Protection of endangered species has become controversial in the US. Biodiversity declines can be difficult to monitor, because animals can move broadly, and many avoid human observation. [Quaternary megafauna collapses in the Americas and Australia coincided with the appearance of humans; surviving species are small, fast, often nocturnal, and often restricted to regions of limited human pressure.] Attribution of declines to habitat degradation and/or climate change is difficult when there are multiple abuses with interacting effects. Conservation actions are controversial when stakeholders such as environmentalists, land owners, and industry perceive them to be “zero-sum” remedies–any benefit to one group must come at the expense of others.
In this vignette we consider evidence for bird declines and litigation surrounding the endangered species act (ESA), from the standpoint of stakeholders involved in litigation since the Obama administration.
Timeline for vignette
day 1: Evidence for bird declines
day 2: Discussion of readings
days 3-4: in groups vignette on wood thrush analysis
days 5-7: Endangered species act, litigation, and stakeholder debate
Resources
Citizen science data
Files on Sakai
Resources/code/clarkFunctions2022.r
Resources/data/bbsExample.Rdata
In the media
Birds Are Vanishing From North America (New York Times)
Silent Skies: Billions of North American Birds Have Vanished (Scientific American)
To save birds, should we kill off cats? (National Geographic)
Science resources
Decline of the North American avifauna (Science)
STATE OF THE WORLD’S BIRDS. (BIRDLIFE INTERNATIONAL)
The Audubon Climate Report A study of citizen science data, with species distributions models, to predict species at risk.
Connectivity of wood thrush breeding, wintering, and migration sites, Stanley et al on tracking wood thrush populations combined with BBS (Cons Biol).
Declines in insectivorous birds are associated with high neonicotinoid concentrations, Hallmann et al. find a relationship between bird declines and surface-water concentrations of insecticides.
Conservation Status of North American Birds in the Face of Future Climate Change Langham et al. report on the Audubon study.
Breeding bird survey, explanation and data, with supporting data
The IUCN Red List is a widely-referenced effort that divides species into categories of risk: Not Evaluated, Data Deficient, Least Concern, Near Threatened, Vulnerable, Endangered, Critically Endangered, Extinct in the Wild and Extinct.
Objectives
Introduce background issues on biodiversity threats from global change
Gain an exposure to concepts in modeling and computation, including data sources and quality, sources of uncertainty
From last time: Post-mortem on big oil debate
Abandoning our positions for the debate, an objective discussion. If there is no disagreement that corporate actions harm public health, where does the buck stop? What is the role of the courts, particularly when municipalities have no other options? Are there legislative options that have not been tried or should be considered again?
We’ll take the first part of today’s meeting to wrap up some of these lingering issues.
For today
Organizing background research
The questions below are for discussion at next class meeting. Use the references given above or any additional ones to draft several sentences for each question. In your groups, divide up the questions to cover them all:
The IUCN Red List of Threatened Species: what are the categories of risk, and how are they determined?
What is an IBA, and how is it determined?
At what time of year are the BBS surveys, the Christmas bird counts, and eBird done, and why is this important?
How are the counts done and why?
How are following practices contributing to bird vulnerability?
- agriculture
- logging
- other forms of habitat loss
- invasive species, including why they can be especially problematic on oceanic islands
- cats
Name three actions that can help relieve extinction risks.
What variables are used to predict future bird distributions? How well do you expect these models to capture the role of diet items (seeds, fruits, invertebrates) and natural enemies (other bird species, snakes, cats).
In Langham et al., the term climate sensitivity is a term is based on fractions of the current ranges that would be lost. Defend and/or criticize this interpretation.
How do habitat affinities defined by IUCN and Audubon differ?
Each person does this: Using the BBS data described below select a species for study, identify where it is increasing or decreasing. If you select a rare species, you will not see any pattern. If this happens, try a different species. Based on your research, speculate on causes for increases or declines.
Introduce data (this vignette)
For next class
Next meeting will be a discussion.
Post your written responses to sakai.
breakout groups to synthesize research from your group.
Present findings based on the research done by group members. The individuals will contribute with additional details on their specific analyses on BBS data.
What do the data say?
We will look at changes in abundance in eastern North America using the Breeding Bird Survey (BBS) and eBird using maps and analysis of trends. Read about how this this done at the BBS web site. Today we consider BBS data.
First install several R packages that will be needed to generate maps:
install.packages('MBA')
install.packages('RColorBrewer')
install.packages('maps')Here I make them available in my workspace:
library(MBA)
library(RColorBrewer)
library(maps)
library(repmis)Data for maps can be loaded from this file, available on sakai:
d <- "https://github.com/jimclarkatduke/gjam/blob/master/bbsExample.rdata?raw=True"
source_data(d)## Downloading data from: https://github.com/jimclarkatduke/gjam/blob/master/bbsExample.rdata?raw=True## SHA-1 hash of the downloaded data file is:
## 85072ed0a96a5c39685819e5b0a6a8f5fa629e5c## [1] "x" "y"The message from the console tells me that I have loaded two objects, x and y. Use the R functions dim, head, and tail, to identify columns in these objects.
The object y is an observations-by-species matrix, where each row is a route and year, and each column is a species. We will discuss the structure of x and y in class.
Here is a list of species contained in y:
specNames <- colnames(y)
specNames[1:10]## [1] "WesternGrebe" "Red-neckedGrebe" "EaredGrebe" "Pied-billedGrebe"
## [5] "CommonLoon" "PacificLoon" "Red-throatedLoon" "BlackGuillemot"
## [9] "Razorbill" "ParasiticJaeger"I want to generate change maps for a species. One of the species in y is the WoodThrush. I choose this species.
I need several functions from this file on sakai:
source('clarkFunctions2022.r')The migratory wood thrush in R
The migratory wood thrush population is declining. There are multiple threats, both here in the temperate zone and in their tropical wintering grounds in Central America. Stanley et al. (2015) quantified declines in wood thrush abundance in the breeding range by region, and they related it to forest cover. They used tracking data to determine if regional differences in the US might be explained by the fact that birds summer and wintering grounds were linked. I used this example to examine changes in abundance using the breeding-bird survey (BBS) data. Specifically, I generate maps over time to ask whether or not wood thrush populations could be in decline.
I specify year intervals to generate maps. The data run from 1995 to 2015. The sequence of years I specify will be used to extract abundances between these years.
specName <- 'WoodThrush'
years <- c(1995, 2005, 2015)The function mapBBSTime in clarkFunctions2022.r will draw maps:
mapBBSTime(specName, years, x, y)
Red in the upper plots mean abundant. The zero contours for no difference in the bottom plots show the break-even locations, with red meaning increase, blue decrease. These lines separate regions of positive and negative change.
For the last question, you will interpret maps like this for your species to speculate on causes of increases or declines and on its relationship to you research.