The Road to Forest Recovery: What is Driving us to be a Factor Smarter

https://www.nature.com/articles/ncomms11666

The view from above of a clear cut forest from logging (credit Sightline Institute)

When disasters strike, crops fail, and people lose their livelihoods, forests serve as safety nets that provide vulnerable communities with alternative food and income. Also, around 250 million people live in forests and depend on them as the primary source of livelihood. In short, forests provide important ecosystem services. Yet recent authoritative reports reveal a persistent hemorrhaging of the world's most valuable ecosystems. An especially worrisome signal is that we are not doing enough to stop the bleeding. We are losing 13 million hectares of forest globally every year - an area the size of Greece lost every week, or roughly one football pitch every 2 seconds (FAO, 2020). Such loss is tragic on multiple levels-health, environmental, socio-economic, and cultural. The main drivers of forest loss are shifting agriculture, urbanization, wildfires, extractive activities, roads, and other infrastructure (Figure 1).

Figure 1: Main Drivers of Forest Loss

Copyright: ©2018 Arizona State University and University of Arkansas. Infographic design by Giada Mannino, Senior Designer, The Sustainability Consortium.

Forest restoration is on the rise but we see little success

Restoration initiatives are becoming increasingly widespread. On 5 June 2021, on the occasion of the World Environment Day, the United Nations Decade on Ecosystem Restoration (UN Decade 2021-2030) was launched. This followed a call to action by over seventy countries for a fundamental shift in how biodiversity is utilized and managed. Led by the Food and Agriculture Organization of the United Nations (FAO) and the UN Environment Programme (UNEP), the UN Decade aims to build an effective global movement to shift the world on to a trajectory compatible with a sustainable future.

The UN Decade is not the first global policy document on biodiversity protection and conservation. There have been several other global proclamations of similar breadth and depth. For instance, the Great Green Wall Initiative, the Bonn Challenge, the UN Decade for Deserts and the Fight Against Desertification (2010-2020), the United Nations Decade on Biodiversity (2011-2020), the 2030 Agenda for Sustainable Development, which has several targets related to ecosystem restoration, the Convention on Biological Diversity (CBD), the United Nations Convention to Combat Desertification, the Aichi Biodiversity Targets, and the Paris Agreement, to mention but a few. Countries also have their national policies, and so the list is endless. These policies come with huge financial investments and a plethora of forest restoration programs and projects, majority of which end up being complete failures.

Fixing the problem

The above synthesis begs the fundamental question: Why do some forest restoration projects achieve near-total success while others become complete failures? To improve the knowledge and understanding of forest restoration success, a team of researchers embarked on a path-breaking research study. They sought to answer the following research questions:

1. What are the main ecological drivers of forest restoration success?

2. Does restoration success change across different geographic regions and ecological metrics used to assess biodiversity?

To answer these questions, they conducted a global meta-analysis on forest restoration success. The study encompassed 269 primary studies, 221 study landscapes, 53 countries and six geographic regions, and contained 4,645 quantitative comparisons between reference systems and either restored or degraded systems.

What did they find after looking at over 200 different studies?

A meta-analysis of forest ecosystem restoration developed the critical takeaway from hundreds of studies over time from many areas that showed that time is the primary driver and plants are the key factor to measure with. What are the drivers and factors in ecological restorations? Drivers are when humans or nature cause direct or indirect changes in a natural environment. A Factor changes how an organism or community of organisms behaves. So, the short answer to all the studies on forest restoration says that it takes a long time to reach a quality habitat value. The best way to gauge this is by looking at the invertebrates and plants and measuring the vegetation's density, cover, and biomass. Bootstrapped response ratios are the meta-analysis of studies with similar samples from similar sizes, combining all the data with running statistical analysis on the complete data sets.

Primary Succession of restoration from barren to a full forest (credit University of Chicago)

The best way to place value on the vegetation in a forest system is to look at the successional traits of the plants. What is succession? In this case, we are not referring to a hit television show. Instead, the scientific basis for describing habitat rebuilding is based on the mix of species found over time that gradually changes as moving from barren to a diverse forest. Politically the push has been on the restoration of heavily logged areas. Still, this study has shown that choosing to restore degraded or moderately degraded forests will have more tremendous success in a healthy succession over time. They aren't saying leave the clear-cut logging areas barren. Still, we want to affect the health of many levels of plants and animals. In that case, we can have a more impactful effect on areas with some forested areas.

