Point-Combination Transect (PCT): Incorporation of small underwater cameras to study fish communities

Available underwater visual census (UVC) methods such as line transects or point count observations are widely used to obtain community data of underwater species assemblages, despite their known pit-falls. As interest in the community structure of aquatic life is growing, there is need for more standardized and replicable methods for acquiring underwater census data.Here, we propose a novel approach, Point-Combination Transect (PCT), which makes use of automated image recording by small digital cameras to eliminate observer and identification biases associated with available UVC methods. We conducted a pilot study at Lake Tanganyika, demonstrating the applicability of PCT on a taxonomically and phenotypically highly diverse assemblage of fishes, the Tanganyikan cichlid species-flock.We conducted 17 PCTs consisting of five GoPro cameras each and identified 22,867 individual cichlids belonging to 61 species on the recorded images. These data were then used to evaluate our method and to compare it to traditional line transect studies conducted in close proximity to our study site at Lake Tanganyika.We show that the analysis of the second hour of PCT image recordings (equivalent to 360 images per camera) leads to reliable estimates of the benthic cichlid community composition in Lake Tanganyika according to species accumulation curves, while minimizing the effect of disturbance of the fish through SCUBA divers. We further show that PCT is robust against observer biases and outperforms traditional line transect methods

SpeciesID_List

List of species observed in the study. The ID column represents the 6 letter code used in the community matrices. The last column indicates the position of the species in the water column, 1 referring to a purely benthic and 4 to a pelagic habitat

CommunityMatrix_3507

Raw count matrix of 1 of 2 cameras used for examining community disturbance through SCUBA divers (S5

CommunityMatrix_Observer1

Raw count matrix of PCT image processing. Each row represents one image, the first and second column are PCT and camera number, the thirds represents the unique image ID. The remaining columns are the species counts with the headings corresponding to the SpeciesID_List file. Oberserver 1 is L. Widme

CommunityMatrix_4028

Raw count matrix of 2 of 2 cameras used for examining community disturbance through SCUBA divers (S5

Data from: Point-Combination Transect (PCT): incorporation of small underwater cameras to study fish communities

1. Available underwater visual census methods such as line transects or point count observations are widely used to obtain community data of underwater species assemblages, despite their known pit-falls. As interest in the community structure of aquatic life is growing, there is need for more standardized and replicable methods for acquiring underwater census data. 2. Here, we propose a novel approach, Point-Combination Transect (PCT), which makes use of automated image recording by small digital cameras to eliminate observer and identification biases associated with available underwater visual census methods. We conducted a pilot study at Lake Tanganyika, demonstrating the applicability of PCT on a taxonomically and phenotypically highly diverse assemblage of fishes, the Tanganyikan cichlid species-flock. 3. We conducted 17 PCTs consisting of five GoPro cameras each and identified 22'867 individual cichlids belonging to 61 species on the recorded images. This data was then used to evaluate our method and to compare it to traditional line transect studies conducted in close proximity to our study site at Lake Tanganyika. 4. We show that the analysis of the second hour of PCT image recordings (equivalent to 360 images per camera) leads to reliable estimates of the benthic cichlid community composition in Lake Tanganyika according to species accumulation curves, while minimizing the effect of disturbance of the fish through SCUBA divers. We further show that PCT is robust against observer biases and outperforms traditional line transect methods