Data from: Management inputs, site conditions, and fire history shape outcomes of invasive plant control and native recovery

Millions of dollars and countless hours are spent on invasive plant
management, and the field of invasion ecology has gained increasing
attention in recent decades. Yet, despite these efforts to control and
understand plant invasions, successful management is often elusive.
Budgetary constraints are a common factor limiting invasive plant
management programs, and therefore, optimizing control strategies are
essential. However, such optimization requires data on management inputs
and outcomes, and these data are often missing, lacking, or underutilized.
To address this knowledge gap and identify predictors of successful weed
control in natural areas, we examined nearly 20 years of invasive plant
treatment data in the world’s largest urban national park – Santa Monica
Mountains National Recreation Area of southern California. We resurveyed
279 sites that had undergone control in the past two decades, collecting
data on the abundance of native and invasive plant species to evaluate
long-term management outcomes. We used multiple statistical approaches to
identify management inputs and site characteristics that are predictors of
eradication, invasive plant cover, and native species recovery. We found
that the greater the initial size or percent cover of an infestation, the
lower the probability of eradication. We also found that weed infestations
on steeper slopes and in areas that have burned more frequently are less
likely to be eradicated. Promisingly, our results also showed that greater
reductions in invasives generally benefited native plant communities,
though not in all cases. These analyses also highlighted that persistence
is key; more frequent treatments (both chemical and nonchemical) and
greater investment of labor resulted in larger reductions in invasive
plants. Our results highlight how site characteristics and limited
resources can complicate invasive plant management, while demonstrating
the value of analyzing treatment and monitoring data to identify effective
control strategies and guide adaptive management decisions.