Crowdsourcing Osprey Occurrences and Migration Trends in the US Using Flickr Photos

GEO511

Author

Festus Adegbola

Published

December 13, 2024

Introduction

The Osprey (Pandion haliaetus) is a partial migratory bird species with a broad distribution across North America. Partial migration means that not all populations migrate, and the extent of migration depends on geographical location and environmental conditions. Ospreys breeding in northern latitudes (e.g., Canada and the northern United States) are typically obligate migrants, traveling to Central and South America for the winter. Those in southern parts of the United States may be year-round residents, depending on food availability and climate.

Monitoring bird populations and migration patterns is vital for understanding ecological dynamics and informing conservation efforts. However, traditional monitoring approaches, such as field observations and curated datasets like eBird, are often resource-intensive and may leave gaps in spatial and temporal coverage. As such, there is a growing need to explore complementary data sources that can fill these gaps.

This project investigates the potential of social media as an alternative data source for ecological monitoring by focusing on the seasonal occurrences and geographical distribution of Ospreys across the United States. Using geotagged Flickr photos and comparing them to formal bird observation data from eBird, the study aims to assess the extent to which trends in the presence and migration patterns of Ospreys captured through social media align with patterns documented in citizen science databases. By highlighting the utility of photo-based social media data, this project seeks to advance our understanding of how platforms like Flickr can contribute to ecological and avian studies, offering a scalable and accessible tool for biodiversity monitoring.

Osprey

Materials and methods

Datasets

The first step in the project is to download geotagged images of Ospreys from Flickr’s public API. This involve querying the “Osprey” tag to identify relevant photos. The query is also restricted to a specific timeframe to facilitate the analysis of seasonal patterns. Metadata associated with the photos, including geographic coordinates, date, and time of posting, were collected.

eBird data will be downloaded from the eBird data products website and processed in R. The occurrence and observation data will be accessed through eBird’s data portal, specifically focusing on Osprey sightings across the U.S. The dataset will include key variables such as location, and, date (aligned to match the timeframe of Flickr photo requests). This standardized dataset will enable direct comparisons with the Flickr data for temporal and spatial analysis.

Installing and loading libraries

Let’s start by installing and loading the required R libraries:

Code 
install.packages("FlickrAPI")
install.packages("httr")
install.packages("jsonlite")
install.packages("devtools")
install.packages("usmapdata")
install.packages("maps")
install.packages("USA.state.boundaries")
remotes::install_github("CornellLabofOrnithology/auk")
install.packages("tidyverse")
install.packages("kableExtra")
install.packages("htmlwidgets")
install.packages("widgetframe")
devtools::install_github("nfox29/photosearcher")
Code 
library(FlickrAPI)
library(httr)
library(jsonlite)
library(devtools)
library(sf)
library(usmapdata)
library(maps)
library(ggplot2)
library(dplyr)
library(lubridate)
library(USA.state.boundaries)
library(forcats)
library(auk)
library(tidyverse)
library(rmarkdown)
library(knitr)
library(kableExtra)
library(htmlwidgets)
library(widgetframe)
library(photosearcher)
knitr::opts_chunk$set(widgetframe_widgets_dir = 'widgets' ) 
knitr::opts_chunk$set(cache=TRUE)  # cache the results for quick compiling

Retrieving Flickr Photos

  • Set up your Flickr credentials (i.e Flickr key and secret)
  • Create a Flickr account to set up API key and secret
  • Request Flickr photo using the Flickr API through photosearcher library
  • Set up a bounding box to only extract data within the US
  • Set up minimum and maximum date (2000-2024, in this case)
Code 
Osprey <- photo_search(
  mindate_taken = "2000-01-01",
  maxdate_taken = "2024-11-28", 
  text = "Osprey",
  tags = c("Osprey", "osprey"),
  bbox = c(-124.6813,25.12993,-67.00742,49.38323),
  has_geo = TRUE)

