Research Question

My question remained relatively the same between all of the exercises: how does the spatial pattern of surface water availability affect duck departure from their wintering grounds? Each exercise was a step towards answering this question. For Exercise 1, my goal was to determine the departure date and location for a subset of my data. For Exercise 2, I determined the statistical relationship between my variable A (departure date) and variable B (surface water) and repeated this process for Exercise 3 to further examine the relationship at different spatial scales.

Dataset

I examined GPS tracking locations for four species of migratory dabbling ducks (northern pintail, wigeon, green-winged teal, and northern shoveler) collected between 2015-2022. Ducks were captured and marked with GPS backpack transmitters in the Central Valley during fall and early winter; prior to the initiation of spring migration for their northern breeding grounds (typically Alaska or Prairie Pothole Region, Canada depending on species). Locations were collected at 30-min to 6-hour intervals. I focused on a subset from 2020 for analysis that only included ducks that departed from the Sacramento Valley, northern portion of the Central Valley, for spring migration, February 1 to June 1. Only ducks that exhibited migratory behavior (i.e. departed Central Valley boundary line). A total of 50 ducks departed the Sacramento Valley during the spring of 2020.

Hypotheses

My hypothesis was that departure timing would be affected by surface water (i.e. habitat) availability. I predicted that as proximate surface water on the landscape decreased, the probability of duck departure would increase. It would also be expected that the relationship would change based on spatial scale since ducks are likely making decisions based their immediate surroundings.

Analysis Approaches

Exercise 1

To estimate the departure dates and locations I used maximum displacement methods. I calculated the daily movement distances (total distance between consecutive points per day) of each individual and created a threshold distance that would define migration movement. I validated each departure date based on the last date the bird was located in the Central Valley. Departure location was determined as the last stationary location before the individual initiated migratory flight movement.

Exercise 2 and 3

I used logistic regression to assess the probability of departure given the proximate amount of surface water on the landscape. I used Google Earth Engine to obtain satellite imagery covering the extent of the Sacramento Valley for 2020 and calculate NDWI for each image. I randomly selected non-departure locations to categorize my response variable 0 (non-departure) or 1 (departure). Then I used 2km radius buffers to estimate the mean NDWI around each departure and non-departure location. I performed logistic regression to determine the relationship and repeated the process with a larger buffer size (4km radius) to examine at a larger spatial scale.

Results

I produced statistical relationships and visual maps for my results.

Exercise 1

Exercise 2 and 3

What did you learn from each of the analyses you conducted (i.e., from each exercise)? 

Exercise 1: I was able to define migration based on a distance threshold and the relationship between surface water and probability of departure. I learned that inter-basin (i.e. exploratory) movements were under 150 km while local movements were only 3 km on average. Any distances greater than 150 km were considered migratory movements and were validated in ArcGIS Pro.

Exercise 2 and 3: Logistic regression analysis provided the probability of departure associated with surface water. I found that duck departure and NDWI are negatively correlated at both spatial scales based on a significant p-value (p<0.05) and negative coefficient. The increased presence of surface water reduces the odds of duck departure. However, ducks are more likely making decisions based on smaller spatial scales of proximate habitat conditions and for future analysis, I will be using the average daily local movement of my marked birds calculated for each year and species.

Significance

Understanding the drivers of spring migration departure timing in dabbling ducks using the Central Valley is important for regional conservation planners. My results will provide empirical duck behavior metrics to be included in bioenergetic models used for assessing the impacts of changing climatic conditions on migratory waterfowl. Improved accuracy of model performance will ensure that the habitat needs of target waterfowl populations are being met, which is critical due to persistent water shortages that threaten to diminish vulnerable wetland habitats on their wintering grounds. It will inform resources managers of the potential impacts that water allocation and decision making will have on duck migration behavior in an increasingly arid system.

Software learning

I gained experience using Google Earth Engine, a software that I have never used before. I was able to obtain satellite imagery of my study site and calculate NDWI for different time periods across spring migration. One of the most important steps for my research that I accomplished in this course was identifying and defining migration. It may seem simple, but all subsequent analysis for my thesis depends on this step. I will continue to validate this process as I move forward; however, it has provided the necessary framework to select departure dates and locations that will pave the way for my research.

Statistics learning

Most importantly, I learned logistic regression techniques and how to apply it to my research question. Understanding how to use logistic regression opens the door for exploring many more relationships between departure and changes in surface water.

Evolving question and future directions

My question actually hasn’t changed that much! My research question will likely become more refined and specific as I continue to learn more about my study system. Overall this was exactly the task that I was hoping I could accomplish in this course – I was able to explore the relationship between departure and water availability. It also got me thinking deeper about the different strengths of relationships each dabbling duck species may have with water availability on the landscape based on diet, migration behavior, etc. Even further, I am thinking it would be useful to explore the different wetland types (i.e. seasonal wetlands, flooded agriculture, semi-permanent wetlands) that may influence the timing of departure for spring migration as well. For example, flooded agricultural fields will likely experience more dramatic drawdown periods earlier in the season and species that tend to use those types (i.e. pintail) will may have a stronger relationship to changes in water availability. While species that are utilizing semi-permanent wetlands may not have a strong relationship to changes in water availability. Also, it is clear that the water availability on the landscape changes throughout the season and it will likely be impacted by precipitation trends as well.  This was a great first start to exploring this relationship, and I am looking forward to seeing the trends across years.