DOI: 10.5061/dryad.wdbrv15tn
title: Explaining and predicting animal migration under global change

description: Many migratory species are declining due to global environmental change.
 Yet, their complex annual cycles make unravelling the impacts of potential
 drivers such as climate and land-use change on migrations a major
 challenge. Identifying where, when, and how threatening processes impact
 species’ migratory journeys and population dynamics is crucial for
 identifying effective conservation actions. Here, we describe how a new
 migration modelling framework – Spatially-explicit Adaptive Migration
 Models (SAMMs) – can simulate the optimal behavioural decisions required
 to migrate across open land- or seascapes varying in character over space
 and time, without requiring predefined behavioural rules. Models of
 adaptive behaviour have been used widely in theoretical ecology but have
 great untapped potential in real-world contexts. Applying adaptive
 behaviour models across open environments will allow users to explore
 flexibility in how migratory strategies respond to environmental change
 and the consequences of migrants not being able to adapt to change. We
 outline how SAMMs can be used to model migratory journeys through aerial,
 terrestrial, and aquatic environments, demonstrating their potential using
 a case study on the common cuckoo (Cuculus canorus) and comparing modelled
 to observed behaviours. SAMMs offer a tool to identify the key threats
 faced by migratory species and to predict how they will adapt to future
 migratory journeys in response to changing environmental conditions.
description: Example code for running Spatially-explicit Adaptive Migration
 Models (SAMMs) is provided in R scripts
description: # Explaining and predicting animal migration under global change This
 README file was generated on 10th November 2023 by Christine Howard ##
 GENERAL INFORMATION 1. Title of the Dataset: Explaining and predicting
 animal migration under global change 2. Author Information A. Principal
 Investigator Contact Information Names: Steve G. Willis Institution:
 Durham University Address: Department of Biosciences, Durham University,
 South Road, Durham, UK, DH1 3LE Email:
 [s.g.willis@durham.ac.uk](mailto:s.g.willis@durham.ac.uk) B. Associate or
 Co-Investigator Contact Information Names: Christine Howard Institution:
 Durham University Address: Department of Biosciences, Durham University,
 South Road, Durham, UK, DH1 3LE Email:
 [christine.howard@durham.ac.uk](mailto:christine.howard@durham.ac.uk) 3.
 Date of data collection (single date, range, approximate date): 2012-2016
 4. Geographic location of data collection: Europe and Africa 5.
 Information about funding sources that supported the collection of the
 data: Natural Environment Research Council, Award: NE/T001038/1, Swiss
 National Science Foundation, Award: SNF 31BD30_184120, Belgian Federal
 Science Policy Office, Award: BelSPO BR/185/A1/GloBAM-BE, Dutch Research
 Council, Award: NWO E10008, Academy of Finland, Award: 326315, National
 Science Foundation, Award: NSF 1927743 ## SHARING/ACCESS INFORMATION 1.
 Licenses/ restrictions placed on the data: CC0 1.0 Universal (CC0 1.0)
 Public Domain Dedication license 2. Links to publications that cite or use
 the data: Howard, C., Mason, T.H.E., Baillie, S., Border, J., Hewson, C.,
 Houston, A., Pearce-Higgins, J., Bauer, S., Willis, S., Stephens, S.,
 (2023) Explaining and predicting animal migration under global change,
 Diversity and Distributions 1. Links to other publicly accessible
 locations of the data: None 2. Links/ relationships to ancillary data
 sets: None 3. Was data derived from another source? Yes A. If yes, list
 source (s):
 [https://www.ospo.noaa.gov/Products/land/gvi/](https://www.ospo.noaa.gov/Products/land/gvi/). 4. Recommended citation for this dataset: Howard, C., Mason, T.H.E., Baillie, S., Border, J., Hewson, C., Houston, A., Pearce-Higgins, J., Bauer, S., Willis, S., Stephens, S., (2023). Explaining and predicting animal migration under global change [Dataset]. Dryad. [https://doi.org/10.5061/dryad.wdbrv15tn](https://doi.org/10.5061/dryad.wdbrv15tn) ## DATA & FILE OVERVIEW 1. File List: A) NDVI NOAA STAR 500km mean 2012 - 2016 EQA.csv 2. Relationship between files, if important: None 3. Additional related data collected that was not included in the current data package: None 4. Are there multiple versions of the dataset? No A. If yes, name of file(s) that was updated: NA i. Why was the file updated? NA ii. When was the file updated? NA ## DATA - SPECIFIC INFORMATION FOR: NDVI NOAA STAR 500km mean 2012 - 2016 EQA.csv 1. Number of variables: 370 2. Number of cases/rows: 274 3. Variable List: * x: Longitude at centre of 500km hexagonal grid cell * y: Latitude at centre of 500km hexagonal grid cell * X: Data indexing code (relict) * id: Cell identification code * land: Land classification (1= land, 0 = sea) * X1 - X365: Mean 2012- 2016 interpolated daily NDVI for the hexagonal grid cell (X1 = 1st January, X365 = 31st December) 4. Missing data codes: NA 5. Specialised formats or other abbreviations used: NA