Received: 25 June 2024 | Accepted: 11 October 2024
DOI: 10.1111/2041-210X.14455
APPLICATION
mignette: An R package for creating and visualizing migratory network models
Matthew G. DeSaix1	| Christen M. Bossu1	| Julie C. Hagelin2 | Ryan J. Harrigan3 |
James F. Saracco4	|	Marius Somveille1,5	| Caz M. Taylor6	| Kristen C. Ruegg1

Correspondence
Matthew G. DeSaix
Email: mgdesaix@gmail.com
Present address
Matthew G. DeSaix, United States Department of Agriculture, Wildlife
Services, National Wildlife Research Center, Fort Collins, Colorado, USA
Funding information
Alaska Department of Fish and Game, Grant/Award Number: 23- 011; Solar
Energy Technologies Office, Grant/Award Number: DE- EE0009005
Handling Editor: Edward Codling

1Biology Department, Colorado State University, Fort Collins, Colorado, USA; 2Alaska Department of Fish and Game, Threatened, Endangered and Diversity Program, Fairbanks, Alaska, USA; 3Center for Tropical Research, Institute of the Environment and Sustainability, University of California, Los Angeles, California, USA; 4The Institute for Bird Populations, Petaluma, California, USA; 5Department of Genetics, Evolution and Environment, Centre for Biodiversity and Environment Research, University College London, London, UK and 6Department of Ecology and Evolutionary Biology, Tulane University, New Orleans, Louisiana, USA
Abstract
1.		A prominent challenge for managing migratory species is the development of conservation plans that accommodate spatiotemporally varying distributions throughout the year. Migratory networks are spatially- explicit models that incorporate migratory assignment and seasonal abundance data to define patterns of connectivity between stages of the annual cycle. These models are particularly useful for widespread application because different types of migratory data can be used to quantify individual and population-level movement across the annual cycle of migratory species. While there are clear benefits of combining migratory assignment and abundance data for the development of conservation strategies, there is a concurrent need for corresponding user- friendly software to facilitate the integration of these data for conservation.
2.		Here, we present mignette (migratory network tools ensemble), an R package for developing migratory network models to estimate network connectivity among migratory populations. We demonstrate the functionality of mignette with three empirical examples that highlight the use of different types of tracking data for migratory assignment.
3.		mignette facilitates the modelling of migratory networks by providing R functions to: (1) define breeding and nonbreeding nodes, (2) assemble abundance and assignment data and (3) model the migratory network. Additionally, mignette provides R functions to visualize modelled migratory networks.
4.		With increasing availability of migratory assignment and abundance data, mi- gnette represents a valuable tool for developing effective conservation strategies for migratory species.
Matthew G. DeSaix and Christen M. Bossu contributed equally.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
© 2024 The Author(s). Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society.

Methods Ecol Evol. 2024;15:2216–2225.

2216 | wileyonlinelibrary.com/journal/mee3

KEYWORDS
Bayesian methods, connectivity, conservation prioritization, genoscape, JAGS, migratory ecology, network model

