Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Open Access

Peer-reviewed

Research Article

Variable Gene Dispersal Conditions and Spatial Deforestation Patterns Can Interact to Affect Tropical Tree Conservation Outcomes

Abstract

Tropical lowland rain forest (TLRF) biodiversity is under threat from anthropogenic factors including deforestation which creates forest fragments of different sizes that can further undergo various internal patterns of logging. Such interventions can modify previous equilibrium abundance and spatial distribution patterns of offspring recruitment and/or pollen dispersal. Little is known about how these aspects of deforestation and fragmentation might synergistically affect TLRF tree recovery demographics and population genetics in newly formed forest fragments. To investigate these TLRF anthropogenic disturbance processes we used the computer program NEWGARDEN (NG), which models spatially-explicit, individual-based plant populations, to simulate 10% deforestation in six different spatial logging patterns for the plant functional type of a long-lived TLRF canopy tree species. Further, each logging pattern was analyzed under nine varying patterns of offspring versus pollen dispersal distances that could have arisen post-fragmentation. Results indicated that gene dispersal condition (especially via offspring) had a greater effect on population growth and genetic diversity retention (explaining 98.5% and 88.8% of the variance respectively) than spatial logging pattern (0.2% and 4.7% respectively), with ‘Near’ distance dispersal maximizing population growth and genetic diversity relative to distant dispersal. Within logged regions of the fragment, deforestation patterns closer to fragment borders more often exhibited lower population recovery rates and founding genetic diversity retention relative to more centrally located logging. These results suggest newly isolated fragments have populations that are more sensitive to the way in which their offspring and pollen dispersers are affected than the spatial pattern in which subsequent logging occurs, and that large variation in the recovery rates of different TLRF tree species attributable to altered gene dispersal regimens will be a likely outcome of fragmentation. Conservation implications include possible manual interventions (manual manipulations of offspring dispersers and/or pollinators) in forest fragments to increase population recovery and genetic diversity retention.

Citation: Kashimshetty Y, Pelikan S, Rogstad SH (2015) Variable Gene Dispersal Conditions and Spatial Deforestation Patterns Can Interact to Affect Tropical Tree Conservation Outcomes. PLoS ONE 10(5): e0127745. https://doi.org/10.1371/journal.pone.0127745

Academic Editor: Zhengfeng Wang, Chinese Academy of Sciences, CHINA

Received: February 17, 2015; Accepted: April 13, 2015; Published: May 22, 2015

Copyright: © 2015 Kashimshetty et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Data Availability: The source code of the version of NEWGARDEN used for this research is available at: https://github.com/StevePelikan/NewGarden. The computer program NEWGARDEN is freely available, with associated materials and program updates, by agreement with Science Publishers (Enfield, NH) at: http://math.uc.edu/~pelikan/NEWGARDEN/. The raw dataset from this research study is available as S1 Dataset.

Funding: 1. University of Cincinnati - Department of Biological Sciences: Wiemen-Wendel Benedict grant (2012). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 2. Ohio Plant Biotechnology Consortium OPBC 2014-008 (includes funding from The Ohio State University Ohio Agricultural Research and Development Center). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Tropical lowland rain forests (TLRF’s) are high-diversity biomes that continue to be under threat from anthropogenic deforestation for resource extraction and land-use change, resulting in the breakup of once continuous forest into fragments of various sizes [1, 2, 3, 4, 5]. These forest remnants can range from 3 ha to over 120,000 ha [6, 7]. A large majority of tropical tree species exist at low population densities making them highly susceptible to local habitat disruption [8, 9]. Conservation of these vulnerable biomes is critical because they are not only reservoirs of biodiversity, but TLRF’s provide important ecosystem services such as moderating biogeochemical cycles and the provisioning of forest architecture, microclimate, and nutritional resources for other organisms [10, 11, 12, 9].

It has been suggested that a large number of low-density tropical tree species are highly outcrossed, thus highlighting the importance of gene flow in these populations which can in turn affect the spatial distribution of individuals, rates of population growth, and population genetics through space and time [13, 14]. Plant gene dispersal has been found to commonly follow a leptokurtic distribution, involving occasional long-distance dispersal coupled with mainly short-distance dispersal ranges [15, 16]. Fragmentation can affect these naturally-occurring gene dispersal curves (kernels) of TLRF tree populations by bringing about changes in the natural abundance and/or behavior of offspring and/or pollen dispersal vectors [17, 18]. For example, certain pollinators (e.g. some species of large bees and hummingbirds) disperse pollen to greater distances following fragmentation by readily crossing resource-poor areas between fragments that are separated by a few hundred meters to several hundred kilometers, respectively [19, 17]. In contrast, some dispersers in TLRF’s (e.g. certain species of bees, birds and small beetles) are less likely to cross open pasture beyond fragment area, thereby restricting gene dispersal distance to within the fragment [20, 21]. Obviously, not all gene dispersal vectors will respond in the same way to fragmentation, and post-fragmentation alterations in gene dispersal cannot be generalized for all species or situations [22, 23]. As such, the effects of a range of post-fragmentation alterations in offspring and pollen dispersal distances need to be tested to determine how fragmentation might affect population growth and genetic diversity trends of TLRF tree populations.