Exploring Dataset

Code 
Osprey %>% 
  slice(1:5) %>% 
  kable(digits=2,align="c")%>% 
  kable_styling(bootstrap_options = 
                  c("striped", "hover", "condensed", "responsive"))
license id owner secret server farm title ispublic isfriend isfamily dateupload lastupdate datetaken datetakengranularity datetakenunknown count_views count_faves count_comments tags latitude longitude accuracy context place_id woeid geo_is_public geo_is_contact geo_is_friend geo_is_family url_sq height_sq width_sq url_t height_t width_t url_s height_s width_s url_q height_q width_q url_m height_m width_m url_n height_n width_n url_z height_z width_z url_c height_c width_c url_l height_l width_l url_o height_o width_o description license_name license_url
0 89373850 67778747@N00 5af5ab4f92 29 1 osprey-15 NA NA NA 2006-01-21 20:04:14 2008-03-04 21:48:30 2000-01-01 01:34:00 0 0 188 0 0 osprey military airplane helicopter marines athens georgia 33.95 -83.32 16 0 2492982 NA NA NA NA https://live.staticflickr.com/29/89373850_5af5ab4f92_s.jpg 75 75 https://live.staticflickr.com/29/89373850_5af5ab4f92_t.jpg 75 100 https://live.staticflickr.com/29/89373850_5af5ab4f92_m.jpg 180 240 https://live.staticflickr.com/29/89373850_5af5ab4f92_q.jpg 150 150 https://live.staticflickr.com/29/89373850_5af5ab4f92.jpg 375 500 https://live.staticflickr.com/29/89373850_5af5ab4f92_n.jpg 240 320 https://live.staticflickr.com/29/89373850_5af5ab4f92_z.jpg 480 640 https://live.staticflickr.com/29/89373850_5af5ab4f92_c.jpg 600 800 https://live.staticflickr.com/29/89373850_5af5ab4f92_b.jpg 768 1024 NA NA NA All Rights Reserved NA
0 89373851 67778747@N00 6c125a40d1 26 1 osprey-16 NA NA NA 2006-01-21 20:04:14 2008-03-04 21:48:31 2000-01-01 01:36:43 0 0 210 0 0 osprey military airplane helicopter marines athens georgia 33.95 -83.32 16 0 2492982 NA NA NA NA https://live.staticflickr.com/26/89373851_6c125a40d1_s.jpg 75 75 https://live.staticflickr.com/26/89373851_6c125a40d1_t.jpg 75 100 https://live.staticflickr.com/26/89373851_6c125a40d1_m.jpg 180 240 https://live.staticflickr.com/26/89373851_6c125a40d1_q.jpg 150 150 https://live.staticflickr.com/26/89373851_6c125a40d1.jpg 375 500 https://live.staticflickr.com/26/89373851_6c125a40d1_n.jpg 240 320 https://live.staticflickr.com/26/89373851_6c125a40d1_z.jpg 480 640 https://live.staticflickr.com/26/89373851_6c125a40d1_c.jpg 600 800 https://live.staticflickr.com/26/89373851_6c125a40d1_b.jpg 768 1024 NA NA NA All Rights Reserved NA
0 89373853 67778747@N00 1c06740445 14 1 osprey-17 NA NA NA 2006-01-21 20:04:14 2008-03-04 21:48:31 2000-01-01 01:39:27 0 0 226 0 0 osprey military airplane helicopter marines athens georgia 33.95 -83.32 16 0 2492982 NA NA NA NA https://live.staticflickr.com/14/89373853_1c06740445_s.jpg 75 75 https://live.staticflickr.com/14/89373853_1c06740445_t.jpg 75 100 https://live.staticflickr.com/14/89373853_1c06740445_m.jpg 180 240 https://live.staticflickr.com/14/89373853_1c06740445_q.jpg 150 150 https://live.staticflickr.com/14/89373853_1c06740445.jpg 375 500 https://live.staticflickr.com/14/89373853_1c06740445_n.jpg 240 320 https://live.staticflickr.com/14/89373853_1c06740445_z.jpg 480 640 https://live.staticflickr.com/14/89373853_1c06740445_c.jpg 600 800 https://live.staticflickr.com/14/89373853_1c06740445_b.jpg 768 1024 NA NA NA All Rights Reserved NA
0 89371533 67778747@N00 466382d747 27 1 osprey-01 NA NA NA 2006-01-21 19:58:49 2008-03-04 21:48:29 2000-01-01 01:42:41 0 0 230 0 0 osprey military airplane helicopter marines athens georgia 33.95 -83.32 16 0 2492982 NA NA NA NA https://live.staticflickr.com/27/89371533_466382d747_s.jpg 75 75 https://live.staticflickr.com/27/89371533_466382d747_t.jpg 75 100 https://live.staticflickr.com/27/89371533_466382d747_m.jpg 180 240 https://live.staticflickr.com/27/89371533_466382d747_q.jpg 150 150 https://live.staticflickr.com/27/89371533_466382d747.jpg 375 500 https://live.staticflickr.com/27/89371533_466382d747_n.jpg 240 320 https://live.staticflickr.com/27/89371533_466382d747_z.jpg 480 640 https://live.staticflickr.com/27/89371533_466382d747_c.jpg 600 800 https://live.staticflickr.com/27/89371533_466382d747_b.jpg 768 1024 NA NA NA All Rights Reserved NA
0 89371534 67778747@N00 a96aa9aa88 17 1 osprey-02 NA NA NA 2006-01-21 19:58:49 2008-03-04 21:48:29 2000-01-01 01:45:03 0 0 214 0 0 osprey military airplane helicopter marines athens georgia 33.95 -83.32 16 0 2492982 NA NA NA NA https://live.staticflickr.com/17/89371534_a96aa9aa88_s.jpg 75 75 https://live.staticflickr.com/17/89371534_a96aa9aa88_t.jpg 75 100 https://live.staticflickr.com/17/89371534_a96aa9aa88_m.jpg 180 240 https://live.staticflickr.com/17/89371534_a96aa9aa88_q.jpg 150 150 https://live.staticflickr.com/17/89371534_a96aa9aa88.jpg 375 500 https://live.staticflickr.com/17/89371534_a96aa9aa88_n.jpg 240 320 https://live.staticflickr.com/17/89371534_a96aa9aa88_z.jpg 480 640 https://live.staticflickr.com/17/89371534_a96aa9aa88_c.jpg 600 800 https://live.staticflickr.com/17/89371534_a96aa9aa88_b.jpg 768 1024 NA NA NA All Rights Reserved NA

