Are plains zebras really the leaders of the migrations?
The core theme underlying Project Plains Zebra is that the individual plains zebras are the leaders of the migrations on the African savannah, to the exclusion of other migrating mammals. We show that there is both scientific and anecdotal evidence (link to video) to support this conclusion, including: (1) The plains zebras are part of all the migrations; (2) The plains zebras have very large non-migratory home ranges; (3) The plains zebras are known to appear first during the migration; (4) The plains zebras appear to “remember” their migratory route; and (5) The neural navigation system within the plains zebra brain is the largest of all mammals. Despite this supportive evidence, it is of importance to clearly demonstrate this aspect of the life of plains zebras, as this will in many ways be central to the development of appropriate conservation and management strategies aimed at conserving the biodiversity of the African Savannah Ecosystem. To achieve this, we need to examine many individuals of different species, not only the plains zebras, but also the other species that participate in these migrations. In the Serengeti migration, in addition to plains zebras, blue wildebeest (Connochaetes taurinus), Grant’s gazelles (Nanger granti), and Thomson’s gazelles (Eudorcas thomsonii) form substantial proportions of the migrating mammals. In the two migrations known in South Sudan, in addition to the plains zebra, white-eared kob (Kobus leucotis), tiang antelopes (Damaliscus lunatus tiang) and Mongalla gazelles (Eudorcas albonotata) also form significant proportions of the migration. What we hope to do is to collar numerous individuals of all species with GPS collars that will last for over 3 years. By outfitting these species with collars, and tracking their location over this period (i.e., obtaining a GPS location for each collared animal every 30 minutes over this 3+ year period), we can reveal which species is consistently leading the migrations and how species that follow these leaders interact with the leaders. By adapting state-of-the-art machine learning analyses and algorithms, we can determine which animals are those that lead these migrations with great specificity. In addition, these analyses can extract information about specific individual animals, both within and between species, and determine whether there are clear interdependent relationships between individuals. For example, does a specific individual tiang antelope follow a specific plains zebra during a migration, and does it follow the same plains zebra in subsequent years. In addition, the GPS data can be augmented through the addition of environmental data (such as weather and geographic features), allowing for even deeper insights into the inter-relationships between migrating animals. The data that will be generated from these studies has the potential to provide a definitive answer regarding the core theme of this project, and as such forms one of the central pillars of the project. In addition, the data generated can provide deep insights into the movement of the various migrating species and how these relate to each other, and how environmental conditions impact the migrations.
Hypothesis: The plains zebra will consistently be observed to be the leaders of the migrations, with the movements of the co-migrating species showing distinct fidelity to an individual, or group, of plains zebras.
Aim: We aim to attached GPS collars and track up to 200 plains zebra, 50 blue wildebeest, 50 Grant’s gazelles, 50 Thomson’s gazelles, 50 white-eared kob, 50 tiang antelopes, and 100 Mongalla gazelles for a period of 3+ years in the four known migrations. We believe that this series of tracking studies will provide the data needed in order to support or negate the hypothesized role of the plains zebra in the great migrations.
Specific Objective 1: We aim to collar up to 50 plains zebra in the Chobe River region of north-east Botswana and follow their migrations over a period of 3+ years. Using GPS data (position recorded every 30 minutes) and spatiotemporal analysis, combined with analysis of the prevailing environmental conditions, we aim to determine what environmental factors may influence this migration.
Specific Objective 2: We aim to collar 50 plains zebra, 50 blue wildebeest, 50 Grant’s gazelles, and 50 Thomson’s gazelles, involved in the annual Serengeti migration and track them for 3+ years. Using GPS data (position recorded every 30 minutes) and spatiotemporal analysis, combined with analysis of the prevailing environmental conditions, we aim to determine which species are leading and which are following in this migration and what environmental factors may influence this migration.
Specific Objective 3: We aim to collar 50 plains zebra, 50 white-eared kob and 50 Mongalla gazelles involved in the annual north-east South Sudan migration and track them for 3+ years. Using GPS data (position recorded every 30 minutes) and spatiotemporal analysis, combined with analysis of the prevailing environmental conditions, we aim to determine which species are leading and which are following in this migration and what environmental factors may influence this migration.
Specific Objective 4: We aim to collar 50 plains zebra, 50 tiang antelopes, and 50 Mongalla gazelles involved in the annual north-west South Sudan migration and track them for 3+ years. Using GPS data (position recorded every 30 minutes) and spatiotemporal analysis, combined with analysis of the prevailing environmental conditions, we aim to determine which species are leading and which are following in this migration and what environmental factors may influence this migration.
Methods:
Collaring: 450 adult animals (with comparable representation of the sexes) will be fitted with a GPS collar (https://awt.co.za/product/) with a battery life of 3+ years. The animals will be immobilized using a dart gun (e.g., Plangsangmas et al., 2022) from a helicopter and rapidly located by ground crews. The collars will be fitted, the immobilization reversed (e.g., Plangsangmas et al., 2022), and the animals allowed to return to their natural habitat.
Data Analysis: The GPS location recorded every 30 minutes by the collars will be received through a daily satellite download. This data will then be collated, and the locations and movements of the collared animals plotted, both as individuals, comparing individuals, and as groups. As we plan to record over a period exceeding 3 years, the data should cover 3 full migrations. In addition, we aim to correlate GPS location with current meteorological conditions as recorded at nearby weather stations and geographical/geological data through the migration paths. By using time-varying mixed models of location density (within and across individuals) we will be able to derive robust migratory patterns of plains zebra populations. These will then be linked to corresponding models from co-migrating species using directed inference approaches such as Granger causality mapping using the either the location- or direction-space models as input time-series. Thereby we should be able to draw causal inference and determine which animals are those that lead these migrations. In addition, these analyses can extract information about specific individual animals, both within and between species, and determine whether there are clear interdependent relationships between individuals.