1		| INTRODUCTION
Migratory species occupy multiple habitats throughout the year that can be geographically separated by vast distances. A prominent challenge for the conservation and management of these species is addressing population decline in the face of spatiotemporally varying distributions and incomplete knowledge of migratory connectivity (Runge et al., 2014). While research addressing population declines in migratory species has historically been hindered by a bias of research toward the breeding season (Marra et al., 2015), advancements in the development of species abundance data across the annual cycle as well as migratory assignment and tracking methods (hereafter referred to as ‘assignment’) are facilitating full annual cycle research aimed at informing conservation efforts. Although there are clear benefits of combining assignment and abundance data for the design of conservation strategies, the availability of user-friendly software that facilitates the modelling of migratory population dynamics with these data has been limited (but see Cohen et al., 2018).
Current methods excel at quantifying the ‘strength’ of migratory connectivity of a species—a metric that defines the correlation of distances between individuals or populations across stages of the annual cycle (Cohen et al., 2018). The strength of migratory connectivity is a valuable metric for conservation and management efforts as it informs the extent to which individuals or populations experience similar environmental conditions or stressors throughout the year, and such methods are currently implemented in R packages (e.g. MigConnectivity; Cohen et al., 2018). Here, we present mignette (migratory network tools ensemble), a complementary approach to existing methods by quantifying the ‘pattern’ of connectivity (i.e. network connectivity), which describes the proportion of a species' global population that migrates between breeding and nonbreeding populations. mignette expands upon a previously published network model (Ruegg et al., 2020), which was designed initially for use with genetic assignment data, by accommodating any type of migratory assignment or tracking data (from here on referred to as assignment data) that can be used to confidently assign individuals to cross-season populations. In this paper, we walk- through a description of mignette and then provide three examples demonstrating the use of different types of assignment data (genetic only, banding only, genetic and geolocator combined), typically available for migratory species.
2		| PACKAGE DESCRIPTION
mignette is a package in the R programming language (R Core Team, 2023) applicable for any study of migratory species that has basic information on migratory movement and abundance. We outline a general workflow for estimating network connectivity with mignette (Figure 1): (1) define breeding and nonbreeding nodes, (2) assemble abundance and assignment data and (3) model network connectivity. Assignment data is directional—that is, there is an ‘encounter’ season and a ‘recovery’ season (Procházka et al., 2017; Figure 2). For assignments generated from extrinsic markers, individuals are initially captured or tagged during the encounter season, and during the recovery season they are re-captured or re-sighted. In the case of geolocators or GPS trackers, the recovery season is where individuals are inferred to have been. For genetic data, the encounter season is nonbreeding and the recovery season is the breeding origin inferred from a genetic analysis of the individual using population assignment methods.
mignette implements a Bayesian connectivity model using JAGS, a software for Markov Chain Monte Carlo simulation (Depaoli et al., 2016; Plummer, 2004) and runs JAGS using the R packages rjags (Plummer, 2019) and jagsUI (Kellner, 2019). mignette is designed for working with spatial data in R using terra (v1.7- 39; Hijmans, 2021) and data manipulation is performed with tidyverse (Wickham et al., 2019). The mignette functions and data sets are provided in Supporting Information (Tables S1 and S2). Information on how to install and use mignette is provided in an online vignette (https:// mgdes aix. github.i o/conne ctivi ty- book/ ).
2.1		| Step 1: Define nodes
The migratory network model is a graph-based model where populations are spatially distinct regions defined as nodes and the links between nodes (network connectivity) are defined as edges (Taylor & Norris, 2010). In mignette, we provide vector data of conservation regions, stored as conservation_regions, which can be used to delineate terrestrial breeding or nonbreeding nodes in the Western Hemisphere (Figure S1). These regions were delineated based largely on US Environmental Protection Agency Level I ecoregion boundaries and geopolitical borders (Commission for Environmental Cooperation, 1997; Griffith et al., 1998). Development of mignette was motivated by our studies of Neotropical migratory landbirds, and we combined some smaller adjacent and biogeographically similar regions, as well as some larger regions containing relatively few nonbreeding Neotropical migratory landbird species. Geopolitical borders were used to split some of the larger conservation regions, as such divisions may be more practical for conservation purposes. Although these conservation regions may be also applicable for other terrestrial migratory species, mignette users can alternatively provide vector data for spatially delineating nodes that are relevant to their study system and objectives.
For mignette users with genetic data, breeding nodes can also be defined using population structure analyses that spatially delineate

			

			

			

			

		

						

						

FIGURE 1 Workflow for creating a migratory network model in mignette. Data shown are from the American Redstart example in the text.

populations (Ruegg et al., 2020). While it is outside the scope of mi- gnette to perform population genetics analyses, there are numerous reviews on approaches for delineating populations with genetic data (e.g. Hohenlohe et al., 2021).
2.2		| Step 2a: Assemble relative abundance data
We provide functions in mignette that take range- wide abundance estimates in raster format and calculate the relative abundance for each node (Table S2). From such raster data, the mignette user can calculate the relative abundance in each node either by supplying a vector (a terra spatVector object) with polygons delineating population boundaries using the mignette function get_vector_abunds(), or by supplying a raster (a terra spatRaster object where each layer is a raster for a given node) in which each cell has a probability of membership to a given node and using the mignette function get_raster_abunds(). This latter function is used for genetically distinguished populations, such as a genoscape (Ruegg et al., 2020), which are created from spatially determining population genetic