Retrieving eBird Osprey Observations

See https://science.ebird.org/en/use-ebird-data/download-ebird-data-products to download eBird EBD data

  • Set path for the downloaded eBird data
  • Import the eBird txt file
Code 
#auk_set_ebd_path(path = "path/to/eBird/data")

ebird <- read.delim("ebird_Osprey_Oct-2024.txt", sep = "\t", 
                    header = T, quote = "", 
                    stringsAsFactors = F, na.strings = c(""," ",NA))

Explore Dataset

Code 
ebird %>% 
  slice(1:5) %>% 
  kable(digits=2,align="c")%>% 
  kable_styling(bootstrap_options = 
                  c("striped", "hover", "condensed", "responsive"))
COMMON.NAME SCIENTIFIC.NAME STATE COUNTY LOCALITY LATITUDE LONGITUDE OBSERVATION.DATE year month season
Osprey Pandion haliaetus California Yolo Lake Solano--Pleasants Valley Rd. bridge (YOL Co.) 38.49 -122.03 2000-01-09 2000 1 Winter
Osprey Pandion haliaetus Florida Indian River Jaycee Beach Park 27.67 -80.36 2000-01-15 2000 1 Winter
Osprey Pandion haliaetus Florida Monroe Harry Harris County Park 25.02 -80.50 2000-01-16 2000 1 Winter
Osprey Pandion haliaetus Florida Monroe Lower Matecumbe Key 24.86 -80.71 2000-01-16 2000 1 Winter
Osprey Pandion haliaetus Florida Monroe No Name Key 24.70 -81.33 2000-01-16 2000 1 Winter

