Elsevier

Ecological Modelling

Volume 403, 1 July 2019, Pages 35-43
Ecological Modelling

A genetic approach for simulating persistence of reintroduced tree species populations in restored forests

https://doi.org/10.1016/j.ecolmodel.2019.04.014Get rights and content

Highlights

  • We present a model to evaluate genetic diversity over time in restored populations.

  • Our model can be applied to tree species from tropical and temperate forests.

  • The model can support planning of both restoration projects and management actions.

  • We evaluated the minimum number of seed sources for one tree species.

Abstract

Tree populations in regions undergoing restoration are generally made up of few individuals, isolated from neighboring populations, and are found within a matrix of inhospitable human-modified landscapes. Resulting negative genetic consequences such as inbreeding depression and genetic drift require mitigation strategies to maintain sufficient genetic diversity in restoration areas. Such strategies often involve seed sampling from many source trees with different provenances. However, the efficacy of these approaches has not been validated. We present an individual-based spatial simulation model to evaluate the effects of: 1) differing levels of initial genetic diversity; and 2) different area sizes on short (tens of years) and mid-term (hundreds of years) restored population viability. We demonstrate this approach and the use of our model with case study of Centrolobium tomentosum, a tropical tree species widely used in restoration projects in the Atlantic Forest of Brazil. Our model and analysis framework can be applied in studies of tree species with different characteristics, from tropical and temperate forests, to assess population persistence in restoration sites as a function of genetic diversity and population size. This knowledge can support planning of both restoration projects and management actions, increasing the probability of restoration success while also reducing associated costs.

Introduction

Ecological restoration endeavors to recover and improve ecological processes to attain functional and resilient communities that can adapt to changing conditions (Alexander et al., 2011). Restoration is now acknowledged as a global environmental priority and many international initiatives are aiming to restore millions of hectares around the world (Suding et al., 2015; Holl, 2017). Broad scale re-conversion of pasturelands and agricultural fields to native forest ecosystems is seen as necessary to mitigate future extinctions in regions with high levels of habitat loss and fragmentation (Banks-Leite et al., 2014; Newmark et al., 2017). The goal of such restoration is to support conservation, re-establish plant populations with sufficient genetic diversity to persist overtime, and to increase landscape connectivity to facilitate plant and animal gene flow in human-modified landscapes (Chazdon et al., 2015; Brancalion et al., 2018).

Despite the myriad of benefits that restoration may provide, the probability of population persistence following forest restoration planting is uncertain, and can be affected by multiple factors including micro-site limitations for recruitment (Bertacchi et al., 2016), competition with invasive species (D’Antonio and Meyerson, 2002), pollination and dispersal limitation (Dixon, 2009), climate change (Harris et al., 2006), and genetic-mediated processes, such as inbreeding depression (Thomas et al., 2014). Improving our understanding of the conditions under which persistence is more likely is critical to successful restoration and effective conservation (Suding et al., 2015).

The use of computer models is one potential solution for this problem (Epperson et al., 2010). The process through which data and models of populations are evaluated to estimate likelihoods of population persistence over an arbitrary amount of time is called population viability analysis (PVA; Boyce, 1992). Some research groups developed forest models with varied applications, such as to predict succession dynamics of forest communities (Pacala et al., 1996), understand individual tree growth (Hauhs et al., 1995) and community dynamics (Bugmann, 2001), and evaluate the interaction of trees with environmental factors (Seidl et al., 2012). Also, an increasing number of forest landscape simulation models are available, as reviewed by Scheller and Mladenoff (2007). However, there is an unfortunate lack of simulation programs that combine population dynamics and spatial and temporal genetic variation of tree species, which have long life spans, and overlapping generations. Also, most PVAs assessments ignore or inadequately model genetic factors as the effect of inbreeding depression on total fitness (Frankham et al., 2014).

Mid- and long-term persistence of restored tree populations depends on the capacity of species and populations to evolve in response to environmental changes. This capacity in turn depends on intra-specific population levels of genetic diversity (Booy et al., 2000). Populations with reduced genetic diversity tend to show reduced fitness, limited potential for adaptation (Reed and Frankham, 2003), and have a higher probability of extinction due to diseases or environmental stochasticity (Mills, 2012). Thus, using seeds with high genetic diversity in restoration projects is one of the main strategies for effective restoration and successful conservation in the face of environmental change (Zucchi et al., 2018). However, our understanding of how to use genetic data in restoration projects remains incomplete (Thomas et al., 2014; Basey et al., 2015).

Efforts to include genetic diversity in forest restoration often involve the selection of specific source populations and a diversity of trees from which seeds are collected for sapling production (mother trees) for seed harvesting (Bozzano et al., 2014; Basey et al., 2015). Restoration practitioners typically aim to ensure the long-term persistence of reintroduced populations by using seeds from local populations, which increases the probability that individuals will be adapted to local environmental conditions. Practitioners also aim to maximize the genetic diversity of the founding population through the selection of many different mother trees (Hufford and Mazer, 2003; McKay et al., 2005).

