New publication: MPNV Models Help Predict Post-Treatment Needs in Grassland Restoration

Shrub removal is widely used to restore and conserve species-rich open habitats. However, sustaining these ecosystems after initial clearing often demands costly and repeated post-treatment efforts. A new study published in Restoration Ecology explores how estimates of vegetation self-sustainability—derived from Multiple Potential Natural Vegetation (MPNV) models—can guide expectations about the intensity of such post-treatments.

The researchers tested the hypothesis that if the target grassland vegetation is not self-sustainable, restoration will require more intensive maintenance. Using field data from Hungarian grassland restoration projects, the study assessed how MPNV-based estimates of grassland and forest self-sustainability correlate with the effort needed to preserve restored open habitats.

Findings show that higher forest self-sustainability strongly predicts the need for more intensive post-treatment—even in cases where grasslands are themselves relatively self-sustaining. This highlights the risk posed by persistent woody vegetation dynamics in some landscapes, where forests can rapidly re-establish without continuous management.

By applying MPNV models, restoration planners can better anticipate which sites are likely to need long-term intervention, and which may achieve stability with less effort. This predictive capacity can help optimize restoration investments by identifying sites where conservation goals can be met with lower maintenance costs.

This study demonstrates the practical value of integrating ecological modeling into restoration planning, especially in systems where management resources are limited. Read the whole article here: https://doi.org/10.1111/rec.70037

Published by Márton Vörös, Ákos Bede-Fazekas, Lorenzo Crecco, Balázs Deák, Adrienn Gyalus, András Schmotzer, Orsolya Valkó, Melinda Halassy, Imelda Somodi in the Restoration Ecology.