TLRF fragments created as a result of initial deforestation can undergo further disturbance in the form of different types of logging activities from clear cutting to selective logging and Reduced Intensity Logging (RIL) [24, 25, 26]. Clear-cutting is the removal of essentially all above-ground biomass, whereas selective logging is a less intense form of deforestation that involves the removal of only the economically valuable trees, these usually being the largest trees [27, 28, 29]. Studies on selective logging in Amazonian forests showed that logging fewer, larger tracts of TLRF’s was advantageous in reducing any negative impacts on the functional composition of the remaining stand (e.g., increasing the number of early successional species) [30]. The life-history characteristics of the tree species being selectively-logged, e.g. minimum reproduction DBH (cm), has also been reported as being an important factor in determining the overall sustainability of population genetic diversity from this type of deforestation [31].

Reduced Intensity Logging (RIL) is a lower-intensity form of selective logging where less-damaging wood transportation systems are also coupled with measures implemented to better protect pre- and post-logging seedlings [32, 25, 33]. Previous RIL simulation studies have shown that certain life-history traits are important determinants in post-logging population recovery, with size-class distributions being the most influential factor, followed by growth rates (DBH increments) and mortality rates [34]. The success of RIL was found to partly depend upon sufficiently long cutting cycles that enabled natural regeneration processes to occur [24].

One aspect of logging that has not been fully explored is whether different spatial logging patterns of TLRF trees in forest fragments might affect the demographic dynamics and the genetics of populations with varying life-history characteristics. For example, are there certain geometrical patterns of tree removal which are more sustainable in terms of improved population re-growth and genetic diversity retention over others? How do these variable patterns of logging interact with post-fragmentation alterations in life-history traits such as gene dispersal distances in terms of population recovery and genetic diversity retention? Detailed research into such issues is necessary to help reduce the high rates of rain forest biodiversity alteration and loss, [35, 36, 37]. For example, such interventions could affect existent population abundance and growth dynamics, as well as the spatial distribution patterns of offspring recruitment and/or pollen dispersal, and thus have a potential effect on the viability of the population in the short and/or long term [38].

To study these aspects of TLRF deforestation and fragmentation, the goal of this research was to investigate the effects of different spatial logging patterns on a plant functional type (that of a long-lived, tropical canopy tree population) that is commercially exploited, under a range of different offspring and pollen dispersal distances potentially resulting from post-fragmentation shifts in dispersal vector abundance and/or behavior [39, 40, 41]]. Alternatively, the experimental virtual populations examined here could be thought of as different species that share most of their life history characteristics except for offspring and pollen dispersal kernels: how would fragmented populations of these different species respond to different deforestation practices? A central objective of this study was to determine whether tree removal patterns could be found that differentially maximized population recovery and genetic diversity retention, and/or kept population subdivision and inbreeding levels low, in order to recommend more sustainable extraction practices and effective conservation strategies under particular gene dispersal scenarios. We hypothesized that TRLF tree populations would respond differently to post-fragmentation alterations in gene dispersal and variable spatial patterns and types of logging (i.e., in lines, at edges, or selective logging) in terms of their population recovery, genetic diversity retention, inbreeding, and population subdivision.

Given the stochastic nature of several processes driving population development in natural populations, a high level of experimental replication is crucial for providing statistically accurate short- and long-term insights regarding the issues we are addressing [42, 43]. Sufficient experimental replication in the field in tropical low land rain forests (TLRF’s) is currently not feasible due to expenditures and spans of time needed [34]. As such, we used a spatially-explicit computer simulation program, NEWGARDEN, which models the population growth and genetic diversity dynamics of plant populations through time, and which is also capable of highly replicated computer modeling providing statistical robustness [44], to conduct an entirely theoretical modeling study exploring how, for a ‘typical’ TLRF long-lived canopy tree, alternative deforestation methodologies in TLFR fragments can interact with differing gene dispersal patterns to affect subsequent population recovery and genetics [45, 46, 47, 48, 49].

Methods

NEWGARDEN

NEWGARDEN (NG) is a spatially explicit, individual-based computer program that simulates natural population development of plant species through bouts of mating, and provides output to assess population growth, genetic diversity, population subdivision and/or inbreeding level trajectories of the developing, comparative, virtual populations. Population development is conditioned by user defined input parameters such as genetic diversity of the source population; geometric number, age distribution and pattern of the founders; gene dispersal distance kernels; etc.

NG operates based on a set of user-specified input conditions that specify: 1) the characteristics of the preserve fragment system; 2) the number, geometric placement, genetic diversity, and age distribution of the founding population; and 3) several life-history characteristics such as age-specific offspring and pollen production, mortality and life-span describing the species being modeled. Each set of user-specified input conditions constitutes a “trial”. For each set of trial input conditions, the user can define the number of replicate “run” analyses using those conditions to be performed, thus allowing for statistical comparisons of mean output values for intra-trial runs (given with standard deviations) between contrasting trials. Output statistics compared across trials from each successive bout of reproduction for the entire population include means for population size, measures of genetic diversity including the number of founding alleles retained, observed and expected heterozygosity, and Fit values reflecting population subdivision and/or inbreeding (interpreted according to [50]). These mean values can be compared across trials using ANOVA’s and T-tests of means. In this way, output from trials differing in input conditions can be compared to examine the effect of varying one or more characteristic(s) on the population growth and genetic diversity of resultant populations [46, 48, 49].