Format Data

Code 
#Extract month and year from the "datataken" column of Flickr data
Osprey$year <- format(Osprey$datetaken, "%Y")
Osprey$month <- format(Osprey$datetaken, "%m")

#Assign observations into seasons for Flickr
Osprey$month <- as.numeric(Osprey$month)
Osprey$season <- ifelse(Osprey$month %in% c(12,1, 2), "Winter",
                     ifelse(Osprey$month %in% c(3, 4, 5), "Spring",
                            ifelse(Osprey$month %in% c(6, 7, 8), "Summer", "Fall")))

#Extract Month and Year for eBird data
ebird$OBSERVATION.DATE <- as.Date(ebird$OBSERVATION.DATE)
ebird$year <- format(ebird$OBSERVATION.DATE, "%Y")
ebird$month <- format(ebird$OBSERVATION.DATE, "%m")

#Assign observations into seasons for eBird
ebird$month <- as.numeric(ebird$month)
ebird$season <- ifelse(ebird$month %in% c(12,1, 2), "Winter",
                       ifelse(ebird$month %in% c(3, 4, 5), "Spring",
                              ifelse(ebird$month %in% c(6, 7, 8), "Summer", "Fall")))

Assign States to Flickr Photos

Code 
#Import Shapefile of the US
us_states <- st_read("gadm41_USA_1.shp")

# Create a spatial points dataframe from the 'flickr photos' dataframe
robin_ss <- st_as_sf(Osprey, coords = c("longitude", "latitude"), crs = st_crs(us_states))

# Perform a spatial join to determine the state for each point
Osprey_states <- st_join(robin_ss, us_states)

#Assign state to Flickr observation from the new dataframe
Osprey$state <- Osprey_states$NAME_1

#remove all rows where state is NAs 
Osprey <- Osprey[!is.na(Osprey$state), ]

Display Distribution of Ospreys using Flickr Photos

Create map to show the distributions of Osprey within the US using Flickr geotagged photos

Code 
us_map <- map_data("state") 

ggplot() +
  geom_polygon(data = us_map, aes(x = long, y = lat, group = group),
               fill = "gray90", color = "white") +
  geom_point(data = Osprey, aes(x = longitude, y = latitude), 
             color = "black", fill = "gray50", shape = 21, size = 3, alpha = 0.7) +
  coord_quickmap(xlim = c(-125, -66), ylim = c(24, 50)) +
  labs(
    title = "Flickr Osprey Observations in the United States using Flickr Photos",
    x = "Longitude",
    y = "Latitude"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(size = 12, face = "bold", hjust = 0.5),
    axis.title.x = element_text(size = 12),
    axis.title.y = element_text(size = 12),
    legend.position = "right"
  )

Visualizing Seasonal Geodistribution of Ospreys’ Occurrences

Code 
#Check the frequency of seasons 
summer_frequency <- Osprey %>%
  filter(season == "Summer") %>%
  count(state, name = "frequency") %>%
arrange(desc(frequency))

winter_frequency <- Osprey %>%
  filter(season == "Winter") %>%
  count(state, name = "frequency") %>%
  arrange(desc(frequency))

#show them as percentage 
summer_sum <- sum(summer_frequency$frequency)
winter_sum <- sum(winter_frequency$frequency)

summer_frequency <- transform(summer_frequency, percent = frequency / summer_sum * 100)
winter_frequency <- transform(winter_frequency, percent = frequency / winter_sum * 100)

summer_frequency <- head(summer_frequency, n = 5)
winter_frequency <- head(winter_frequency, n = 5)

summer_frequency$season <- "Summer"
winter_frequency$season <- "Winter"

seasonal_occ <- rbind(summer_frequency, winter_frequency)

