Data and code from Cappello et al. 2024. Ecosphere.

Data citation:
Cappello, C. D., K. V. Jacobson, J. T. Driscoll, and K. M. McCarty. 2024. Evaluating the effects of nest management on a recovering raptor using integrated population modeling (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11051071.


Contact:
Caroline Cappello
Email:
ORCID: https://orcid.org/0000-0002-2687-4019


Manuscript abstract:
Evaluating population responses to management is a crucial component of successful conservation programs. Models predicting population growth under different management scenarios can provide key insights into the efficacy of specific management actions both in reversing population decline and in maintaining recovered populations. Bald eagle (Haliaeetus leucocephalus) conservation in the United States has seen many successes over the last 50 years, yet the extent to which the bald eagle population has recovered in Arizona, an important population within the Southwest region, remains an area of debate. Estimates of the species’ population trend and an evaluation of ongoing nest-level management practices are needed to inform management decisions. We developed a Bayesian integrated population model (IPM) and population viability analysis (PVA) using a 36-year dataset to assess Arizona bald eagle population dynamics and their underlying demographic rates under current and possible future management practices. We estimated that the population grew from 87 females in 1993 to 211 females in 2022, an average yearly increase of 3%. Breeding sites that had trained personnel (i.e., nestwatchers) stationed at active nests to mitigate human disturbance had 28% higher reproductive output than nests without this protection. Uncertainty around population trends was high, but scenarios that continued the nestwatcher program were less likely to predict abundance declines than scenarios without nestwatchers. Here, the IPM-PVA framework provides a useful tool both for estimating the effectiveness of past management actions and for exploring the management needs of a delisted population, highlighting that continued management action may be necessary to maintain population viability even after meeting certain recovery criteria.


Description of the files:
These tables are a subset of data collected on bald eagles (Haliaeetus leucocephalus) between 1987 and 2022 in Arizona, USA. Data were collected by Arizona Game and Fish Department and their partners.


cappello_baldeagleipm.R
ipm & pva code


data_mr.csv
data used in the mark-resight-recovery submodel
EagleID = Unique identifier for each banded eagle included in the mark-recapture-recovery submodel
1987-2022 = For each year, a code (1-13) is given to represent the observed state of each individual. State definitions are given in the model code and manuscript text


data_abun.csv
data used in the abundance submodel
Year = Calendar year
OccupiedNestCount = Total number of occupied nesting territories found in Arizona, USA during surveys between January and June each year


data_prod.csv
data used in the nest productivity submodel
Year = Calendar year
BANumber = Unique identifier for each nesting territory
NumFledged = Number of nestlings that fledged from each occupied nesting territory in a given year. Ranges from 0 to 3.


eagle_constants.rds
list of model constants
nbrood = total number of occupied nests
nsites_prod = number of monitored nesting territories
nyear_prod = number of years in the productivity dataset
NW_prod = binary representing whether a nesting territory had nestwatchers (1) or not (0) in that year
propNW = average proportion of nests that had nestwatchers from 1993 to 2022
propNW_t = proportion of nests that had nestwatchers in each year from 1993 to 2022
closures = binary representing whether a nesting territory had an area closure (1) or not (0) in that year
propClos = average proportion of nests that had a closure from 1993 to 2022
propClos_t = proportion of nests that had a closure in each year from 1993 to 2022
yrID_prod = index of year corresponding to each row in the productivity dataset
siteID_prod = index of nesting territory ID corresponding to each row in the productivity dataset
first = first year that an individual was observed
first_state = first state that an individual was observed in
sex = sex of the individual: male (1) or female (0)
nyears_mr = number of years in the mark-resight-recovery dataset
nind = number of individuals in the mark-resight-recovery dataset
nyears = number of years in the abundance dataset
sex_ratio = number by which to divide productivity, to account for the skewed sex ratio at fledging
nyears_proj = number of years projected forward in the PVA
scenarios = number of management scenarios tested in the PVA