FIGURE 2 Examples of the directionality of assignment data and the corresponding model in mignette. (a) Genetic data from American Redstart (AMRE) uses the breeding assignment model (BR); (b) Banding data from Osprey (OSPR) uses the nonbreeding assignment model (NB); (c) Genetic data (breeding assignment) and geolocator data (nonbreeding assignment) for Swainson's Thrush (SWTH) is the bidirectional model (BR- NB).

structure. get_raster_abunds()uses the membership probabilities of different populations from the raster data to then calculate the abundance of each population by weighting each cell in the abundance raster by the corresponding cell of membership probability to the given node.
2.3		| Step 2b: Assemble assignment data
In mignette, we specifically refer to ‘assignment data’ as a matrix specifying the number of individuals sampled from each node in the ‘encounter’ season that are assigned to nodes in the ‘recovery’ season (Procházka et al., 2017). An assignment matrix requires data from a single type of tracking method in a single direction. One current limitation is that the network model does not account for node assignment uncertainty. Therefore, the assignment data solely consists of individuals that can be confidently assigned to a single node and it is essential for users to consider appropriate sample selection and node size delineation to address the geographic uncertainty in their assignment method. Individuals with low confidence of assignment to a given node should be pruned. Node size can also be increased to accommodate the geographic uncertainty of assignment.
Since the network model accounts for sampling effort in the encounter season, we provide different model variations in mignette based on the directionality of the data: Breeding Assignment Model (BR) = a single assignment matrix where the nonbreeding season is ‘encounter’ (columns) and the breeding season is ‘recovery’ (rows); Nonbreeding Assignment Model (NB) = a single assignment matrix where the breeding season is ‘encounter’ (rows) and the nonbreeding season is ‘recovery’ (columns); and Multi-directional Assignment Model (BR- NB) = two assignment matrices, one of the BR model type and one of the NB model type (Figure 2).
While sampling design recommendations for a migratory connectivity study will likely vary according to the particular system (see Vickers et al., 2021), we performed basic simulations to highlight how sample sizes, in relation to different migratory connectivity scenarios, affect estimates of network connectivity (Figures S2 and S3; see Supporting Information for details). We found that sampling as few as five individuals per encounter node can result in consistently accurate estimates of connectivity (i.e. 95% credible intervals overlapping with the true connectivity value) for weak migratory connectivity scenarios (Figure S3). In scenarios where an encounter node has strong connectivity to a single recovery node and weak connectivity to other recovery nodes, the network model can also consistently estimate the strong connectivity values accurately with sample sizes as low as 5 but may inaccurately estimate the weak connectivity values as zero, even when sample sizes are as high as 10 or 20 individuals, due to a lack of sampling of less frequent individuals that migrate to the weaker connectivity node (Table S3).
2.4		| Step 3: Model network connectivity
mignette expands on a published network model (Ruegg et al., 2020; Supporting Information). In short, we assumed individuals assigned to recovery season nodes sampled at a given encounter season node (specified in the assignment matrix) followed a multinomial distribution with probabilities proportional to the corresponding recovery season column. Similarly, we assumed the individuals found at each recovery season node from a given encounter season node followed a multinomial distribution with probabilities equal to the corresponding rows of encounter season nodes weighted by the sampling effort at each recovery season node. Sampling effort was estimated as the proportion of the total number of individuals sampled at a given encounter season node. Finally, we assumed the population size indices (i.e. abundance) for the encounter and recovery season nodes were drawn from a multinomial distribution with node probabilities derived from the connectivity matrix.

