An individual-based model trained on multiple data sources estimates population connectivity and facilitates aggregation of harvest management units

Management boundaries are often delimited by political and social factors, whereas animal movements are affected by ecological and geophysical constraints. Thus, understanding connectivity among distinct management units is of considerable importance, particularly for harvested species, where quotas set in ignorance of connectivity may fail to meet management goals. Our goal was to construct an individual-based model (IBM) to better understand Wild Turkey movements at large scales, benefiting from multiple data sources that are often available for harvested species. We built an IBM describing spring seasonal movements of Wild Turkeys, using data from ringed, radio-, and GPS-marked turkeys captured in Maine, USA. Our IBM accommodated variation in individual turkey response to landscape connectivity metrics and identified emergent migratory connectivity dynamics among harvest management regions. We calculated a low degree of connectivity among wildlife management districts (WMD) which, in combination with the substantial number of boundary crossings observed, indicated a more diffuse distribution of turkeys among WMDs. Nevertheless, the number of turkeys moving between districts provided a clear delineation of where immigration was strongest, identifying which WMDs should be managed as singular population units. This approach has widespread utility for any species or system where harvest management decisions are made at finer spatial scales than the movement dynamics affecting population processes.

Methods
We captured 890 turkeys from December through March of 2018-2020 and marked each with one or two unique leg rings imprinted with information for reporting recovered birds. From late April to early June of 2018-2020, Maine allowed the recreational harvest of bearded turkeys in most management districts. When ringed turkeys were reported, we asked hunters to identify the town of harvest, which we used to define the locations of recoveries. Of captured turkeys, we fit a subset of females with either a GPS or VHF transmitter to monitor seasonal movements and nesting activity. We programmed GPS transmitters to record one nighttime roost location and hourly daylight locations each day from November 1 through July 31 each year. We relocated radio-marked turkeys from April through July of 2018-2020 to identify nests following methods in Gonnerman, Shea, et al. (2022).

We classified movement between winter and spring home ranges by comparing capture locations to either harvest or nest locations for the same turkey in the year of capture, as data collected in the following years included additional seasons of movement. We defined a seasonal movement as any instance where a harvested or nesting turkey was observed greater than 5.5km from its given capture location, which corresponds to the radius of the circle of area equal to the average Maine town. This was chosen to correspond with the most coarse observational scale at which harvest data was collected and while conservative ensured that all potential behaviors were captured.