Prioritizing landscapes for restoration based on spatial patterns of ecosystem controls and plant–plant interactions

Abstract

Summary The widespread degradation of natural ecosystems requires cost‐efficient restoration techniques that minimize risk and consider context‐specific restoration conditions. However, meeting these demands can be difficult because information on ecosystem‐level factors controlling vegetation and continuous spatial data on species interactions are often lacking. Using airborne light detection and ranging (LiDAR) data from a Hawaiian dry forest, we delineated crowns and assessed the 3D structure of more than 700 000 shrubs and trees. We used Random Forest machine learning to assess the relative importance of resource availability, environmental conditions, and disturbance regimes on canopy density. We then modelled and scaled up plant–plant interactions (i.e. potential nursery effects) to landscape units using a LiDAR‐derived Canopy Coalescence (CC) index. We used the relative importance of ecosystem factors, canopy cover, and CC index to prioritize landscapes for restoration. Here, we demonstrate a methodological framework that prioritizes landscapes in need of restoration (i.e. planting woody species) using two ecological perspectives: (i) ecosystem‐level controls on remnant woody vegetation, and (ii) potential interactions between established canopies and seedlings (e.g. nursery effect, competition). Our results highlight the heterogeneous nature of ecosystem‐level drivers affecting forest structure along elevation gradients. Consequently, the degree of potential nursery interactions between established canopies and seedlings at the landscape‐scale was context‐specific. Synthesis and applications. Our study provides a methodological approach that prioritizes landscapes for restoration by identifying the main controls on tree spatial distribution and by inferring the favourable conditions for seedlings. This approach can guide land managers to define cost‐efficient restoration strategies for large ecological areas.

Integrating anthropogenic factors into regional‐scale species distribution models—A novel application in the imperiled sagebrush biome

“…Broad‐scale plans to restore ecosystem function to extensive amounts of degraded land include the Aichi Convention on Biodiversity (global restoration of 15% of degraded ecosystems; Convention on Biological Diversity, ) and the Bonn Challenge (restoration of 150 million hectares; http://www.bonnchallenge.org/). However, achieving restoration at landscape to regional scales will require spatial planning to allocate limited resources for effective results (Barbosa & Asner, ; Neeson et al, ), including the need to consider climate variation in restoration plans (Harris, Hobbs, Higgs, & Aronson, ; Jackson & Hobbs, ). Most studies for this purpose have taken a prospective approach to forecast restoration outcomes based on climatic variation in plant performance (Amburgey et al, ; Harris et al, ; Richardson & Chaney, ).…”

“…bonnc halle nge.org/). However, achieving restoration at landscape to regional scales will require spatial planning to allocate limited resources for effective results (Barbosa & Asner, 2017;Neeson et al, 2015), including the need to consider climate variation in restoration plans (Harris, Hobbs, Higgs, & Aronson, 2006;Jackson & Hobbs, 2009). Most studies for this purpose have taken a prospective approach to forecast restoration outcomes based on climatic variation in plant performance (Amburgey et al, 2018;Harris et al, 2006;Richardson & Chaney, 2018).…”

Landscape heterogeneity is key to forecasting outcomes of plant reintroduction

“…In particular, such information could help with landscape‐level planning by prioritizing reintroductions to locations where they are most likely to succeed (Neeson et al. , Barbosa and Asner ). However, this information is generally unavailable due to the difficulty of tracking demographic trajectories of populations at landscape scales.…”

A data science challenge for converting airborne remote sensing data into ecological information

“…We ran a competition to improve three tasks that are central to converting airborne remote sensing (images and vertical structure measurements collected from airplanes) into the kinds of vegetation diversity and structure information traditionally collected by ecologists in the field: 1) crown segmentation, for identifying the location and size of individual trees (Zhen et al, 2016); 2) alignment to match ground truth data on trees with remote sensing data (Graves et al, in prep); and 3) species classification to identify trees to species (Fassnacht et al, 2016). If these three tasks can be conducted with a high degree of accuracy, it will allow scientists to map species locations over large areas, and use them to understand the factors governing the individual level behavior of natural systems at scales thousands of times larger than possible from traditional field work (Barbosa & Asner, 2017).…”