Analysis of cottontop abundance at St Juan Nepomuenco

1 Introduction

This document contains an analysis of cottontop tamarin survey results from St Juan Nepomuenco.

require(sf)
require(tidyverse)
require(readxl)

require(raster) 
require(OpenStreetMap) #To download the imagery
require(tmaptools) # To download satellite imagery
require(tmap) #To plot maps

#Store EPSG coordinate system code for utm18
utm18 <- 32618
wgs84 <- 4326

source("CottontopHelperFunctions.r")

1.1 Data

#Read in patches
patch_names <- c("San Juan Nepomuceno 1", "Perico", "Laguna")
dsn_Design <- ("../2021 Survey Design/GISdata")
patches <- st_read(dsn_Design, "forest_parcels_2021", quiet = TRUE)
lpatches <- patches %>% 
  dplyr::filter(patch %in% patch_names)
transect_strips <- st_read(dsn_Design, "transect_strips_2021", quiet = TRUE)
#Use the non-overlapping buffer functions to get buffered polygons
# rather than reading from the original shapefiles as these did not deal
# with buffering
#30m buffer includes original patch within it
lpatches_30m <- create_nonoverlapping_buffers(lpatches, 30, TRUE)
#300m buffer buffers 30m patch, and does not include original patch
lpatches_300m <- create_nonoverlapping_buffers(lpatches_30m, 300, FALSE)

#Find transects that touch the selected patches
ind <- (lengths(st_intersects(transect_strips, lpatches_30m)) > 0) | 
  (lengths(st_intersects(transect_strips, lpatches_300m)) > 0) 
ltransect_strips <- transect_strips[ind, ]

lsegments <- read_rds("segments.rds")
lsegments$n <- lsegments$titis
lsegments$n[is.na(lsegments$n)] <- 0

#Download a sat image basemap and plot behind the patches and segments
#create a 500m buffer around the segments, so the basemap is a bit bigger than the segment extent
tmp <- st_buffer(st_geometry(lpatches_300m), dist = 1000)
sat <- read_osm(tmp, zoom = 14, type = "esri-imagery") #"bing")
sat <- as(sat, "Raster")
#sat2 <- projectRaster(sat, crs = CRS(paste0("epsg:", utm18)), method = "bilinear")
#sat_df <- as.data.frame(sat, xy = T)

We present below a map of the local area, showing transects and titi detections. For illustration purposes, the figure shows an “outside” segment on each side of the transect line, where this outside segment is 100m wide. Segments in blue were surveyed; those in grey were not. Titi detections are shown in yellow for those inside the transects and in red for those outside. The green lines show the forest patches (the inner sets of lines) and a 300m buffer aroudn these patches (the outer set of lines).

tm_shape(sat) +
    tm_rgb() +
  tm_shape(lpatches_300m) + 
  tm_polygons(fill = NULL, fill_alpha = 0.2, col = "green", col_alpha = 1, lwd = 2) +
  tm_shape(lpatches_30m) + 
  tm_polygons(fill = NULL, col = "green", fill_alpha = 0.5, lwd = 2) + 
  tm_shape(lsegments[(lsegments$n > 0) & (lsegments$inside), ]) +
  tm_polygons(fill = "yellow", fill_alpha = 0.4) +
  tm_shape(lsegments[(lsegments$n > 0) & (!lsegments$inside), ]) +
  tm_polygons(fill = "red", fill_alpha = 0.4) +
  tm_shape(lsegments[lsegments$surveyed, ]) +
  tm_polygons(fill = NULL, col = "blue") +
  tm_shape(lsegments[!lsegments$surveyed, ]) +
  tm_polygons(fill = NULL, col = "gray40") +
  tm_shape(ltransect_strips) + 
  tm_text("name") +
  tm_shape(lpatches_30m) +
  tm_text("patch", col = "green")

2 Estimate

core_overlap <- st_intersection(lsegments, lpatches_30m$geometry[lpatches_30m$patch == "San Juan Nepomuceno 1"])

Warning: attribute variables are assumed to be spatially constant throughout
all geometries

core_overlap$area <- st_area(core_overlap)
core_overlap$n <- core_overlap$titis / (core_overlap$area / 5000)
core_D <- sum(core_overlap$n, na.rm = TRUE) / sum(core_overlap$area, na.rm = TRUE)
core_N <- core_D * st_area(lpatches_30m[lpatches_30m$patch == "San Juan Nepomuceno 1", ])
buffer_overlap <- st_intersection(lsegments, lpatches_300m$geometry[lpatches_300m$patch == "San Juan Nepomuceno 1"])

Warning: attribute variables are assumed to be spatially constant throughout
all geometries

buffer_overlap$area <- st_area(buffer_overlap)
buffer_overlap$n <- buffer_overlap$titis / (buffer_overlap$area / 5000)
buffer_D <- sum(buffer_overlap$n, na.rm = TRUE) / sum(buffer_overlap$area, na.rm = TRUE)
buffer_N <- buffer_D * st_area(lpatches_300m[lpatches_300m$patch == "San Juan Nepomuceno 1", ])
N <- core_N + buffer_N

Here, we use titi detections both from inside and outside the transects; to make use of those from outside we assume in this initial analysis that they come from a survey region that is 100m wide (as is shown on the above figure).

Sightings of tits are recorded in the segment they were detected on. In some rare cases the detection is split between inside and outside strata – in which case it is split in proportion to the area of the segments surveyed in that regon.

The total surveyed area in the core area is 443.3946 ha. Within this area 61.83383 titis were seen. This gives a density for the core of 1.394555e-05 and an abundance of 118.1204.