Restoration projects that aim to increase genetic diversity and local adaptation in seedling populations nonetheless face significant challenges when implemented at small geographic scales, because small areas comprise small populations. Because of their relatively low effective population size, these populations are at risk of loss of genetic diversity similar to populations negatively affected by habitat loss and fragmentation (Sezen et al., 2007; Chazdon, 2014). The loss of genetic diversity can be observed as a reduction in heterozygosity and allelic diversity through time. The smaller the population, the faster the manifestation of the negative effects of drift on genetic diversity is expected (Mills, 2012). The loss of genetic diversity in these populations due to inbreeding depression further increases the risk of local extinction (Frankham, 2005) and, consequently, restoration failure.

To address this issue, researchers and practitioners have provided different recommendations for the minimum population size necessary to avoid the negative effects of genetic drift and inbreeding depression in the context of restoration in both the short-term (5 generations) and long-term (>100 years). It has been suggested as a general rule that seeds should be sampled from at least 30 (Vencovsky, 1986) or 50 trees (Basey et al., 2015) to recover sufficient genetic diversity in tree populations with an effective population size (Ne) ≥100. However, there is a lack of studies validating or testing the efficacy and feasibility of these recommendations due to constraints in both ecological (limited number of mother trees of low density species in fragmented landscapes) and financial terms (time and resources spent to find numerous mother trees and collect their seeds) (Brancalion et al., 2012). For example, in tropical forest restoration efforts, where over 100 native tree species have been reintroduced in restoration plantations (Rodrigues et al., 2011), reaching the targeted number of mother trees per species (i.e., 30–50) can be difficult. Also, as the current recommendations for forest restoration is to plant 20 in/ha, in small restoration areas (< 5 ha), up to two or three seedling from each mother tree would be planted if the general rule is followed. Improved evaluation of the probability of success of restoration approaches that vary in the number of mother trees used and the effective population size targeted is needed to effectively deploy limited restoration resources.

In this study we present a novel individual-based model to simulate the spatio-temporal genetic and population dynamics of trees in restoration plantations in response to different restoration strategies and over long time scales. Using this model, we specifically examine how different levels of initial genetic diversity and population size influence long-term restoration success, defined as short and mid-term population persistence. Currently, there are few programs that simulate the population dynamics of tree species, which have long life spans, and overlapping generations, and that also model spatial and temporal genetic variation. Also, most of these models ignore or inadequately capture genetic factors such as the effect of inbreeding depression on total fitness (Frankham et al., 2014). In our expository treatment of our newly developed model, we present a case study application based on restoration of a tropical tree in the Atlantic Forest of Brazil.

Section snippets

The model

The description of our model follows the ODD (Overview, Design concepts, Detail) protocol of Grimm et al. (2006, 2010).

This model was designed to investigate the influence of initial genetic diversity and area size on population persistence. Initial genetic diversity within a restoration plantation is represented by the minimum number of mother trees from which seeds were collected. In addition to initial area size, this is one of the parameters over which management agencies have the greatest

Model application

We demonstrate application of our model using a simulated restoration experiment using C. tomentosum (Fabaceae) in the tropical Atlantic Forest of Brazil. This species was selected because it is widely used in restoration projects in Brazil and because demographic and genetic data are readily available. C. tomentosum is frequently used for forest restoration because it is a common gap-colonizing species, reproduces once a year, grows quickly, produce high-quality timber, and has symbiotic

Sensitivity analysis

We found that our model is not overly sensitive to any of the parameters tested given the range of values we examined (Table 2). Each parameter had different effects on the mean number of alleles (A), the inbreeding coefficient (FIS), and the proportion of extinct populations. The parameters with the strongest effect were maximum age that a tree can reach, germination rate, selection pressure value, and average number of seeds produced by each tree on the number of alleles (Table 2).

Model application

Complete

Model development

The model we present is a tool for understanding the effects of initial genetic diversity and population size on the long-term viability of tree populations in forest restoration projects. It can be applied in studies of tree species with different characteristics, from tropical and temperate forests. This model is particularly useful when rare and threatened species are reintroduced in restoration sites to support their conservation, which relies on the potential of reintroduced populations to

Final remarks

Simulation approaches have been used previously in forest restoration to assess the impact of ecophysiological parameters on species resilience of forest stands (Pietsch and Hasenauer, 2002); to understand the influence of management on forest structure over time (Covington et al., 2001); and to predict habitat quality in restoration plantations (Pausas et al., 1997). Simulations are a useful tool for evaluating restoration success when empirical manipulation of the system is either too costly

Author’s contributions

PSS, PMAJ, MIZ and PHSB worked on the study conception and design. PSS and MEN wrote the simulation program. PSS analyzed, interpreted data and drafted the manuscript. All authors participated in critical revision of the manuscript.

Conflicts of interest

None.

Acknowledgements

This research was developed at the Université de Montréal, during an internship to PSS funded by the FAPESP: São Paulo Research Foundation. National Council for Scientific and Technological Development, (FAPESP - 2014/01364-9; BIOTA 2011/50296-8). The authors declare that they have no conflict of interest. PHSB thanks the National Council for Scientific and Technological Development (CNPq; grant #304817/2015-5).

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    Present address: Health School, Centro Universitário do Distrito Federal, Brasília, DF, 70390-045, Brazil.

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