The migratory network is created using the mignette function run_network_model(). This function is a wrapper for the jagsUI au- tojags() function (Kellner, 2019) and will accept the same arguments for the MCMC parameters (Table S1). Additionally, the run_net- work_model() function requires the abundance data, assignment data and model type to be specified by the corresponding arguments. The function returns a list object with four components. The first component, [[“conn”]], is an R tibble object of the mean connectivity estimates. These values are interpreted as the proportion of individuals within the global population that migrate between the corresponding nodes, such that all values in the connectivity matrix sum to one. The second component, [[“jags_out”]], is a list object of the output from jagsUI autojags(). The final two components, [[“brnode_names”]] and [[“nbnode_names”]], store the node names corresponding to the rows and columns, respectively, of the connectivity matrix.
After running the network model, convergence and fit need to be assessed to ensure that the model provides reasonable inferences (Conn et al., 2018). Gelman-Rubin diagnostic statistics (Gelman & Rubin, 1992), R, for all parameters of the connectivity matrix can be accessed from the autojags() output. R values <1.1, combined with traceplots showing that stationary distributions have been reached, can provide evidence of adequate convergence. MCMC specifications can be adjusted and the model re-run to address any apparent convergence issues (Kellner, 2019). In mignette, we implement posterior predictive checks using the Freeman-Tukey discrepancy statistic to compare the fit of the observed data to data simulated from the model (Conn et al., 2018). The function get_FT_fit() produces a density plot of the Freeman-Tukey statistics for qualitatively comparing observed and simulated data, and outputs the Bayesian p-value of the goodness- of- fit.
Visualization of different features of the migratory network is implemented in mignette. Statistical uncertainty in the network connectivity estimates is characterized by the credible intervals provided in the network model output for the conn parameter. The mignette function plot_network_CI() plots the mean and 95% credible intervals for network connectivity which allows users to assess the uncertainty in the estimates. The mean connectivity estimates can be plotted as a migratory network using a two-step process of preparing the network output with the function net_ create() and then plotting the network with net_draw(). The network model can produce extremely small connectivity values for unsampled migratory connections and setting the connected_tol parameter in net_create will set a threshold for plotting only robust connections.
3		| EXAMPLE APPLICATIONS
Here, we create migratory networks for three species of migratory birds to highlight the three directionality models in mignette. In the first example, we demonstrate the use of mignette with genetic assignment data from a study on the American Redstart (Setophaga ruticilla; DeSaix et al., 2023). In the second example, we use breeding Osprey (Pandion haliaetus) banding and re- encounter records (Celis- Murillo et al., 2022). Finally, we combined geolocator data (Cormier et al., 2013; Delmore et al., 2012) with genetic assignment data (Turbek et al., 2024) to estimate network connectivity for Swainson's Thrush (Catharus ustulatus). In the subsequent sections, all species' examples are described while code examples are provided only for American Redstart.
3.1		| Data preparation
For American Redstart, we used the five genetically delineated populations from DeSaix et al. (2023) as breeding nodes (Table 1). For nonbreeding nodes, we used the intersection of sampling coordinates for 135 individuals from the stationary nonbreeding range with the conservation regions (Table 1; Figure 2a). For Osprey, we intersected conservation regions with coordinates for 683 individuals banded during the breeding season to define five breeding nodes and defined eight nonbreeding nodes from the resight coordinates (Table 1; Figure 2b). For Swainson's Thrush, we used three genetically distinct breeding populations of Swainson's Thrush as breeding nodes and identified six nonbreeding nodes from both genetic data (n = 122) and geolocator data (n = 15) (Table 1; Figure 2c).
To calculate relative abundance, we obtained seasonal abundance data from the eBird Status and Trends dataset (Fink et al., 2022) using the ebirdst package in R (Strimas- Mackey et al., 2022). To estimate the breeding node abundance for American Redstart and Swainson's Thrush, we used the get_raster_ abunds() function in mignette with a raster object of the breeding population structure (i.e. genoscape) and the ebirdst raster object of the breeding range abundance. For the breeding node abundance of Osprey and the nonbreeding node abundance of all species, we used the get_vector_abunds()function with the node vector object and an ebirdst raster object of the breeding and nonbreeding range abundance as inputs, respectively.
3.2		| Creating the migratory network
The assignment and abundance data are the two main inputs into the migratory network model (Figure 1). For the American Redstart example, individuals were sampled on the nonbreeding grounds and assigned to breeding populations so we use the BR Model.
network_model <- run_network_model(abundance=amre_abundance,
nb2br_assign=amre_assign,
model=“BR”)
We used similar coding to run the model for Osprey and Swainson's Thrush, specifying the corresponding abundance