seasonal_occ <- seasonal_occ %>%
  mutate(state = fct_reorder(state, -frequency))

ggplot(seasonal_occ, aes(x = factor(state, levels = rev(unique(state))), y = frequency, fill = season)) +
  geom_bar(stat = "identity", position = position_dodge(width = 0.8), width = 0.6, color = "black") + 
  geom_text(aes(label = paste(round(percent, 1), "%")), 
            position = position_dodge(width = 0.8), 
            vjust = -2.5, size = 3.5, color = "black", fontface = "bold") +
  labs(
    title = "Osprey Seasonal Occurrences by State",
    x = "State",
    y = "Percentage of Total Occurrence"
  ) +
  scale_fill_manual(
    values = c("Winter" = "#1f78b4", "Summer" = "#ff7f00"), 
    labels = c("Summer", "Winter"), 
    name = "Season"
  ) +
  theme_minimal() +
  theme(
    legend.position = "top",
    plot.title = element_text(size = 16, face = "bold", hjust = 0.5), 
    axis.title.x = element_text(size = 14, face = "bold"),
    axis.title.y = element_text(size = 14, face = "bold"),
    axis.text.x = element_text(size = 12, face = "bold", angle = 45, hjust = 1), 
    axis.text.y = element_text(size = 12, face = "bold"),
    legend.title = element_text(size = 12, face = "bold"),
    legend.text = element_text(size = 11),
    panel.grid.major.y = element_line(color = "gray80", linetype = "dashed"), 
    panel.grid.major.x = element_blank() 
  ) +
  facet_wrap(~ season, scales = "free", ncol = 1) +
  coord_flip()

Comparing Seasonal Geodistribution of Ospreys from Flickr to eBird

Code 
#Check the frequency of seasons 
winter_frequency <- Osprey %>%
  filter(season == "Winter") %>%
  count(state, name = "frequency") %>%
  arrange(desc(frequency))

winter_ebird <- ebird %>%
  filter(season == "Winter") %>%
  count(STATE, name = "frequency") %>%
  arrange(desc(frequency))

winter_sum <- sum(winter_ebird$frequency)
flickr_win_sum <- sum(winter_frequency$frequency)

winter_ebird <- transform(winter_ebird, percent = frequency / winter_sum * 100)
winter_frequency <- transform(winter_frequency, percent = frequency / flickr_win_sum * 100)

winter_ebird <- head(winter_ebird, n = 6)
winter_frequency <- head(winter_frequency, n = 6)


winter_ebird$source <- "eBird"
winter_frequency$source <- "Flickr"

winter_ebird <- winter_ebird %>%
  rename(state = STATE) 

winter_occ <- rbind(winter_ebird, winter_frequency)

winter_occ <- winter_occ %>%
  mutate(state = fct_reorder(state, -percent))

ggplot(winter_occ, aes(x = factor(state, levels = rev(unique(state))), y = frequency, fill = source)) +
  geom_bar(stat = "identity", position = position_dodge(width = 0.8), width = 0.6, color = "black") + # Add border for bars
  geom_text(aes(label = paste(round(percent, 1), "%")), 
            position = position_dodge(width = 0.8), 
            vjust = -0.5, hjust = -0.99, size = 3.1, color = "black", fontface = "bold") +
  labs(
    title = "Osprey Winter Occurrences by State",
    x = "State",
    y = "Percentage of Total Occurrence"
  ) +
  scale_fill_manual(
    values = c( "eBird" = "#ff7f00", "Flickr" = "#1f78b4" ), 
    labels = c("eBird", "Flickr"), 
    name = "Source"
  ) +
  theme_minimal() +
  theme(
    legend.position = "top",
    plot.title = element_text(size = 16, face = "bold", hjust = 0.5), 
    axis.title.x = element_text(size = 14, face = "bold"),
    axis.title.y = element_text(size = 14, face = "bold"),
    axis.text.x = element_text(size = 12, face = "bold", angle = 45, hjust = 1), 
    axis.text.y = element_text(size = 12, face = "bold"),
    legend.title = element_text(size = 12, face = "bold"),
    legend.text = element_text(size = 11),
    panel.grid.major.y = element_line(color = "gray80", linetype = "dashed"), 
    panel.grid.major.x = element_blank() 
  ) +
  facet_wrap(~ source, scales = "free", ncol = 1) +
  coord_flip()