Review of previous forest simulation models indicates NG as not fitting any one model classification (e.g., it is not entirely a physiologically-based process model, gap model or an empirical stand model; [51, 52, 53]). While NG does share some features with other programs, (e.g. it seeks to describe population growth through the processes of recruitment, growth and mortality and it can model gap and non-gap situations (see [51, 52, 53]), it is unlike any other program currently available as it combines different features of a range of models and contains unique features as well. For example, 1) it models a full range of life-history traits set by age-specific probabilities including reproduction, pollen production and mortality; 2) it is driven by explicit ‘virtual’ life-history events such as reproduction, gene dispersal and mortality which are set by age-specific parameters and not by mathematical growth equations nor any physiologically-based equations regarding photosynthesis, carbon balance and other abiotic conditions; 3) it has the ability to model different mating systems including agamospermy, different levels of selfing, and dioecy; 4) it allows the user to specify, for a given NG trial, the genetic diversity of the source population, spatial location and age structure of the founders; 5) it can model complex genetic histories including multi-locus founder genetic diversity levels where the user can set desired allele frequencies, self-incompatibility alleles and effects of inbreeding depression; 6) it models reproduction, dispersal and immigration in a spatially-explicit manner including offspring and pollen dispersal distance categorical probabilities, allowing mating events only between eligible pollen donors and pistillate individuals; 7) it can model spatially heterogeneous landscapes that can change through time; 8) it is capable of high levels of replication of comparative simulations, and calculates means and standard deviations for important measures to inform conservation practices, such as population growth, genetic diversity levels and population genetic statistics like Fit and Fst over time; and 9) output reported after each new bout of mating include both entire standing crop versus only the most recent cohort data, and includes coordinates, parentage, allelic status, and age of death of all individuals as well as other statistics if requested (S1 Table; [45, 46, 47, 48, 49]). NG is freely available, with associated materials and program updates, by agreement with Science Publishers (Enfield, NH) at: http://math.uc.edu/~pelikan/NEWGARDEN/.

Control Equilibrium Population

Our modeling was centered on the plant functional type of a long-lived, canopy tree species since this group is commonly harvested for timber in TLRF’s [54, 39, 32, 41]. A “control equilibrium population” (one not changing in size through bouts of mating; [55] for this plant functional type was developed to enable the simulation of comparative trials of an initially stable baseline tree population that then undergoes varying spatial logging patterns and modification of gene dispersal conditions.

To develop and model this control population of a ‘typical’ TLRF long-lived canopy tree, we incorporated some of the initial input life history characteristics from one of the better studied TLRF canopy trees, Symphonia globulifera L. f. (Clusiaceae), a widespread species distributed in several regions of the Neotropics and the West African tropics [56, 57]. Since no single tropical tree species has been studied for all the parameters utilizable with NG (including S. globulifera), we used empirical studies on other species, or groups of species [58] thought to be typical of this plant functional type to derive other life-history parameters for these computer simulations. For example, studies on the commonly exploited canopy tree species S. globulifera provided estimates for population density, age-class distribution, mating system and preserve characteristics, while data from a 50-ha rain forest plot in Pasoh, Malaysia provided estimates for size-class distribution for the smaller size-classes, and data on mortality rates were derived from a study on tropical canopy tree species from La Selva, Costa Rica [58, 59, 60, 41]. The equilibrium population was based on our best estimates of the life history conditions for a tropical, long-lived canopy tree species and setting the reproductive value (r, average offspring produced per individual in a given round of mating) such that the population size remained stable. These control equilibrium population conditions were subsequently perturbed by altering logging patterns and gene dispersal conditions in comparative trials to analyze the resulting population growth and genetic diversity consequences of such alterations. It is important to note that the aim of this research was not to model one particular TLRF plant species. Rather, we attempted to model responses of different populations of a more or less “representative species” of a particular functional group that varied in internal patterns of culling and/or gene dispersal distances.

With a complex study like ours which explored the effects of both spatial logging pattern and gene dispersal distances (via both offspring and pollen), it was simpler to begin with a basic equilibrium population to determine the general effects of these variables separately, as well their synergistic effects. As we were mainly varying two variables in this study (spatial logging pattern, and gene dispersal condition) to specifically analyze their population growth and genetic diversity consequences, it was necessary to control for all the other parameters including the equilibrium state of the founding population in order to detect and measure the effects of the variables under scrutiny.

Parameters held constant among trials

Preserve characteristics.

In NG simulations of the preserve system, individuals establish on a Cartesian grid system, the establishment points of which are called grid points. The distance between two grid points is meant to reflect the naturally-occurring average minimum distance between two mature, reproductive individuals in the population. We modeled a 500 ha plot based on a study of S. globulifera in the Tapajos National Forest, Brazil (equivalent to a 160 x 160 grid point fragment in NG; modified to be in the shape of a square to begin simulations with the simplest shape possible; Fig 1) with the average minimum distance between two reproductive individuals being 14m, as reported in the literature [41]. The initial control equilibrium population comprised of 965 founders randomly placed in the 500 ha un-logged fragment, with a resulting density of 1.932 individuals/ha (with ~ 0.332 reproductive individuals/ha), with equal probabilities of dispersing offspring to or receiving pollen from each distance frame respectively, relative to a pistillate individual selected for mating (see below for gene dispersal descriptions).

thumbnail
Download:
Fig 1. Spatial logging patterns showing 10% total deforestation of a 500 ha fragment (in grey).

