population 1. Zoomed density map in a 20 × 20 m quadrat within the rectangle

Figure 6

a Population 2. Density map of the trended population. b Population 2. Zoomed density map in a 20 × 20 m quadrat within the rectangle

Figure 7

a Population 3. Density map of the clustered population. b Population 3. Zoomed density map in a 20 × 20 m quadrat within the rectangle

Figure 8

a Population 1. Density map of the stripped population. b Population 4. Zoomed density map in a 20 × 20 m quadrat within the rectangle

Fig. 9

Graphical representation of the P. oceanica meadow selected for the case study in the Tremiti Islands marine protected area. The meadow, located off the coast of San Domino Island in the Tremiti Archipelago, evidenced by a striped pattern. Its shapefile was extracted from the dataset of Telesca et al. (2015). The inset map in the lower right corner shows the location of the marine protected area, in Southern Italy

Figure 10

a Interpolated map of density achieved by means of NN interpolation of 27 density values recorded at locations supposed to be selected by URS. Blue areas indicate a high value of shoot density (number of shoots/m2), while light green and yellow colours represent lower density. b Bootstrap root mean squared error map from NN interpolation. c Interpolated map of density achieved by means of IDW interpolation of 27 density values recorded at locations supposed to be selected by URS. Blue areas indicate a high value of shoot density (number of shoots/m2), while light green and yellow colours represent lower density. d Bootstrap root mean squared error map from IDW interpolation

Figure 11

a Interpolated change density map by means of NN interpolator. Blue colour indicates that the shoot density (number of shoots/m²) increased from 2015 to 2020, while orange areas are characterised by a density decrease in the same period. b Interpolated change density map by means of IDW interpolator. Blue colour indicates that the shoot density (number of shoots/m²) increased from 2015 to 2020, while orange areas are characterised by a density decrease in the same period