Overall Seasonal Trend of Observations

Code 
#Plot of seasons
seasons <- Osprey %>%
  group_by(season) %>%
  summarise(Count = n()) %>%
  arrange(factor(season, levels = c("Spring", "Summer", "Fall", "Winter")))

season_sum <- sum(seasons$Count)

seasons <- transform(seasons, percent = Count / season_sum * 100) %>%
  arrange(factor(season, levels = c("Spring", "Summer", "Fall", "Winter")))

#For ebird data
seasons_ebird <- ebird %>%
  group_by(season) %>%
  summarise(Count = n()) %>%
  arrange(factor(season, levels = c("Spring", "Summer", "Fall", "Winter")))

ebird_season_sum <- sum(seasons_ebird$Count)

seasons_ebird <- transform(seasons_ebird, percent = Count / ebird_season_sum * 100) %>%
  arrange(factor(season, levels = c("Spring", "Summer", "Fall", "Winter")))

seasons_ebird$source <- "eBird"
seasons$source <- "Flickr"

seasonal_trend <- rbind(seasons_ebird, seasons)

ggplot(seasonal_trend, aes(x = factor(season, levels = c("Spring", "Summer", "Fall", "Winter")), y = percent, fill = source)) +
  geom_bar(stat = "identity", position = position_dodge(width = 0.8), width = 0.7) +
  geom_text(aes(label = paste(round(percent, 1), "%")), 
            position = position_dodge(width = 0.8), 
            vjust = -0.3, size = 4, color = "black", fontface = "bold") +
  
  # Title and axis labels
  labs(
    title = "Osprey Seasonal Frequency across Data Sources",
    x = "Season",
    y = "Percentage of Total Events",
    fill = "Data Source"
  ) +
  scale_fill_manual(values = c("Flickr" = "#1f78b4", "eBird" = "#ff7f00")) +
  theme_minimal(base_size = 12) +
  theme(
    plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
    axis.title.x = element_text(size = 14, face = "bold", margin = margin(t = 10)),
    axis.title.y = element_text(size = 14, face = "bold", margin = margin(r = 10)),
    axis.text.x = element_text(size = 12, angle = 45, hjust = 1, vjust = 1),
    axis.text.y = element_text(size = 12),
    legend.title = element_text(size = 12, face = "bold"),
    legend.text = element_text(size = 12),
    panel.grid.major.y = element_line(color = "gray80", linetype = "dashed"),
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_blank()
  )

Overall GeoDistribution of Ospreys in the US comparing both datasets

Code 
#State freq
State_frequency <- Osprey %>%
  count(state, name = "frequency") %>%
  arrange(desc(frequency))

#State freq
State_ebird <- ebird %>%
  count(STATE, name = "frequency") %>%
  arrange(desc(frequency))

State_ebird <- head(State_ebird, n = 10)
State_frequency <- head(State_frequency, n = 10)

ebird_state_sum <- sum(State_ebird$frequency)
flickr_season_sum <- sum(State_frequency$frequency)

State_ebird <- transform(State_ebird, percent = frequency/ ebird_state_sum * 100) 
State_frequency <- transform(State_frequency, percent = frequency/ flickr_season_sum * 100) 

State_ebird$source <- "eBird"
State_frequency$source <- "Flickr"