Fragment is 2.23 km or 160 grid points on a side. Diagram is approximately to scale. a = Corners, b = Road, c = Central Square, d = 4Squares, e = 3Strips, f = Strip. Additionally, selective logging was also performed, representing a 10% removal of only those individuals belonging to the highest age classes.

https://doi.org/10.1371/journal.pone.0127745.g001

Age class distribution.

The age-class distribution was based on field data of S. globulifera from Tapajos National forest Brazil [41]] and size-class distribution data and growth rates reported from Pasoh, Malaysia (further details in S1 Appendix; [58, 61]). It was set as follows: Ages 1 to 168 (1 to 15cm DBH) represent seedlings, saplings and young juveniles which collectively make up approximately 81% of the population (some tropical trees have been reported to take 98–183 years to reach 10 cm DBH; [62]), ages 169 to 236 (16 to 30 cm DBH) reflect the juvenile age classes which form ~ 10%, and greater than age 236 (30cm DBH) represent mature reproductive individuals that make up 9% of the population.

Loci and allelic variation.

NEWGARDEN is capable of modeling founding populations with a user designated numbers of loci, each locus having a user specified number of alleles, and with each allele having a user specified initial founding population allele frequency [45, 47]. In our analyses here (and previously: e.g., [45, 46, 47, 48]) all individuals were simulated to have 30 loci, with each locus in the source population having 100 alleles, each of these alleles occurring at a frequency of 0.01. We use this loci configuration for several reasons. Although natural populations will have much more complex loci panel structures, it would not only be arbitrary to choose one at random, but we also want to use a simplified, easy-to-interpret null model for ready comparison among trials. Such low frequency alleles are a more sensitive and readily interpretable indicator of changes in population genetic diversity compared to the much less sensitive changes in heterozygosity or gene frequencies at loci with fewer, more common alleles [63]. In small populations such as found in tropical rain forests, considering the thousands of intraspecific loci, surely a considerable portion of these loci will include low frequency alleles such as those we model. Rare alleles can arise in a number of ways (e.g., genetic drift, population immigration or emigration; selection, de novo mutation, etc.). Such alleles are of especial concern in conservation efforts since they are easily lost and yet may have potential significance under differing circumstances (e.g., [64, 65, 66]) In conservation efforts, loss of the most common alleles is relatively unlikely, and thus less worrisome than loss of rare alleles. Further, we have shown that populations experiencing higher rates of loss of rare alleles tend to have a greater variance in the frequencies of more common alleles ([45], work in progress). Monitoring the magnitude of rare allele loss over time can be used as an indirect way to estimate the degree of expected variance in the frequencies of more common alleles in the population. Thus our results with loss of rare alleles reflect the expected degree of variance of common alleles under the different experimental conditions compared. Greater loss of rare alleles and the associated greater variance in the frequencies of common alleles can make populations less tractable to the processes of natural selection, and more susceptible to random genetic drift. By analyzing the rate at which rare alleles are lost, the effects of genetic drift versus natural selection can perhaps be more readily interpreted ([45], work in progress).

Mating system.

The plant functional type modeled was a hermaphroditic species with a selfing rate of 0.098 in all trials [67, 41, 59].

Offspring and pollen production.

In NG simulations, the user designates an age-specific reproduction rate, based on which offspring are generated from the mating of randomly selected eligible individuals. Offspring produced from mating events are distributed across eligible reproducing individuals following a Poisson distribution. The reproduction rate is meant to simulate naturally occurring phenomenon where not all the seedlings produced from a mating event will successfully establish. Studies have found fecundity to be associated with increase in tree size in certain tropical tree species, where canopy trees reach reproductive capacity at approximately 30cm DBH, this generally being the time taken to reach the canopy [62]. The age-specific reproduction rate for the control equilibrium population was as follows: Ages 140 to 167 = 0.5, 168 to 195 = 2.5, 196 to 235 = 7.8, 236 to 424 = 8.0, 425 = 0.0 (S2 Table). Pollen production pattern was similar to age-specific reproduction: Ages 140 to 167 = 0.10, 168 to 195 = 0.15, 196 to 215 = 0.20, 216 to 235 = 0.60, 236 to 424 = 1.0, 425 = 0.75, 426 = 0.0 (S2 Table).

Mortality.

Age-specific mortality rates were based on a modified version of tropical tree mortality patterns reported from studies in La Selva, Costa Rica [60]. Mortality rates, which along with age-specific reproduction, were altered to obtain as near an equilibrium population as possible, were set as follows: Ages 0 to 10 = 25%, 11 to 25 = 14%, 26 to 50: 0.75%, 51 to 75: 0.85%, 76 to 100: 0.95%, 101 to 425: 1% (S3 Table). Further details regarding the functioning of the age-specific mortality parameter in NG, can be found in S1 Appendix.

