Thresholds in seascape connectivity: the spatial arrangement of nursery habitats structure fish communities on nearby reefs

2020-03-05T12:20:57Z (GMT) by Charlotte Berkström
File format: csv

File descriptions:
File: Mean_prop_abun_nursery_spcs - Mean proportional abundance (fish/100m2) of nursery and non-nursery fish species on reefs in the Bazaruto Archipelago, Mozambique (Fig 2)

File: Mean_biomass_nursery_species - Mean biomass (grams/100m2) of nursery and non-nursery fish species on reefs in the Bazaruto Archipelago, Mozambique (Fig 3)
File: Prop_nursery_spcs_by_family - Proportion nursery fish species within Scarinae, Haemulidae, Lutjanidae and Lethrinidae on reefs in the Bazaruto Archipelago, Mozambique. Data used in Generalized Additive Models (GAMs), Fig 4, 5 and Table 1

File: Abun_fish_spcs_and_family - Abundance (total no. of fish/transect) of fish species and nursery and non-nursery fish species within fish families on reefs in the Bazaruto Archipelago, Mozambique. Data used in Generalized Additive Models (GAMs), Fig 4, 5 and Table 1 and RDA, Fig A3

File: Abun_Scarini - Abundance (total no. of fish/transect) of Scarini fish species and nursery and non-nursery Scarini species on reefs in the Bazaruto Archipelago, Mozambique. Data used in Generalized Additive Models (GAMs), Fig 4, 5 and Table 1 and RDA, Fig A3

File: Abun_nursery_spcs - Abundance (total no. of fish/transect) of nursery and non-nursery fish species on reefs in the Bazaruto Archipelago, Mozambique. Data used in Generalized Additive Models (GAMs), Fig A4

File: Habitat_data - Habitat data (distance to mangrove, seagrass, channels, topographic complexity, depth and % habitat cover) for each fish transect on reefs in the Bazaruto Archipelago, Mozambique. Used in Generalized Additive Models (GAMs), Fig 5 and Table 1.

Data analyses:
To investigate if the fish assemblage was affected by environ-mental variables, redundancy analysis (RDA) ordinations were performed for selected abundant fish taxa relevant to the study aims. Species were categorised as nursery species or non-nursery species where possible, following Froese and Pauly (2019) (Supplementary material Appendix 1 Table A1). The following taxa were included; Acanthurus spp., Chaetodon auriga, Chaetodon non-nursery species, Chlorurus sordidus, Chromis fieldi, Chromis viridis, Gnathodentex aurolineatus, Halichoeres scapularis, Lethrinus nursery-species, Lutjanus nursery species, Lutjanus non-nursery species, Mulloidichctys spp., Naso spp., Parupeneus spp., Haemulidae nursery spe-cies, Haemulidae non-nursery species, Scarus ghobban, Scarus rubroviolaceus, Siganus sutor and Upeneus tragula.
Fish data was transformed using Hellinger transforma-tion (Legendre and Gallagher 2001) and the environmental variables were transformed to z-scores using the ‘standard-ize’ transformation since variables were measured on differ-ent scales. Significance of axes was tested with the function anova.cca in R (ver. 3.3.4), using 999 permutations.
The ordinations were performed in the package ‘vegan’ in R (ver. 3.3.4) using each UVC as a replicate and variables on habitat level (depth, topographic complexity, proportion sand, proportion bare rock, coral rubble and cover of thick leathery algae, branching coral, tabular coral, massive coral, CCA and EAM cover on carbonate and on rocky substrate), combined with distance to mangroves, seagrass beds and channels.

To understand how relationships of proximity to nurseries structured fish assemblages on the reefs and identify thresh-olds where abrupt changes in fish abundance, biomass or proportion nursery and non-nursery species occurred, gener-alized additive models (GAMs) were performed for fish fami-lies that include both nursery and non-nursery species, and that were fairly abundant in the data set (Francesco Ficetola and Denoël 2009). These included Scarinae, Lutjanidae and Haemulidae. For all models, each UVC was used as a rep-licate. Collinearity of predictors was tested with variance inflation factor (VIF), and predictors with a value larger than three were excluded from the models. Correlation of variables was investigated using the correlation chart function in the ‘PerformanceAnalytics’ package in R. The GAMs were per-formed with the ‘logit’ function, and REML as smoothing parameter estimation method, using proportion (binomial distribution) and abundance (Poisson distribution) of nurs-ery species for the three families, with distance to mangroves, seagrass and/or channels and complexity, depth, coral and EAM cover as predictor variables for initial models.
To investigate distribution patterns in relation to nurs-ery habitat use, abundance and biomass were also modelled for fish grouped into different nursery categories (seagrass,mangrove, occasional and non-nursery species). All variables were scaled since they were measured on different scales, and both abundance and biomass were modelled using a Gaussian distribution with the ‘identity’ function. The final models
were chosen based on lowest AIC values, when ΔAIC > 2 between models (Burnham and Anderson 1998). All GAMs were executed with the ‘mgcv’ package (Wood 2017) in R (R Core Team).