Chapter 3: Data Analysis

Summary

Utilizing nest survival data gathered over multiple years through an experimental design setup, this project aims to compare variables using a logistic exposure model approach. Through this analysis, I intend to pinpoint the factors influencing nest survival, ultimately informing the development of effective conservation strategies for supporting nesting grassland birds.

Objectives

We aim to understand how regenerative grazing and haying regimes influence grassland bird communities on working farms in Virginia.

• Questions:

  • Does the timing of management practices influence the reproductive success of grassland birds in fields under regenerative haying and grazing practices.

• Hypotheses:

  • Nest concealment hypothesis: nest success will be contingent on the timing and intensity of management, such as grazing intensity and the timing of haying. The basis for this hypothesis lies in our understanding that both grazing and haying directly modify vegetation structure. Reduced vegetation density around nests may expose nests, making them more detectable to predators, while denser vegetation may provide better concealment and protection.

  • Edge habitat hypothesis: landscape variables may better account for the variation in nest survival. Forest edges negatively influence nest survival as predators are more active around edges.

Methods

Here, I model nest survival as a function of nest site and treatment level habitat covariates and linear and quadratic time trends. I’ve used a logistic exposure model to calculate species specific daily survival rates (Shaffer 2004). The logistic exposure model uses a logit link function (loge[p/(1 - p)], where p is the probability of a success) and utilizes encounter history to model daily survival rates as a function of the explanatory variables. Given that nests are found at various stages, this approach accounts for the bias encountered with varying exposure.

Data Overview

• Response Variable
  • Nest monitoring data collected between 2020-2023
• Covariate Data
  • Random effects
  • Temporal effects
  • Nest site level
  • Treatment level
  • Landscape level

Click here for covariate definition at each scale

Variable	Type	Description
Random Effects
Nest ID	Random	Unique identification code. Used in all models to account for repeated measures that occur through monitoring nests.
Field	Random	Unique field identification codes used to assess random variation in nest survival across fields.
Farm	Random	Unique identification for each farm.
Geographic cluster	Random	Distinct clusters of nests used to assess landscape-level spatial autocorrelation
Year	Random	Random factor used to assess potential temporal autocorrelation in nest survival across study years.
Temporal
Julian	Continuous	Day of the year (January 1 = Day 1) of nest monitoring visit
Growing day	Continuous	Weather-based indicator for assessing crop development.
Stage	Factor	Dominant stage of nest (egg or nestling) during the exposure period
Nest Level
Robel	Continuous	Mean Robel reading at the nest from two cardinal directions. A measure of live/dead vegetation density directly at the immediate nest site.
Height	Continuous	Mean height of vegetation at the nest from two cardinal directions.
Grass %	Continuous	Estimated grass cover at nest
Forb %	Continuous	Estimated forb cover at nest
Thatch %	Continuous	Estimated dead vegetation cover at nest
Dist to perch	Continuous	Nest distance to nearest perch (fence, tree/shrub (>1m) (m)
Openness	Continuous	Clinometer angle reading and bearing to the tallest (i.e. maximum clinometer angle reading) horizon visible from the point survey
Treatment Level
Treatment	Factor	Type of manamgement type on a field for a partcular year (stockpiled, grazed, early hay, late hay)
Grass	Factor	Dominant grass in field: fescue, orchard, brome
Stocking density	Continuous	Livestock per unit of land area
Grazing days	Continuous	Number of days a particular field is grazed
Hay date	Continuous	Date when hay is cut and harvested in a particular field
Robel	Continuous	Mean Robel reading at the nest from two cardinal directions. A measure of live/dead vegetation density directly at the immediate nest site.
Height	Continuous	Mean height of vegetation at the nest from two cardinal directions.
Grass %	Continuous	Estimated grass cover at nest
Forb %	Continuous	Estimated forb cover at nest
Thatch %	Continuous	Estimated dead vegetation cover at nest
Landscape Level
Forest	Continuous	% of forest cover in landscape (multiple scales? TBD)
Grass	Continuous	% of grassland in landscape (multiple scales? TBD)
Edge	Continuous	Total amount of edge in landscape pertaining to five digitized cover types (forest, grassland, developed, agriculture, water) (multiple scales? TBD)
Dist to edge	Continuous	Distance to nearest edge habitat

Nest Data

Let’s take a peek at our nest data. We have 605 nests with complete data for modeling purposes. Nests were found through both behavioral cues and systematic searching (rope dragging/ stick swishing), with some found incidentally. Nests were monitored approximately every 4-7 days with minimal disturbance until the nest either fledged or failed. Cues such as observed flightless young, parents alarm calling, adults delivering food, and undiscarded fecal sacs were used along with the age of the brood to determine if a nest was successful.

Summary of Nest Data

Site	Count
Chancellors Rock	76
Glenmore	29
Hidden Creek	3
Little Milan	19
Oak Grove	113
Oak Spring	67
Over Jordan	6
Oxbow	370

Species	Failed	Successful
EAME	56	60
RWBL	208	197
SAVS	3	2
GRSP	3	10
BOBO	19	47

Landcover Data

I’m using the Chesapeake Bay Program’s One-meter Resolution Land Use/Land Cover (LULC) Data to extract landscape covariates. Let’s take a look at that.

Now let’s take a look at some of our covariates. A few covariates are heavily skewed with zeros and will need to be log transformed.

Let’s take take a look at collinearity.

I will be taking a step or hierarchical approach to modeling.

• Step 1 focuses on incorporating random effects to account for variability across different levels of the data.
• Step 2 adds temporal effects to capture changes over time.
• Step 3 examines single-scale effects, including nested scales, treatment scales, and landscape scales, to identify how different spatial levels influence the outcome.
• Step 4 integrates multi-scale effects by combining the most significant single-scale effects, allowing for a comprehensive analysis of interactions across different scales.