Sustainable Management of Rangelands: An Assessment of Invasion Co asion Cover Trajectories and Their Contribution t ories and Their Contribution to Invasion Management in Marigat Sub-County, Kenya
Abstract
Invasive alien species have complex spatiotemporal patterns of spread beyond geographical and
jurisdictional boundaries. This calls for a coordinated management approach that is spatially explicit,
extends beyond individual plot levels, and incorporates land users’ perceptions and decisions. This
study, therefore, aims at assessing spatiotemporal invasion trajectories of the invasive tree Prosopis
juliflora in Baringo County, Kenya, and evaluating their possible relation to land users’ management
decisions. Pre-classified land cover data over a seven-year time period (1988–2016) were reclassified
based on the presence or absence of P. juliflora and integrated into ArcGIS to produce P. juliflora cover
trajectories for analysis. The spatiotemporal analysis of Prosopis invasion dynamics yields trajectories
that can be linked to underlying land users’ management decisions. Areas that remained free of Prosopis
since their first clearance were primarily areas where the invasion would cause the highest loss in terms
of income or opportunity costs; areas that were never cleared since they were first invaded tended to be
areas where no one could be personally held accountable for their management, while the abandonment
of management followed by re-invasion appeared to be linked to different drivers, including
diversification of livelihoods and lower market prices for horticultural products. Our findings indicate
that invasion trajectories are useful in informing existing management strategies to adopt context-based
invasive species management practices. The study recommends scaling up the trajectory analysis
approach to be replicated in large-scale invasion management strategies. Since it requires considerable
finances and time to conduct such analyses on raw satellite imagery, we suggest further research on
how to simplify the approach to make it easily and efficiently replicable for large-scale applications.