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Ecological monitoring programs depend on the robust estimation of descriptive parameters. Percent cover, gleaned from transects sampled with video imagery, is a popular benthic ecology descriptor often estimated using point counting, an image-based method for identifying substrate types beneath random points. We tested the hypothesis that the number of points needed to robustly estimate benthic cover in video imagery transects depends on cover itself, predicting that lower cover will require more points/frame to be accurately estimated. While this point may seem obvious to the statistically inclined, the justification of point density has been largely ignored in the literature. We examined the statistical behavior of point count estimates using computer-simulated 20 m-long transects patterned after data from a Bahamian reef. The minimum number of points necessary to insure accurate percent cover estimation, the Optimal Point Count (OPC), is a function of mean percent cover and spatial heterogeneity of the benthic community. More points are required to characterize reefs with lower cover and more homogeneously distributed coral colonies. These results show that careful consideration must be given to sampling design and data analysis prior to attempting to estimate benthic cover, especially in the context of long-term monitoring of degrading coral reef ecosystems.

A common problem while working in ecological characterization and monitoring programs is how to effectively test and optimize methods and experimental designs. Live percent cover is a widely used ecological descriptor in marine conservation biology and large-scale monitoring projects (e.g., [

The number of points to be used per unit area (point density and the unit area being an image frame from a video transect) is crucial to obtaining a robust estimate of percent cover, and initial statistical tests should be performed to establish the point density that will provide adequate precision and accuracy while maximizing efficiency (the time spent identifying substrate types underlying points). Such tests include power analysis [

While the need for determining adequate point density is mentioned in coral reef monitoring manuals (e.g., [

In addition to bias introduced by poorly calibrated sampling efforts, the accuracy of cover estimates could significantly decrease as reef structure and cover levels change over time. In this case, even if resource managers assess the statistical rigor at the beginning of a long-term monitoring program, nothing guaranties that cover estimates will be bias-free over multiyear surveys. To our knowledge, the behavior of point count estimates relative to varying percent cover has not been addressed, although it is a central question given the ongoing global decline of coral reefs. If the number of random points used for accurately estimating cover is a function of cover itself, the optimal number of points (Optimal Point Count, OPC) sufficient for robust cover estimates for high-cover reefs is likely to increase as cover decreases. In this communication, we test this idea using computer-generated video transects that allowed us to directly compare true (simulated) cover and its estimation by point count. We are not trying to review coral monitoring sampling design; rather, we recognize a severe problem with one specific aspect of video transect sampling using point counting and address it with simulations.

To assess the statistical robustness of the point count method, we used the R environment [^{2}/frame; 10 m^{2}/transect).

Our simulation engine was initialized by setting a target mean percent cover (

For each simulated transect, cover was estimated using the point count method, by incrementing a “cover” counter if a random point coincided with an area of simulated cover. Random points were generated with the “splancs” package [

Estimations of computer-simulated cover were qualitatively compared to estimations from human generated data (ground-truth data) to assess realism of the computer model. We judged our simulation procedure to be realistic, as estimations of ground-truth cover and simulated cover were similar (Figure

Comparison between ground-truth (a) and simulated (b) estimates of mean percent cover (±SE) using different numbers of points per frame. Ground-truth data are sponge cover estimates from a 20-m long transect video recorded at Rainbow Gardens Reef in 2004. Simulated sponge cover estimates are based on field estimates of

The efficacy of the point count method was also tested by quantifying bias (the absolute difference between a parameter and its estimate) for the mean and the standard deviation of percent cover over simulated transects. Bias was calculated using the values of

Estimated and simulated parameter values converged as the number of points per frame increased (Figure

Optimal point count (OPC, defined in the methods) for different mean percent cover values (

5 | >600 | 260 | 137 |

10 | 382 | 98 | 62 |

20 | 174 | 46 | 26 |

30 | 97 | 22 | 13 |

Performance of point count estimation of a transect mean percent cover ((a):

Box-and-whisker plots of

Bias of the estimated mean cover (a)–(c) and standard deviation of mean cover (d)–(f), based on 100 replicated simulations.

Reef ecologists are faced with the dilemma of having to capture the realities of complex and dynamic environments with statistical precision. The design of any ecosystem monitoring program must be based on an understanding of population dynamics and obey the statistical premise that the sampling regime reflects the true abundance of organisms. Coral reef ecologists adopted some of the general survey techniques from plant ecology owing to the structural similarities of forests and reefs (i.e., quadrats, line transects, and nearest neighbor analysis). Modifications occurred due to the need to optimize underwater working time and other aspects of working underwater. Line intercept transects were replaced by point intercept, still photography, and currently video.

We have chosen to address the issue of percent cover determination because it lies at the heart of the survey methodology. Obviously, the requirements for a long-term, health-status monitoring program demand more information than simply percent cover and therefore require more data than offered here for planning complex, community-oriented ecological surveys. As the number of benthic categories (e.g., coral species, substrate types, and health status, etc.) increases, obviously more attention must be applied to determining the OPC (particularly for detecting rare species). The project goal, spatiotemporal scope, and ecosystemic resolution should dictate the experimental design. Each project should undertake its own statistical design based on ecological sampling theory beginning with an overall assessment of how many stations, transects, quadrats (etc.) all the way down to how many categories and points/frame to be used. While all of these parameters influence the robustness and reliability of a sampling design, this communication recognizes a severe problem with one specific aspect of video transect sampling using point counting and has addressed it with simulations.

As ecosystems change, the optimal number of points (OPC) should be adjusted to maintain a consistent degree of accuracy. For example, when

Transect-level heterogeneity in cover strongly affected the OPC. At Rainbow Gardens Reef, scleractinian coral cover varied twice as much within transects as octocoral cover. Benthic types might therefore strongly influence point count calibration. Also, benthic heterogeneity might depend on reef geomorphology itself (e.g., [

The results of our study make it clear that OPC must be determined for a specific set of environmental circumstances. However, providing a simple rule of thumb to calculate OPC is difficult, because OPC depends on more factors than just reef cover and heterogeneity. OPC determination must be tailored to a specific nested design, to reach accuracy at the required spatial scale. In addition to the sampling variables listed above (e.g., quadrat size, transect length, and number of transects, etc.), the OPC will depend on the biologically meaningful difference one wishes to detect between treatment groups (i.e., the “effect size”). Our simulations could have therefore incorporated a plethora of variables. Instead, we chose to limit ourselves to a set of parameters that reflect a real situation (the study of Rainbow Gardens Reef, [

The authors thank Amélia Viricel, Paul Leberg, John Fauth, Judith Lang, and two anonymous reviewers for commenting on earlier drafts; Amélia Viricel and Catherine Booker for field help, and the personnel of the Caribbean Marine Research Center (Lee Stocking Island) for technical support. Work at Rainbow Gardens Reef was funded by a Grant from the Perry Institute for Marine Science and the National Oceanic and Atmospheric Administration (NOAA) Undersea Research Program (Project number CMRC-04-PRPD-04-04A) to PD and EP and by a grant from the Slocum Lunz Foundation and the College of Charleston to EP. Views expressed herein are those of the authors and do not necessarily reflect the views of PIMS, NOAA, or any of their subagencies. The authors thank the Department of Biology at the College of Charleston for covering page charges. This is Grice Marine Biological Laboratory contribution number 381.