Nest-site fidelity of Arctic Terns (Sterna paradisaea) in a managed environment exposed to benign human activity
Data files
Oct 08, 2024 version files 33.70 KB
Abstract
Human activity near seabird colonies is often equated with disturbance. Coping with human disturbance is a critical task in the management of seabird colonies where human access is an issue. Nest-site fidelity and breeding dispersal were characterised in an Arctic Tern population exposed to a high level of benign human activity from resident conservation staff and public visitors. The annual return rate of nesting birds was 90% over the 10-year study period. Nest-site fidelity was high with most birds returning to the same 2 x 2 m square in subsequent years, in particular to nest sites that were against walls and exposed to a high level of human activity at close range. This indicates that human activity was not a deterrent to nesting by Arctic Terns. Breeding dispersal to other nearby colonies was low, except when related to colony abandonment. The data suggest that benign human activity from resident staff and public visitors can be a positive benefit, both within and across years, to Arctic Tern colonies. Managed public access could be considered an effective management tool for Arctic Tern conservation, as well as seabird colonies more widely.
Raw data for the virtual grid analysis are in the file:
Arctic_Tern_site_fidelity_Inner-Farne_virtual_grid_recapture_data.csv
Each row represents the end point of a capture-recapture event for an individual bird. That is, the grid-cell location and type that the bird is recaptured in after its previous capture in the virtual grid.
Data description
| Data column | Content |
|---|---|
| SPEC | Species: always Arctic Tern (ARCTE) |
| RING | The BTO ring number of the individual bird in that row |
| PLACE | Location of the virtual grid: always Inner Farne (inf) |
| RTYPE | Record type: always recapture (R) |
| DTE | Date and time of recapture event (end point of capture-recapture event) |
| THL | Total head length of individual (most at recapture) in millimetres |
| YEAR | Year of recapture (end point of capture-recapture event) |
| col.num | Virtual grid column at recapture (end point of capture-recapture event) |
| row.num | Virtual grid row at recapture (end point of capture-recapture event) |
| iti.diff | Number of grid cells moved between capture and recapture |
| iti.int | Interval in years between capture and recapture |
| dest | Destination grid-cell type: nn, dn, nx or dx |
| mov.tpe | Movement type: c(ori.cat, dest) |
| ori.cat | Originating grid-cell type: nn, dn, nx or dx |
| s.THL | Scaled (mean = 0) and standardised (sd = 1) total head length |
nn = nothing special, dn = ‘disturbed’, nx = edge, dx = ‘disturbed’ and edge where edge is against a wall or building, and ‘disturbed’ means directly exposed to human activity (directly next to a path or boardwalk)
Concatenation of ori.cat and dest
Analysis:
Data can be analysed using R 4.4.2 and the packages glmmTMB
https://cran.r-project.org/web/packages/glmmTMB/index.html
or ordinal
https://cran.r-project.org/web/packages/ordinal/index.html
For example:
dat <- read.csv(“/…directory-path…/Arctic_Tern_site_fidelity_Inner-Farne_virtual_grid_recapture_data.csv”, header=T)
#data: iti.int = interval between capture and recapture in years
# ori.cat = originating grid-cell type
# iti.diff = grid cells moved between capture and recapture
# RING = ring number
# With package glmmTMB (for number of grid cells moved as count data):
| model <- glmmTMB(iti.diff ~ iti.int + ori.cat + (1 | RING), data= dat, family=nbinom2) |
#or package ordinal (treating the number of grid cells moved as an ordered factor related to distance):
| model <- clmm(iti.diff ~ iti.int * ori.cat + (1 | RING), data= dat)# default link “logit” |
#
#
Movement of nesting Arctic Terns within the virtual grid on Inner Farne between capture and recapture.
code
datmf <- read.csv(“ ~Arctic_Tern_site_fidelity_Inner-Farne_virtual_grid_recapture_data.csv”, header=T)
#data: iti.int = interval between capture and recapture in years
# ori.cat = originating grid-cell type
# iti.diff = grid cells moved between capture and recapture
# s.THL = scaled and standardised total head length
# RING = ring number
| m3.2 <- glmmTMB(iti.diff ~ iti.int + ori.cat + (1 | RING), data= datmf, family=nbinom2) |