State of the Birds 2017: Background

Black-capped chickadee © Bill Thompson, USFWS

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In previous editions of the State of the Birds we used data that had been collected over decades to estimate the trends in the populations of the breeding birds in Massachusetts. From there we developed an evidence-based ranking system to describe our Conservation Concern for each species.

In this edition of State of the Birds we are considering the future, and adding information to our Conservation Concern score by developing a Climate Change Vulnerability score. We ask two questions:

  • In light of the additional stress of climate change, how will our breeding birds fare through the year 2050?
  • Which species are projected to benefit from the warming climate, and which face additional stress?

This is more than an academic exercise. Developing climate change-driven projections helps us to refine our conservation priorities—and ultimately helps us to set up on-the-ground advocacy, land conservation, science, and education programs.

We looked into the future using a technique called climate envelope modeling. This technique requires a series of steps, which we summarize below. Ultimately, this technique creates two maps of the range of the suitable climate for each species—one map for the range of current climate suitability, and a second map for the range of future climate suitability.


Creating Climate Envelope Models for Breeding Species

Modeling Current Climate Conditions

In order to give each breeding species a Climate Change Vulnerability score we needed to develop a model that, using real bird location data, defined the current climate conditions at occupied points on a map for each species. The current climate data were drawn from a dataset that interpolates average monthly temperature and precipitation data obtained from weather stations in eastern North America for the years 1960–1990. The current bird location data were drawn from eBird records in eastern North America during the months of June and July, are assumed to represent primarily breeding birds, and were not constrained by the year of data collection.

The analysis used the eBird data in conjunction with elevation and 19 climate variables to create a complex equation that predicts the likelihood of a species’ occurrence in each of nearly 13 million 1 km2 cells. For our analysis we clipped the results to restrict the maps to the state of Massachusetts, although the models were built using data from the entire northeastern United States.

The current maps show, for each cell, the likelihood that climatic conditions are suitable for a species. Assuming that these values reflect the likelihood of actual presence of a species, we then calculated the average values across the state for the species’ current distribution. The analysis also indicates which of the input variables were of most importance in the resulting equation.

Modeling Future Climate Conditions

Next, we substituted the projected climate values for each of the 19 climate variables for the year 2050 into the equation describing current conditions and evaluated how the likelihood of finding each species in each cell might change in the future. We used future climate data that reflects a high emissions scenario (Hadley GEM-2), which assumes that greenhouse gas emissions will continue to increase. As described above, we then made new maps that have a value for each cell, clipped the results to the Massachusetts borders, and calculated the average likelihood of the presence of a species within Massachusetts in the future. These two averages (current and future) were then compared to evaluate projected changes in the distribution of each species within the state.

What the Maps Do & Don't Show

Our models, maps, and statistics estimate only the suitability of the climate across the state for each species. It is essential to remember that birds are not directly responding to the environmental variables used in this analysis. Rather, the environmental variables are surrogates for underlying changes in habitat that typically affect distributional patterns of birds.

These projections do not include the possibility that some species may be able to adapt to live in climate envelopes that they do not currently occupy. They also do not directly estimate the current or future condition of the vegetation or other components of breeding habitat, the footprint of future development, the effect of greenhouse gas emissions that differ from the Hadley GEM-2 scenario, or the impacts of sea level rise. Further, the models do not portray current or future population densities or sizes, and we did not estimate a threshold value of likelihood of occurrence below which a species likely would disappear from a cell, or the entire state. Additionally, sea level rise is not a variable in the projections.

As the climate shifts toward envelopes that are increasingly unsuitable for a species, we anticipate that population declines will be incremental. For some species this is a critical window of opportunity for us to engage in conservation measures (i.e., increasing redundancy, resiliency, and resistance) that would slow the decline of or buffer the species from the most detrimental climate change effects. Such measures could allow some individuals to persist in areas where they are projected to have a low likelihood of occupancy.


How to Read the Maps

Current model of black-capped chickadee
Black-capped chickadee (current)
Current model of ruffed grouse
Ruffed grouse (current)

What the Numbers Mean

The climate envelope maps show the probability that climate will be suitable at each 1-km2 cell in Massachusetts. It is important to note that the range of the climate suitability probabilities differs between species.

For example, the probability that climate is suitable for black-capped chickadees (top right map) ranges from 0.0 to 0.55 across the state, with the darker green indicating a higher (0.55) probability.

However, the probability that climate will be suitable for ruffed grouse (bottom right map) ranges from 0.30 to 0.60 across the state. This means that the maps cannot be directly compared among species since the range of climate suitability probability differs for each species.

Limitations of the Model

Our models indicate that the current climate of the entire state is suitable for both the black-capped chickadee and the ruffed grouse.