Parameters that varied among comparative trials

Offspring and pollen dispersal.

In NG, offspring and pollen dispersal distance kernels were simulated via a series of hierarchically nested limiting distance frames, for each of which probabilities of dispersing offspring to, and receiving pollen from, can be designated relative to each pistillate individual selected for mating. The lower and upper limits of each distance frame are specified on the Cartesian fragment grid system [45, 46]. Offspring and pollen dispersal was varied across comparative trials to simulate ‘Near’, ‘Equal’ or ‘Far’ distances. To model these dispersal conditions, we created five distance frames, whose dimensions in the x or y direction around a pistillate individual selected for mating are (gp = grid points): a) 0 to 32 gp (448 m); b) 32 to 64 gp (896 m); c) 64 to 96 gp (1344 m); d) 96 to 128 (1792 m); and e) 128 to 160 (2240 m). ‘Near’ gene dispersal condition corresponded to 80% probability of dispersing offspring to or receiving pollen from the nearest distance frame, the ‘Equal’ condition to equal probability of dispersing offspring or receiving pollen (20%) from each distance frame, and the ‘Far’ condition to 80% probability of dispersing offspring to or receiving pollen from the furthest distance frame (S1 Fig and S4 Table).

All possible combinations of ‘Near’, ‘Equal’ and ‘Far’ variations for offspring versus pollen dispersal distances resulted in a total of nine gene dispersal regimes being tested: NONP, NPEO, FONP, NOEP, EOEP, FOEP, NOFP, EOFP, FOFP; (N = ‘Near’, F = ‘Far’, E = ‘Equal’, O = offspring, P = Pollen).

Comparative logging patterns

The initial control equilibrium population was modified in comparative trials to study six contrasting spatial logging patterns, each comprising a total of 10% (50 ha) removal of the original 500 ha fragment in varying patterns including: one side strip (Strip); three equal area, vertical, equidistantly separated strips (3Strips); four internal, equidistantly separated, equal area subdivided squares (4Squares); four equal area corner squares (Corners); one horizontal central road (Road); and one central square (CentralSq) (Fig 1). In addition, selective logging (Selective Logging) was also modeled as the removal of the top 10% of individuals based on age class. All logging patterns, including an un-logged control fragment were tested with all of the nine gene dispersal conditions, resulting in a total of 72 trials being performed.

Data Analyses

Each set of trial conditions was analyzed through 600 bouts of mating for 100 replicate runs. Replicate run output values for two different classes of summary data, ‘entire fragment’ data which included data calculated from the entire 500 ha fragment and ‘recovery region’ data calculated from only the combined logged areas of the fragment, were generated for each trial. Two-factor ANOVA’s were performed to assess the amount of variance explained by logging pattern, gene dispersal condition, and their interaction, in the output (mean population size, founding allele retention, and population subdivision/inbreeding levels). A separate set of two-factor ANOVA’s were also used to determine the relative impact of offspring versus pollen dispersal distance, and their interaction, on the amount of variance explained in the output. For this last set of ANOVA’s the mean variance reported in the results for each output was an average across all spatial logging patterns. Unpaired T-tests (Bonferroni corrected) were conducted between trials to ascertain whether means were significantly different (p<0.05).

Results

Below are the results from two different classes of summary data: ‘entire fragment’ data which included data calculated from the entire 500 ha fragment, and ‘recovery region’ data calculated from only the combined logged areas of the fragment.

Mean Population Size—Entire fragment

Trials with the same gene dispersal distance conditions but variable spatial logging pattern showed similar population growth through 600 bouts of mating, although there was extensive variation among trials grouped according to dispersal conditions (Fig 2). The gene dispersal condition that maximized population growth was when offspring and pollen were dispersed to/from ‘Near’ distances (Group A; Figs 2 and 3). Trials with offspring dispersal biased to ‘Near’ distances (Groups A-C) had the highest mean population sizes followed by ‘Equal’ (Groups D-F) and then ‘Far’ (Groups G-I) distances respectively, regardless of pollen dispersal condition. However, within the offspring dispersal “Near” category trials (Groups A-C), as well as the “Equal” category (Groups D-F), population growth is highest when pollen dispersal is “Near” (Groups A and D), and lowest when pollen dispersal is “Far” (Groups C and F; Figs 2 and 3). Similarly, control trials simulating unlogged fragments responded significantly to variation in gene dispersal condition (Figs 2 and 3). Two-way ANOVA analysis showed that offspring dispersal distance had a greater impact on population growth, accounting for a mean variance of 87.6%, compared to pollen dispersal (6.3%) and their interaction (4.9%).

thumbnail
Download:
Fig 2. Mean population growth in the entire fragment across 600 generations.