TABLE 1 Node labels, names, season and species for the three example species: American Redstart (AMRE), Osprey (OSPR) and Swainson's Thrush (SWTH).
Genetic node label	Genetic node name	Season	Species	
A	Pacific Northwest	Breeding	AMRE, SWTH	
B	Basin Rockies	Breeding	AMRE	
C	Southern Temperate	Breeding	AMRE	
D	Northern Temperate	Breeding	AMRE	
E	Maritime Provinces	Breeding	AMRE	
F	Pacific Coast	Breeding	SWTH	
G	East/West Boreal	Breeding	SWTH	
Conservation node label	Conservation node name	Season	Species	
1	Northwestern Forested Mountains (NWFM)	Breeding	OSPR	
2	Great Plains (GP)	Breeding	OSPR	
3	Northern Forests (NF)	Breeding	OSPR	
4	Eastern Temperate Forests (ETF)	Breeding	OSPR	
5	Southeastern US (SEUS)	Breeding, Nonbreeding	OSPR	
6	Northwest Mexico (NWM)	Nonbreeding	OSPR	
7	Southwest Mexico (SWM)	Nonbreeding	OSPR	
8	Highland Interior Mexico (HIM)	Nonbreeding	SWTH	
9	Atlantic Lowland Mexico (ALM)	Nonbreeding	AMRE, OSPR, SWTH	
10	Highland Central America (HCA)	Nonbreeding	OSPR, SWTH	
11	Lowland Central America (LCA)	Nonbreeding	AMRE, OSPR, SWTH	
12	Andes	Nonbreeding	OSPR, SWTH	
13	Amazon/Orinoco- Northern Uplands (AONU)	Nonbreeding	AMRE, OSPR, SWTH	
14	Caribbean (CAR)	Nonbreeding	AMRE, OSPR	

(Tables S4 and S5) and assignment data (Tables S6 and S8). The Osprey connectivity model is based on breeding season banding data (with band resights on the nonbreeding grounds), therefore we use the NB Model (Table S6), whereas the Swainson's Thrush model uses both genetic and geolocator data (deployed on breeding grounds with inferred nonbreeding locations), so we use the BR-NB Model, which requires two separate assignment matrices (breeding and nonbreeding assignment; Tables S7 and S8).
For genetic data, we followed standard practice in genetic assignment tests to use assignment consistency of ≥0.8 as the threshold for confidently assigning an individual to a node (DeSaix et al., 2023, 2024). Assignment from banding/re-encounter data can reasonably be assumed accurate so long as individual identification is not an issue. For geolocator data we used the centroids of the inferred nonbreeding location to assign conservation regions. While light-level geolocator inference can have high geographic uncertainty, our data had inferred locations on the periphery of the Swainson's Thrush nonbreeding range and inspection of error bars and kernel densities supported confident assignment of each individual to a single node. In the absence of being able to reliably prune individuals for assignment uncertainty, we recommend generating assignment matrices from assignment probabilities and estimating connectivity for each generated matrix to assess that similar results are produced (Supporting Information).
3.3		| Model checking of the migratory network
We check that all parameters converged by looking at the Rhat column in network_model$jags_out as well as visually assessing chains for convergence with jagsUI::traceplot(network_model$jags_out, “conn”) (Figure S4). Below, the posterior predictive check of the American Redstart model shows largely overlapping distributions of the Freeman-Tukey discrepancy statistics for the observed and simulated data, with a corresponding Bayesian p-value of 0.45—indi- cating sufficient goodness of fit (Figure 3a).
get_FT_fit(network_model)
The Osprey and the Swainson's Thrush models also converged, and had overlapping Freeman-Tukey statistic distributions in the posterior predictive check, suggesting reasonable goodness of fit

(a) Posterior Predictive Check with Freeman-Tukey statistic
Bayesian p-value = 0.448

Data

Observed
Simulated

FIGURE 3 American Redstart network model results from (a) posterior predictive check from the get_FT_fit() function and (b) 95% credible intervals from plot_network_CI() where shape specifies estimates with a posterior mass at zero.
Breeding
WB
BR
NT
ST
MP
Zero
FALSE
TRUE
Nonbreeding

(Osprey: Bayesian p- value = 0.23 [Figure S5]; Swainson's Thrush: Bayesian p- value = 0.45 [Figure S6]).
3.4		| Visualizing the migratory network
We plot the mean and 95% CI of the connectivity estimates with the plot_network_CI() function (Figure 3b).
plot_network_CI(network_model)
We also plot the connectivity estimates as a network (Figure 1; Table 2).
amre_net <- net_create(network_model) net_draw(amre_net)
Visualization of the migratory network can be readily expanded upon for users to produce graphical interpretations to best suit their needs (Figure 4).
4		| CONCLUSIONS
With the development of mignette, we present a tool for quantifying population-level migratory connections across the annual cycle. This tool can provide crucial information for conservation