Majestic cats or beautiful owls get more attention than snakes and snails. (Mountain lion, cottonmouth, long eared owl and forest snail credit Anne Hughes)

When looking at the health of a forest system, we want to focus a lot of the research on what's underfoot. Looking at the leaf litter, the biomass, and the invertebrates (yes, we love the bugs) to know how healthy the forest is. Everyone wants to look at the pretty birds or the cute and furry animals. Still, this study has shown that they can evolve to survive in many environments and don't require healthy forest systems to thrive. So, in short terms, the creepy crawlies are much more important in giving a value system to a restored forest than the cute furry critters and brightly colored birds.

Methods of Data Collection:

• 7 key literature review sources (peer reviewed journals)

• 269 studies were reviewed.

• Reviews of Primary and Secondary Forest systems

• Compared ecological metrics: richness, diversity, and similarity.

• Population patterns of the communities and abundance

Issues in this meta-analysis:

• The forests were in different areas with different climate and soil types.

• Forests studied were different sizes.

• Some of the forests were considered unique systems but were actually close together, comparing like to like.

• The edges of the forests were different and could have affected the resulting data based on size and inclusion or exclusion in each of the studies.

Data Extraction:

· Study Year

· Country

· Geographic region

· Latitude and longitude

· Time elapsed since restoration (in years)

· Disturbance type (selectively logged, secondary forest, complete/partial clearing)

· Degraded/restored system, vegetation structures given a ranking.

o Taxonomic groups

Plants

Mammals

Invertebrates

Birds

herpetofauna

o Vegetation structures

Litter

Cover

Biomass

Density

Height

· Restoration activities (passive regeneration or active management/planting)

· Biodiversity assessed using ecological metrics (richness, diversity & abundance)

· GIS software to assess land cover using satellite maps over time.

How did they do it?

Response ratio calculations

Many of the past studies on forest restoration success do not provide information on the measurements of how much their data differs from the average or expected values. With this in mind, the global meta-analysis conducted by Crouzeilles et al. used the response ratio as the standard mean effect size, which allowed them to use more studies in their meta-analysis. Choosing to use the standard mean effect size gave the researchers a total of 4,645 quantitative (numerical) comparisons between reference sites and restored/degraded sites.

Meta-analysis

The researchers took several precautions to make their meta-analysis more accurate. Some of these precautions included resampling the available data with replacement, only using one comparison per landscape, and removing outliers (data that was extreme compared to the majority). Since the main focus of the study was on reference sites compared with restored and/or degraded systems, they did not use past studies on directly restored vs. degraded systems. If they were to include these closely related studies, they would have decreased the amount of available data that could have been used in the review on drivers of forest restoration success. Accounting for these problems allowed the research to focus on using the reference sites as a benchmark to get a more accurate message across to the audience.

What they found

Potential ecological drivers

This study identified several potential drivers of restoration success at a local and landscape level. These included 3 for local scale, 5 for landscape scale, and 2 overarching variables that could broadly influence restoration success (figure 2)

Information theoretic approach

To identify the main drivers of restoration success, the researchers used an information theoretical approach, which may also be known as the maximum likelihood estimate (Solberg 2018). According to Marriam-Webster Dictionary, information theory can be defined as a theory that deals with information, the efficiency of communication, and the measurement of its content (Merriam-Webster). The data sets from reference sites were compared with restored systems for each taxonomic group and each measure of vegetation structure. The model used included the 10 potential ecological drivers listed above resulting in 10 separate analyses.

#Forests #Restoration #EcologicalRestoration #Succession #EcologicalDrivers #EcologicalFactors #Logging #MetaAnalysis

References

Food and Agriculture Organization of the United Nations and UN Environment Programme, The State of the World’s Forests 2020. Forests, Biodiversity and People (Rome, 2020), pp. xix, 62. Available at http://www.fao.org/documents/card/en/c/ca8642en.

Solberg, A. (2018, September 12). Information theoretic statistics. NASA. Retrieved March 23, 2023, from https://www.nasa.gov/consortium/InformationTheoreticStatistics

Merriam-Webster. (n.d.). Information theory definition & meaning. Merriam-Webster. Retrieved March 23, 2023, from https://www.merriam-webster.com/dictionary/information%20theory