Trials varied in gene dispersal distance (offspring and pollen) and spatial logging patterns. Groups labelled A-I indicate trials grouped according to gene dispersal condition, with similar growth trajectories and endpoints. Labelled in group-descending order of mean population size after 600 bouts of mating: Gene dispersal condition is abbreviated as follows: ‘N’ = Near, ‘E’ = Equal, ‘F’ = Far, ‘P’ = Pollen, ‘O’ = Offspring, ‘Equal’, ‘Near’ and ‘Far’ refer to the probability of offspring and/or pollen being dispersed to or being received from a particular distance frame relative to a pistillate individual (for more detail see S1 Fig and S4 Table); A = NONP; B = NOEP; C = NOFP; D = EONP; E = EOEP; F = EOFP; G, H, I = FONP, FOEP, FOFP. For a complete list of logging pattern and gene dispersal condition for each trial above, see S1 Appendix.

https://doi.org/10.1371/journal.pone.0127745.g002

thumbnail
Download:
Fig 3. Mean population size for the entire fragment after 600 bouts of mating.

Trials varied in spatial logging patterns and gene dispersal distance (offspring and pollen). Groups labelled A-I indicate trials grouped according to gene dispersal condition in descending order of mean population size. Gene dispersal condition is abbreviated as follows: ‘N’ = Near, ‘E’ = Equal, ‘F’ = Far, ‘P’ = Pollen, ‘O’ = Offspring, ‘Equal’, ‘Near’ and ‘Far’ refer to the probability of offspring and/or pollen being dispersed to, or being received from, a particular distance frame relative to a pistillate individual (for more detail see S1 Fig and S4 Table). A = NONP; B = NOEP; C = NOFP; D = EONP; E = EOEP; F = EOFP; G = FONP; H = FOEP; I = FOFP. “Control” refers to un-logged fragment, and “Selective Logging” refers to 10% removal of only those individuals belonging to the highest age-classes.

https://doi.org/10.1371/journal.pone.0127745.g003

Those trials sharing the same spatial logging pattern but with variable gene dispersal conditions did not exhibit similarities in mean population size trajectories and endpoints (Groups A-I; Fig 2). A two-way ANOVA to show the relative effects of gene dispersal condition versus logging pattern on population growth, indicated that gene dispersal condition explained higher variance in this output (98.5%, p<0.0001, F = 79781.62, df = 8, N = 9) relative to logging pattern (0.2%, p<0.0001, F = 203.20, df = 7, N = 8) and the interaction of the two (0.2%, p<0.0001, F = 23.76, df = 56, N = 72; Fig 3).00200020Selective logging tended to have the lowest population growth for the six highest population-maximizing trials (Groups A-F; Groups G-I did not manifest biologically meaningful differences; Fig 3).

Mean Population Size—Recovery Regions

When fragment recovery regions only (combined logged areas only) were considered, trials with the same gene dispersal conditions did show some similarity in population growth trajectories and endpoints, although variation of endpoints among deforestation patterns was evident within gene dispersal groups (S2 Fig and Fig 4). A two-way ANOVA for showing the effect of offspring versus pollen dispersal distance on population growth in the recovery regions indicated that offspring dispersal distance had a greater impact, accounting for 80.9% of the mean variance relative to pollen dispersal (8.8%), and the interaction of the two dispersal types (7.4%).

thumbnail
Download:
Fig 4. Mean population size after 600 bouts of mating for recovery regions (all logged areas combined).

Trials varied in spatial logging patterns and gene dispersal conditions (offspring and pollen). Groups labelled A-I indicate trials grouped according to gene dispersal condition in descending order of mean population size: Gene dispersal condition is abbreviated as follows: ‘N’ = Near, ‘E’ = Equal, ‘F’ = Far, ‘P’ = Pollen, ‘O’ = Offspring, ‘Equal’, ‘Near’ and ‘Far’ refer to the probability of offspring and/or pollen being dispersed to or being received from a particular distance frame relative to a pistillate individual (for more detail see S1 Fig and S4 Table) A = NONP; B = NOEP; C = NOFP; D = EONP; E = EOEP; F = EOFP; G = FONP; H = FOEP; I = FOFP. Control” refers to un-logged fragment, and “Selective Logging” refers to 10% removal of only those individuals belonging to the highest age classes.

https://doi.org/10.1371/journal.pone.0127745.g004

Within gene dispersal groups, the recovery regions showed more variability in population recovery with regards to spatial logging pattern than did similar comparisons for the entire fragment data. In five of the nine gene dispersal condition groups, situating logged areas closer to the fragment borders (Strip, Corners) generated significantly lower mean population recovery compared to those trials that were logged further away from fragment borders (CentralSq, 4Squares, 3Strips; Groups A, B, D, E and F, Fig 4). However, the CentralSq extraction pattern trial with ‘Near’ offspring and ‘Far’ pollen dispersal (within Group C; Fig 4), had significantly lower levels of population recovery compared to other trials with the same gene dispersal condition. Group C was the only group in which this deforestation method exhibited such a pattern.

A two-way ANOVA showed that gene dispersal condition accounted for the most variance among recovery region endpoints (89.27%, p<0.0001, F = 26559.55, df = 8, N = 9). In the recovery regions, logging pattern, and the interaction of logging pattern and gene dispersal condition, accounted for 2.22% (p<0.0001, F = 1054.89, df = 5, N = 6) and 6.27% (p<0.0001, F = 373.03, df = 8, N = 9; Fig 4) of the variance respectively, this being more than that accounted for by these factors in the entire fragment. Thus, logging pattern, and the interaction of logging pattern and gene dispersal condition, caused greater variability in population growth in the recovery regions than in the entire fragment.

