Post provided by Chloe Robinson

Credit: Food and Agriculture Organization of the United Nations.

Forests, across all ecozones and in all shapes and sizes, are essential for life on earth. Around 80% of the world’s land-based biodiversity call forests home and over 1 billion people, including more than 2,000 Indigenous cultures, rely on forests for food, shelter, energy and income. As with many other ecosystems, forests worldwide are under increasing threat from human activities, with the current rate of deforestation estimated at 13 million hectares per year.

In 2012, the United Nations General Assembly proclaimed 21 March the International Day of Forests (IDF), to raise awareness of the importance of all types of forest. The theme for 2021 is “Forest restoration: a path to recovery and well-being“, with a focus on how restoring forests will improve both the health of our environment and our own health. In this post, Associate Editor Chloe Robinson will highlight some of the Methods in Ecology and Evolution papers from the last 5 years, which contribute towards the process of forest restoration.

Bottoms up: Soilutions

Landslide in Monteverde forest in Costa Rica. Credit: Katie McGee.

When thinking about restoring forests to a healthier state, it may appear as easy as planting more trees and hoping for the best. Planting trees is most definitely one of the core elements of forest restoration, but before we can plant new life, we need to consider the hubbub of live that already exists below the surface.

By firstly evaluating the health of soils, we can identify any changes needed to support new plant growth and improve overall forest health. Soils need microbes and invertebrates, especially arthropods, to thrive and we can monitor these species to get a better understanding of soil health. One way to monitor the biodiversity of forest soils is to use a DNA-based approaches, such as metabarcoding.

An MEE study ‘Metabarcoding and mitochondrial metagenomics of endogean arthropods to unveil the mesofauna of the soil’ by Arribas et al. in 2016, proposed a Flotation–Berlese–flotation (FBF) protocol for sampling arthropods for downstream DNA analyses. Their protocol, combined with PCR‐based and shotgun sequencing pipelines enabled them to overcome common issues with extracting DNA from soils, such as the high prevalence of DNA inhibitors, to allow characterisation of soil diversity. Similarly, an MEE study in 2018 by Dopheide et al. (Impacts of DNA extraction and PCR on DNA metabarcoding estimates of soil biodiversity), highlighted the importance of consistent DNA extraction methods for soils and found that larger samples of soil (≥15 g) should be used for analyses of metazoan (e.g. invertebrate) biodiversity.

Logging road through Costa Rican forest. Credit: Katie McGee.

As well as determining soil health using invertebrates prior to restoration efforts, it is also important to continue monitoring soil health, particularly concentrating on microbial diversity, to determine how successful activities have been to improve the health of forest ecosystems. One of our previous blog posts for World Soil Day 2019 by Dr. Katie McGee, described how DNA metabarcoding of tropical soil microbiomes can be used to evaluate the success of forest restoration in Costa Rica.

Surviving Sprouts: Finding the (pine)needle in the haystack

Forest restoration re-planting does not only include the species which grow to be the giants of the forest, but also includes restoring forest floor vegetation. Understory vegetation is an important component in most forest ecosystems. However, as most forests have multiple canopy levels, it can be difficult to monitor the presence of absence of particular species, especially those which grow close to the ground.

Understory vegetation in Monteverde cloud forest, Costa Rica. Credit: Katie McGee.

A MEE study ‘Informative plot sizes in presence‐absence sampling of forest floor vegetation’ by Ståhl et al. (2017), highlighted the inadequacies of current methods for monitoring non‐tree vegetation such as shrubs and forest floor species. Their study investigated the use of presence-absence sampling to monitor the state and change in forest plant density. They found that for Fennoscandian forests, informative plot sizes range from some tenth of a square metre up to potentially 100 m2, concluding that presence‐absence sampling combined with cover estimates provides in-depth information on local plant abundance.

Attempts to monitor the extent of forest cover, including species presence/absence, is often based on satellite remote sensing or aerial photographs. For tropical forests, authors Tang et al (2021), proposed a novel machine learning framework – artificial perceptual learning (APL) – to tackle problems associated with mapping tree species from aerial photographs in their study ‘Large‐scale, image‐based tree species mapping in a tropical forest using artificial perceptual learning’. They found their method of implementing a fine‐grain mapping of three species, (Prestoea acuminata, Cecropia schreberiana and Manilkara bidentata) attains human‐level cognitive economy, with 50‐fold time savings.

Great Gaps and Big Biomass

Information collected from aerial surveys, such as LIDAR, can be used to develop forest canopy height models (CHM), to look at different surface types and their elevation. As well as measuring height, these models can be used in conjunction with Airborne Laser Scanning (ALS) to map canopy gaps. Silva et al. (2019) published a study in MEE, ‘ForestGapR: An r Package for forest gap analysis from canopy height models’, which introduces a cutting‐edge open source r package, offering tools including automated forest canopy gap detection and computation of gap statistics. They highlight the importance of characterising and understanding tropical forest canopy gaps to assess the effects of forest disturbances on mortality, wood volume, and biomass within structurally complex forest ecosystems.

When it comes to forests, one of the major ecosystem services they provide is carbon storage. Tropical forests in particular play a key role in the global carbon cycle (ca. 285 Pg of carbon stored in above‐ground live biomass). It is therefore important to have robust methods to measure the amount of above‐ground biomass (AGB). In 2018, Sullivan et al. found that allometries constructed with just 20 locally measured values could often predict tree height with lower error than regional or climate‐based allometries. In their study ‘Field methods for sampling tree height for tropical forest biomass estimation’, their results indicated that even limited sampling of heights can be used to refine height–diameter allometries and recommended aiming for a conservative threshold of sampling 50 trees per location for height measurement.

Summary

It is clear the resources and habitats that forests provide for humans and wildlife alike. There is no quick fix for forest restoration as these ecosystems are complex and their health is determined by a variety of both environmental and human factors. The papers featured here, highlight the current research being undertaken to establish the best methods to monitor forest dynamics, from the soil to the sky, for informing restoration efforts.

Credit: Food and Agriculture Organization of the United Nations.

For more information on International Day of Forests, visit the official page here.

To read the Methods in Ecology and Evolution articles featured in this post, click on the links below:

Metabarcoding and mitochondrial metagenomics of endogean arthropods to unveil the mesofauna of the soil

Impacts of DNA extraction and PCR on DNA metabarcoding estimates of soil biodiversity

Informative plot sizes in presence‐absence sampling of forest floor vegetation

Large‐scale, image‐based tree species mapping in a tropical forest using artificial perceptual learning

Satellite remote sensing to monitor mangrove forest resilience and resistance to sea level rise

ForestGapR: An r Package for forest gap analysis from canopy height models

Field methods for sampling tree height for tropical forest biomass estimation