Ruffed grouse © Matt Soberg
Ruffed grouse © Matt Soberg

But we know that their actual distributions are quite different: the black-capped chickadee is widespread, and the ruffed grouse is restricted to specific habitat, so their actual footprints are not perfectly captured by the climate envelope model.

These climate envelope maps show only the suitability of the climate conditions, which acts as an imperfect surrogate for the habitat required by a species. As such, the maps represent an overestimate of the range of a species, as we see with the ruffed grouse.

You can think of these maps as a best-case scenario for each species—if there is habitat available, the climate will be suitable. But the reverse is not true—if habitat is not available it is unlikely that a species will occur, even if the climate is suitable.


Climate Variables

The following 19 bioclimatic predictors were used to construct our climate envelope models. The variables were created by U.S. Geological Survey and U.S. Department of the Interior. Explanations of the variables were provided by M.S. O’Donnell & D.A. Ignizio. 2012. Bioclimatic predictors for supporting ecological applications in the conterminous United States: U.S. Geological Survey Data Series 691, 10 p.

Bio 1—Annual Mean Temperature

The average of the averages of temperature for each month.

Bio 2—Annual Mean Diurnal Range

The average of the monthly temperature ranges (monthly maximum temperature minus monthly minimum temperature).

Bio 3—Isothermality

Isothermality quantifies how much the day-to-night temperatures oscillate relative to the summer-to-winter (annual) oscillations. This variable could indicate that a species’ distribution is influenced more or less by the monthly temperature fluctuations than by the temperature fluctuations over the whole year.

Bio 4—Temperature Seasonality (Standard Deviation)

The amount of variation in temperature over a given year based on the standard deviation of monthly temperature averages.

Bio 5—Max Temperature of the Warmest Month

The maximum monthly temperature occurrence over a given year or averaged span of years. This information is useful when examining whether species distributions are affected by warm temperature anomalies throughout the year.

Bio 6—Min Temperature of the Coldest Month

The minimum monthly temperature occurrence over a given year or averaged of years. This information is useful when examining whether species distributions are affected by cold temperature anomalies throughout the year.

Bio 7—Annual Temperature Range

A measure of temperature variation over the year. This information is useful when examining whether species distributions are affected by ranges of extreme temperature conditions.

Bio 8—Mean Temperature of Wettest Quarter

This quarterly index approximates mean temperatures that prevail during the wettest three months of the year. This variable can be useful in examining how such environmental factors may affect species seasonal distributions.

Bio 9—Mean Temperature of Driest Quarter

This quarterly index approximates mean temperatures that prevail during the driest three months of the year. This variable can be useful in examining how such environmental factors may affect species seasonal distributions.

Bio 10—Mean Temperature of Warmest Quarter

This quarterly index approximates mean temperatures that prevail during the warmest three months of the year. This variable can be useful in examining how such environmental factors may affect species seasonal distributions.

Bio 11—Mean Temperature of Coldest Quarter

This quarterly index approximates mean temperatures that prevail during the coldest three months of the year. This variable can be useful in examining how such environmental factors may affect species seasonal distributions.

Bio 12—Annual Precipitation

The sum of all total monthly precipitation values. Annual total precipitation approximates the total water inputs and is therefore useful when ascertaining the importance of water availability to a species distribution.

Bio 13—Precipitation of Wettest Month

The total precipitation that prevails during the wettest month of the year. This variable is useful if extreme precipitation conditions during the year influence a species potential range.

Bio 14—Precipitation of Driest Month

The total precipitation that prevails during the driest month of the year. This variable is useful if extreme precipitation conditions during the year influence a species potential range.

Bio 15—Precipitation Seasonality (CV)

A measure of the variation in monthly precipitation totals over the course of the year. Since species distributions can be strongly influenced by variability in precipitation, this index provides a percentage of precipitation variability where larger percentages represent greater variability of precipitation.

Bio 16—Precipitation of Wettest Quarter

This quarterly index approximates total precipitation during the wettest three months of the year. This variable can be useful in examining how such environmental factors may affect species seasonal distributions.

Bio 17—Precipitation of Driest Quarter

This quarterly index approximates total precipitation that prevails during the driest three months of the year. This variable can be useful in examining how such environmental factors may affect species seasonal distributions.

Bio 18—Precipitation of Warmest Quarter

This quarterly index approximates total precipitation that prevails during the warmest three months of the year. This variable can be useful in examining how such environmental factors may affect species seasonal distributions.

Bio 19—Precipitation of Coldest Quarter

This quarterly index approximates total precipitation that prevails during the coldest three months of the year. This variable can be useful in examining how such environmental factors may affect species seasonal distributions.