Founding allele retention—Entire fragment

Variation in alleles retained within the entire fragment appears to be influenced by gene dispersal groupings, showing a difference of 11.0% between the most extreme trials (Corners—‘Near’ offspring and pollen vs. Selective Logging—‘Far’ offspring and ‘Equal’ pollen; S3 Fig). Within gene dispersal groups (Groups A-I, S3 Fig), Selective Logging trials consistently displayed significantly lower levels of founding allele retention relative to other logging patterns. Disregarding these Selective Logging trials, within-group variability among all other logging patterns did not show greater than a 4.8% difference in founding alleles retained (S3 Fig).

A two-way ANOVA to depict relative contributions of gene dispersal condition versus spatial logging pattern in explaining variation in founding allele retention, indicated that gene dispersal condition accounted for the highest amount of variance (88.3%, p<0.0001, F = 15387.98, df = 8, N = 9) than spatial logging pattern (4.8%, p<0.0001, F = 951.57, df = 7, N = 8) or the interaction of the two (1.9%, p<0.0001, F = 46.55, df = 56, N = 72; S3 Fig). As expected, those trials and gene dispersal conditions that maximized population growth (‘Near’ followed by ‘Equal’ then ‘Far’ offspring dispersal) generally retained the highest number of founding alleles (see S1 Appendix; S3 Fig). Variation in offspring dispersal distance had a greater impact on alleles retained, as a two-way ANOVA indicated that offspring dispersal explained a mean variance of 90.3%, compared to pollen dispersal (3.7%) and the interaction of the two dispersal types (0.43%). Control trials responded significantly to variation in offspring dispersal, with ‘Near’ dispersal (Groups A-C) retaining the most alleles, followed by ‘Equal’ and ‘Far’ respectively (Groups D-F & G-I respectively; S3 Fig).

Founding allele retention—Recovery region

Compared to entire fragment results, there was a similar, but more pronounced decreasing trend for founding allele retention in recovery regions from offspring ‘Near’ dispersal trials to when offspring were dispersed to greater (‘Far’) distances (Fig 5). Variance explained by offspring, pollen dispersal and their interaction (as demonstrated by a two-way ANOVA), was similar to entire fragment trends (see S1 Appendix). Gene dispersal condition impacted allele retention the most, as a two-way ANOVA indicated that gene dispersal condition explained 97.3% of the variance (p<0.0001, F = 34685.28) relative to spatial logging pattern alterations (<0.01%, p<0.0001, F = 20.17) and the interaction of the two (0.8%, p<0.0001, F = 57.34; Fig 5). For gene dispersal groups A, B, D, E, and F (when offspring dispersal was either ‘Near’ or ‘Equal’), a greater retention of founding alleles for spatial logging patterns situated closer to the border (i.e., Corners and Strip) was found relative to more centrally located logging areas (CentralSq, 4Squares, 3Strips; Fig 5).

thumbnail
Download:
Fig 5. Mean founding allele retention after 600 generations for recovery regions (all logged areas combined).

Trials varied in spatial logging patterns and gene dispersal conditions (offspring and pollen). Groups labelled A-I indicate trials grouped according to gene dispersal condition in descending order of mean population size: Gene dispersal condition is abbreviated as follows: ‘N’ = Near, ‘E’ = Equal, ‘F’ = Far, ‘P’ = Pollen, ‘O’ = Offspring, ‘Equal’, ‘Near’ and ‘Far’ refer to the probability of offspring and/or pollen being dispersed to or being received from a particular distance frame relative to a pistillate individual (for more detail see S1 Fig and S4 Table): A = NOEP; B = NONP; C = NOFP; D = EONP; E = EOEP; F = EOFP; G = FONP; H = FOEP; I = FOFP. “Control” refers to un-logged fragment, and “Selective Logging” refers to 10% removal of only those individuals belonging to the highest age class.

https://doi.org/10.1371/journal.pone.0127745.g005

Population subdivision/inbreeding—Entire fragment

After 600 bouts of mating, Fit for the entire fragment for most dispersal condition groups and deforestation patterns indicated that moderate inbreeding/subdivision had occurred to varying degrees (Fig 6. and S4 Fig). Trials with ‘Near’ offspring and ‘Far’ pollen dispersal distances, (Group 1; Fig 6), showed increasing inbreeding and population subdivision, reaching moderate but increasing levels by the mating bout 600, with little sign that this trend would change with further bouts of mating. All other remaining gene dispersal conditions except for ‘Far’ offspring and pollen dispersal achieve a stable level of moderate inbreeding by approximately mating bout 175 (Groups 2 and 3; Fig 6). After 600 bouts of mating, trials varying in spatial logging patterns, and control trials (trials where no logging occurred) did not exhibit biologically meaningful differences in Fit values within gene dispersal groups (S4 Fig).

thumbnail
Download:
Fig 6. Mean Fit for trials across 600 bouts of mating in the entire fragment.