(a)

TABLE 2 American Redstart mean connectivity estimates, where rows refer to breeding nodes and columns refer to nonbreeding nodes.
Node names are provided in Table 1.
	ALM	LCA	HCA	CAR	AONU	
WB	0.14212	0.13901	0.01346	0.00507	0.00002	
BR	0.00010	0.00021	0.00008	0.02082	0.00004	
NT	0.05680	0.00041	0.00013	0.38276	0.00006	
ST	0.00003	0.01345	0.03618	0.03902	0.06453	
MP	0.00013	0.00036	0.00022	0.00002	0.08496	

FIGURE 4 Alluvial plots of the connectivity estimates for the three species examples (a–c). The bars are coloured by breeding node and the width of the bars corresponds to the mean connectivity estimates.

decision-making concerning migratory species (Ruegg et al., 2020) and supplements existing methods that quantify the strength of migratory connectivity (Cohen et al., 2018). mignette allows visualization of the resulting migratory network using both the standard network plot from mignette (Figure 1) and the alluvial plot (Figure 4), both which can provide clarity in identifying migratory connections. These visualizations represent an important first step in the process of developing full annual cycle conservation strategies for migratory species. For instance, wildlife managers in northwestern North America (e.g. Alaska or British Columbia) can discern that their breeding American Redstarts are part of the larger Western Boreal (WB) breeding population and thereby focus on the nonbreeding connections with WB, which is connected almost equally to the Atlantic Lowland Mexico and Lowland Central America conservation regions (47.5% and 46.5%, respectively, of the WB breeding population). Consequently, should persistence of breeding populations become a priority in the future, conservation efforts could involve the development of partnerships with conservation practitioners in the two nonbreeding regions. Similarly, the Boreal population of Swainson's Thrush are primarily connected to two conservation regions, with 79.3% of Boreal breeding individuals estimated to migrate to the Andes and 18.0% to the Orinoco/Amazon region. In contrast, if the persistence of Boreal breeding Osprey was of concern, the estimated diffuse network connectivity suggests that conservation efforts could involve partnerships across a much broader area of Central America, North America and the Caribbean.
The mignette R package provides a valuable addition to the conservation practitioners toolbox, since mapping migratory connectivity is a critical step when attempting to implement management strategies for migratory species. Future development of mignette will include extending the model to more than two stages of the annual cycle as well as incorporating individual assignment uncertainty. These extensions will further improve the generalizability of the network model and allow users to retain a larger sample size without pruning individuals due to low assignment probability.
AUTHOR CONTRIBUTIONS
Kristen C. Ruegg, Caz M. Taylor, James F. Saracco, Ryan J. Harrigan, Christen M. Bossu and Matthew G. DeSaix conceived the study. Kristen C. Ruegg and Julie C. Hagelin acquired the funding for the study. The specific methods were developed by Caz M. Taylor, James F. Saracco, Ryan J. Harrigan and Marius Somveille. Matthew G. DeSaix developed the R package, and the package was tested by Christen M. Bossu and Ryan J. Harrigan. Christen M. Bossu and Matthew G. DeSaix performed the analyses and led the writing of the paper. All authors contributed critically to the drafts and gave final approval for publication.

