Explore 30 years of data collected at the Palomarin Field Station
August 14, 2018
Point Blue Conservation Science has been monitoring the rapidly changing coastal scrub ecosystem surrounding the Palomarin Field Station (Palo) in the Point Reyes National Seashore, Marin County, CA since 1966. Since its genesis, the banding program at Palo has monitored landbird response to local and regional environmental change, striving to gain a better understanding of the relationship between birds and their environment. With our long term data sets, we are able to look back at our history in order to predict how birds will respond to a changing environment in the future. Below are two graphics depicting just some of the data taken over half a century of monitoring at Palo. These are shown for the purposes of exploring the data in raw form, with some of the extremes detailed in the text below. Other analyses (previous and future ones) parse out these patterns in different ways to answer specific questions about trends and the relationship between weather and bird captures.
Use the sliders at the bottom of each graph to explore different time periods, from the entire data set to individual years.
The graph below depicts the daily high and low temperatures and daily rain accumulated taken using a standardized protocol at Palomarin. Rain accumulated is measured each day at mid-day, so daily rain accumulated depicts the previous day’s afternoon and evening’s rainfall through that of the current morning. We summarized weather data collected following similar protocols since 1975 until 2018.
The overall warmest months (calculated by averaging the daily high temperatures) at Palo were September 1997 with an average high temp of 78.8°F (26.0°C), September 1983 with average high temp of 77.8°F (25.4°C), and August 1992 with an average high temperature of 77.6°F (25.3°C). However, two of the hottest days ever recorded at Palo were both in September 2017, with a record high temperature of 100.4°F (38.0°C) on September 1 followed by 98.6°F (37°C) on September 2.
The tables below highlight some rainfall highs and lows at Palo (excluding 1974-75 and 2017-18 rain years). Annual rain totals are measured in “water years” from July to the following June in order to capture the full extent of the winter rains, our rainiest season. With our daily rainfall data, Point Blue researchers have found interesting relationships between previous winter rainfall and the productivity and survivorship of song bird populations during the following breeding season. You can read more about these trends and others here (see Young and adult Song Sparrows will respond differently to climate change. Global Change Biology, 2014).
The below graphic depicts monthly capture rates at the Palomarin Field Station from 1986-2016. The capture rate is calculated as the number of birds caught (including banded, re-captured, and unbanded birds) at Palo per 100 net hours (1 net open for one hour = 1 net hour; a full day of banding at Palo with all 20 mist nets open for the full six hours is 120 net hours). The banding program is just one way we monitor avian productivity and survivorship at Palo.
Months with highest average capture rate were October 2013 with 29 birds per 100 net hours, December 2013 with 26 birds per hundred net hours, and September 1987 with 25 birds per hundred net hours. The most birds caught per month were September 1987 with 908 birds, October 1990 with 878 birds, and September 2000 with 767 birds. The lowest capture rates were February 2016, February 2015, and February 2013 all with 2 birds caught per hundred net hours. The lowest number of birds caught per month were February 2016 with 24 birds, February 2013 with 31 birds, and February 2015 with 33 birds.
About this page:
This blog post and these graphics were compiled by Mike Mahoney as part of his internship capstone project, with assistance from Kristy Dybala. These interactive graphs were created using RStudio with the packages dygraphs for time series graphs. RStudio is a free and open-source software for programming in R, a programming language for statistical computing and graphics.