State_ebird <- State_ebird %>%
  rename(state = STATE) 

state_trend <- rbind(State_ebird, State_frequency)

state_trend <- state_trend %>%
  mutate(state = fct_reorder(state, -percent))

ggplot(state_trend, aes(x = factor(state, levels = rev(unique(state))), y = frequency, fill = source)) +
  geom_bar(stat = "identity", position = position_dodge(width = 0.8), width = 0.6, color = "black") + 
  geom_text(aes(label = paste(round(percent, 1), "%")), 
            position = position_dodge(width = 0.8), 
            vjust = -2.5, size = 3.5, color = "black", fontface = "bold") +
  labs(
    title = "Osprey State Occurrences across Data Source",
    x = "State",
    y = "Percentage of Total Occurrence"
  ) +
  scale_fill_manual(
    values = c( "eBird" = "#ff7f00", "Flickr" = "#1f78b4" ), 
    labels = c("eBird", "Flickr"), 
    name = "Source"
  ) +
  theme_minimal() +
  theme(
    legend.position = "top",
    plot.title = element_text(size = 16, face = "bold", hjust = 0.5), 
    axis.title.x = element_text(size = 14, face = "bold"),
    axis.title.y = element_text(size = 14, face = "bold"),
    axis.text.x = element_text(size = 12, face = "bold", angle = 45, hjust = 1), 
    axis.text.y = element_text(size = 12, face = "bold"),
    legend.title = element_text(size = 12, face = "bold"),
    legend.text = element_text(size = 11),
    panel.grid.major.y = element_line(color = "gray80", linetype = "dashed"), 
    panel.grid.major.x = element_blank() 
  ) +
  facet_wrap(~ source, scales = "free", ncol = 1) +
  coord_flip()

Results

The analysis of Flickr and eBird data reveals consistent trends in the seasonal distribution of Ospreys across the United States. Flickr photos indicate clear seasonal migration patterns, with Ospreys predominantly observed in northern states like New Jersey and Maryland during the summer months, followed by a shift to southern states such as Florida, California, Texas, Arizona, and North Carolina during the winter (Figure 2).

A closer examination of the winter distribution shows strong alignment between the two datasets. The top four states with the highest recorded Osprey occurrences during winter are identical in both Flickr and eBird data (Figure 3), highlighting a high level of correlation between the two sources.

Flickr and eBird data exhibit consistent trends in the seasonal distribution of Ospreys across the United States, spanning spring, summer, fall, and winter (Figure 4).Furthermore, when comparing the top ten states where Ospreys are observed year-round, nine of these states match between Flickr and eBird datasets (Figure 5).

Conclusions

The high degree of correlation between the two data sources, particularly in the top states for winter occurrences and year-round presence, highlights the reliability of photo-based citizen science data in reflecting species distribution patterns. These findings underscore the value of integrating alternative data sources like Flickr with traditional ecological datasets to enhance spatial and temporal coverage, offering a scalable approach to support conservation and monitoring efforts for migratory species like the Osprey.

References

  1. Bierregaard, R. O., Poole, A. F., & Washburn, B. E. (2014). Ospreys (Pandion haliaetus) in the 21st century: populations, migration, management, and research priorities. Journal of Raptor Research, 48(4), 301-308.

  2. Martell, M. S., Bierregaard Jr, R. O., Washburn, B. E., Elliott, J. E., Henny, C. J., Kennedy, R. S., & MacLeod, I. (2014). The spring migration of adult North American Ospreys. Journal of Raptor Research, 48(4), 309-324.

  3. Martell, M. S., Henny, C. J., Nye, P. E., & Solensky, M. J. (2001). Fall migration routes, timing, and wintering sites of North American Ospreys as determined by satellite telemetry. The Condor, 103(4), 715-724.

  4. Monti, F., Grémillet, D., Sforzi, A., Sammuri, G., Dominici, J. M., Triay Bagur, R., … & Duriez, O. (2018). Migration and wintering strategies in vulnerable Mediterranean Osprey populations. Ibis, 160(3), 554-567.

  5. Henny, C. J., Grove, R. A., Kaiser, J. L., & Johnson, B. L. (2010). North American osprey populations and contaminants: historic and contemporary perspectives. Journal of Toxicology and Environmental Health, Part B, 13(7-8), 579-603.