ACKNOWLEDGEMENTS
Funding for development of mignette was provided by a Cooperative Agreement with the Alaska Department of Fish and Game's Threatened, Endangered and Diversity Program (23-011) to Kristen Ruegg and Julie Hagelin. Funds and support for this research were provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Solar Energy Technologies Office (Grant #DE-EE0009005) to Ryan Harrigan, Christen Bossu and Kristen Ruegg. The views expressed herein do not necessarily represent the views of the U.S. DoE. Any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
CONFLICT OF INTEREST STATEMENT
The authors declare no conflicts of interest.
PEER REVIEW
The peer review history for this article is available at https:// www. webof scien ce. com/ api/ gatew ay/ wos/ peer- review/ 10. 1111/ 2041210X. 14455.
DATA AVAILABILITY STATEMENT
The mignette package is available at https://github. com/ mgdes aix/mignette and archived on Zenodo (https://doi.org/10.5281/ zenodo.13931780; DeSaix, 2024). An online vignette is available at https:// mgdes aix. github.i o/conne ctivi ty- book/. Data used in this manuscript is publicly available from previous studies for the different species: American Redstart (DeSaix et al., 2023); Osprey (Celis-Murillo et al., 2022); Swainson's thrush (Cormier et al., 2013; Delmore et al., 2012; Turbek et al., 2024).
ORCID
Matthew G. DeSaix https://orcid.org/0000-0002-5721-0311
Christen M. Bossu https://orcid.org/0000-0002-0458-9305
James F. Saracco https://orcid.org/0000-0001-5084-1834
Marius Somveille https://orcid.org/0000-0002-6868-5080
Caz M. Taylor https://orcid.org/0000-0002-8293-1014
REFERENCES
Celis- Murillo, A., Malorodova, M., & Nakash, E. (2022). North American bird banding program dataset 1960–2022: U.S. Geological Survey data release. https://doi. org/ 10. 5066/ P9BSM38F
Cohen, E. B., Hostetler, J. A., Hallworth, M. T., Rushing, C. S., Sillett, T. S., & Marra, P. P. (2018). Quantifying the strength of migratory connectivity. Methods in Ecology and Evolution, 9(3), 513–524. https:// doi. org/ 10. 1111/ 2041- 210X. 12916
Commission for Environmental Cooperation. (1997). Ecological regions of North America: Toward a common perspective. Commission for Environmental Cooperation.
Conn, P. B., Johnson, D. S., Williams, P. J., Melin, S. R., & Hooten, M. B. (2018). A guide to Bayesian model checking for ecologists. Ecological Monographs, 88(4), 526–542. https://doi. org/ 10. 1002/ ecm. 1314
Cormier, R. L., Humple, D. L., Gardali, T., & Seavy, N. E. (2013). Light-l evel geolocators reveal strong migratory connectivity and within-winter movements for a coastal California Swainson's thrush population. The Auk, 130(2), 283–290. https:// doi. org/ 10. 1525/ auk. 2013. 12228
Delmore, K. E., Fox, J. W., & Irwin, D. E. (2012). Dramatic instraspecific differences in migratory routes, stopover sites and wintering areas, revealed using light-l evel geolocators. Proceedings of the Royal Society B: Biological Sciences, 279(1747), 4582–4589. https://doi. org/ 10. 1098/ rspb. 2012. 1229
Depaoli, S., Clifton, J. P., & Cobb, P. R. (2016). Just another Gibbs sampler (JAGS): Flexible software for MCMC implementation. Journal of Educational and Behavioral Statistics, 41(6), 628–649. https://doi. org/ 10. 3102/ 10769 98616 664876
DeSaix, M. (2024). mignette (v1.2.0). Zenodo. https:// doi. org/ 10. 5281/ zenodo. 13931780
DeSaix, M. G., Anderson, E. C., Bossu, C. M., Rayne, C. E., Schweizer, T. M., Bayly, N., Narang, D., Hagelin, J., Gibbs, H., Saracco, J., Sherry, T., Webster, M., Smith, T., Marra, P., & Ruegg, K. C. (2023). Low- coverage whole genome sequencing for highly accurate population assignment: Mapping migratory connectivity in the American redstart (Setophaga ruticilla). Molecular Ecology, 32(20), 5528–5540. https://doi. org/ 10. 1111/ mec. 17137
DeSaix, M. G., Rodriquez, M. D., Ruegg, K. C., & Anderson, E. C. (2024). Population assignment from genotype likelihoods for low-coverage whole- genome sequencing. Methods in Ecology and Evolution, 15(3), 493–510. https://doi. org/ 10. 1111/ 2041- 210X. 14286
Fink, D., Auer, T., Johnston, A., Strimas- Mackey, M., Ligocki, O. R., Hochachka, W., Jaromczyk, L., Rodewald, A., Wood, C., Davies, I., & Spencer, A. (2022). eBird status and trends [dataset]. https:// doi. org/ 10. 2173/ebird st. 2021
Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 7(4), 457–472.
Griffith, G. E., Omernik, J. M., & Azevedo, S. H. (1998). Ecological classification of the Western hemisphere. United States Environmental Protection Agency.
Hijmans, R. J. (2021). terra: Spatial data analysis (R package version 1.4- 22) [computer software]. https:// CRAN. R- proje ct. org/ packa ge= terra
Hohenlohe, P. A., Funk, W. C., & Rajora, O. P. (2021). Population genomics for wildlife conservation and management. Molecular Ecology, 30(1), 62–82. https://doi. org/ 10. 1111/ mec. 15720
Kellner, K. (2019). jagsUI: A wrapper around “rjags” to streamline “JAGS” analyses (R package version 1.5.1) [computer software]. https:// CRAN. R- proje ct. org/ packa ge=j agsUI
Marra, P. P., Cohen, E. B., Loss, S. R., Rutter, J. E., & Tonra, C. M. (2015). A call for full annual cycle research in animal ecology. Biology Letters, 11(8), 20150552. https:// doi. org/ 10. 1098/ rsbl. 2015. 0552
Plummer, M. (2004). JAGS: Just another Gibbs sampler [computer software].
Plummer, M. (2019). rjags: Bayesian graphical models using MCMC (R package version 4- 10) [computer software]. https:// CRAN. R- proje ct. org/ packa ge= rjags
Procházka, P., Hahn, S., Rolland, S., van, H., Csörgő, T., Jiguet, F., Mokwa, T., Liechti, F., Vangeluwe, D., & Korner- Nievergelt, F. (2017). Delineating large-scale migratory connectivity of reed warblers using integrated multistate models. Diversity and Distributions, 23(1), 27–40. https://doi. org/ 10. 1111/ddi. 12502
R Core Team. (2023). R: A language and environment for statistical computing [computer software]. R Foundation for Statistical Computing. https:// www. R- proje ct. org/
Ruegg, K. C., Harrigan, R. J., Saracco, J. F., Smith, T. B., & Taylor, C. M. (2020). A genoscape-network model for conservation prioritization in a migratory bird. Conservation Biology, 34(6), 1482–1491. https:// doi. org/ 10. 1111/cobi. 13536
Runge, C. A., Martin, T. G., Possingham, H. P., Willis, S. G., & Fuller, R. A. (2014). Conserving mobile species. Frontiers in Ecology and the Environment, 12(7), 395–402. https://doi. org/ 10. 1890/ 130237
Strimas- Mackey, M., Ligocki, S., Auer, T., & Fink, D. (2022). ebirdst: Tools for loading, plotting, mapping and analysis of eBird Status and Trends

| 2225

data products [computer software]. https:// cornel llab oforni thol ogy. github.i o/ ebird st/
Taylor, C. M., & Norris, D. R. (2010). Population dynamics in migratory networks. Theoretical Ecology, 3(2), 65–73. https:// doi. org/ 10. 1007/ s1208 0- 009- 0054- 4
Turbek, S. P., Bossu, C., DeSaix, M., Rodriguez, M., Rayne, C., Anderson, A., Bayly, N., Paxton, E., González-Prieto, A., Narang, D., Smith, T., Somveille, M., Gómez Villaverde, S., Whitfield, M., Winker, K., & Ruegg, K. C. (2024). Data from: Migratory connectivity influences the strength of local adaptation in migratory birds. Dryad Digital Repository, https://doi. org/ 10. 5061/ dryad.j m63x sjm9
Vickers, S. H., Franco, A. M., & Gilroy, J. J. (2021). Sensitivity of migratory connectivity estimates to spatial sampling design. Movement Ecology, 9, 1–12. https:// doi. org/ 10. 1186/s4046 2- 021- 00254 - w
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T., Miller, E., Bache, S., Müller, K., Ooms, J., Robinson, D., Seidel, D., Spinu, V., … Yutani, H. (2019). Welcome to the Tidyverse. Journal of Open Source Software, 4(43), 1686. https:// doi. org/ 10. 21105/j oss. 01686
SUPPORTING INFORMATION
Additional supporting information can be found online in the Supporting Information section at the end of this article.
Data S1: Additional details of analyses and model.
How to cite this article: DeSaix, M. G., Bossu, C. M., Hagelin, J C., Harrigan, R. J., Saracco, J. F., Somveille, M., Taylor, C. M., & Ruegg, K. C. (2024). mignette: An R package for creating and visualizing migratory network models. Methods in Ecology and Evolution, 15, 2216–2225. https://doi.org/10.1111/2041- 210X.14455