Trials varied in spatial logging patterns and gene dispersal distance (offspring and pollen). Groups labelled 1–4 indicate trials grouped according to gene dispersal condition, with similar growth trajectories and endpoints. In group descending order of mean Fit levels: Gene dispersal condition is abbreviated as follows: ‘N’ = Near, ‘E’ = Equal, ‘F’ = Far, ‘P’ = Pollen, ‘O’ = Offspring, ‘Equal’, ‘Near’ and ‘Far’ refer to the probability of offspring and/or pollen being dispersed to or being received from a particular distance frame relative to a pistillate individual (for more detail S1 Fig and S4 Table); 1 = FONP; 2 = EONP; EOFP; NOFP; NONP; 3 = FOEP, EOEP, NOEP; 4 = FOFP. For a complete list of logging pattern and gene dispersal condition for each trial above, see S1 Appendix.

https://doi.org/10.1371/journal.pone.0127745.g006

A two-way ANOVA analysis to demonstrate the relative effects of gene dispersal condition and spatial logging patterns on Fit levels showed that gene dispersal condition had the greatest impact (explaining 82.0% of the variance, p<0.0001, F = 4240.7, df = 8, N = 9) followed by the interaction of the two (0.6%, p<0.0001, F = 4.34, df = 56) and logging pattern respectively (0.2%, p<0.0001, F = 13.45, df = 7, N = 8; S4 Fig). A separate two-way ANOVA comparing the relative contributions of offspring versus pollen dispersal in explaining variation in Fit levels showed that the interaction of offspring and pollen dispersal had the greatest impact, explaining the highest mean variance (47.9%), followed by pollen dispersal (28.7%) and offspring dispersal (6.3%).

Population subdivision/inbreeding—Recovery regions

Fit trajectories and endpoints were more complex in the recovery regions relative to the entire fragment (S5 Fig). Trials ended with essentially moderate levels of inbreeding/subdivision to varying degrees, except some trials with ‘Far’ offspring and ‘Near’ pollen (Group 1; S5 Fig) exhibited trajectories suggestive of greater Fit levels being reached in future generations. Although a two-way ANOVA showed that gene dispersal condition explained the most variance in Fit (38.0%, p<0.0001, F = 475.4, df = 8, N = 9) compared to the interaction (6.2%, p<0.0001, F = 15.55, df = 40), and logging pattern (2.4%, p<0.0001, F = 47.6, df = 5; S6 Fig), the magnitude of this variance was much less than in the entire fragment. One particular trial (CentralSq, ‘Near’ offspring and ‘Far’ pollen, trial z; S5 Fig) showed unusually high Fit values across early generations (ranging from 0.126–0.138) relative to all other trials. A separate two-way ANOVA analysis for demonstrating the relative effect of offspring versus pollen dispersal distance, showed that offspring dispersal distance had the greatest impact on Fit levels in the recovery regions, explaining a mean variance of 21.3%, followed by the interaction of the two dispersal conditions (14.7%) and pollen dispersal (10.1%).

Discussion

This study explored the effects of variable spatial logging pattern and gene dispersal distances (via both offspring and pollen) on recovery population growth and genetic diversity dynamics for the plant functional type of a long-lived TLRF canopy tree species. Alternative combinations of logging pattern and gene dispersal distance affected this tropical population in different ways and to varying extents as described below. Different species and/or populations may respond quite distinctly to various extraction patterns depending on post-fragmentation dispersal attributes.

Gene Dispersal condition

These simulations demonstrated how populations or similar species with different gene dispersal characteristics will respond differently to alternate disturbance patterns within fragments. Most of the variation in mean population size, retention of founding alleles, and population subdivision/inbreeding levels for both the entire fragments as well as recovery regions was attributable to differences in gene dispersal condition. In fragments of the size used here (500 ha) and conditions simulated in this study, populations exhibiting ‘Near’ offspring and pollen dispersal exhibited the highest population growth rates leading to greater levels of founding allele retention. Higher levels of rare allele retention have been found to be associated with lower variance in the frequencies of common alleles, making populations more tractable to the process of natural selection and less so to random genetic drift ([45], work in progress). Thus, changes in offspring and/or pollen dispersal distances brought about by fragmentation acting on genetic diversity retention, can potentially have consequences for the evolutionary trajectory of populations.

To the best of our knowledge, this is the first study to compare the effects of varying offspring versus pollen dispersal distances in TLRF fragments for this particular plant functional type. Our results indicate that offspring dispersal distance seems to be of greater importance relative to pollen dispersal and the interaction of the two dispersal types, in terms of its impact on population growth and founding allele retention as it explains a higher level of output variation. Nevertheless, variation in pollen dispersal distance can cause reduced but significant alterations in population growth and genetic diversity retention. The relative role of offspring dispersal is reduced for variation seen in population subdivision and/or inbreeding where the interaction of the two dispersal types takes on greater importance in the entire fragment. While pollen disperses genes in the haploid state, offspring genes are disseminated in the diploid state and thus contribute more to the genetic neighborhood size [68, 69].

The comparative trials here show that population differentiation and inbreeding for the entire fragment and recovery regions have a marked increase in Fit (to moderate levels) for the ‘Far’ offspring /‘Near’ pollen dispersal condition compared to all other trials, which exhibited little or no genetic spatial differentiation or inbreeding. When pollen is received primarily from ‘Near’ distances, there is a greater chances of locating eligible pollen donors for mating since the ‘Near’ dispersal distance range would fall within the fragment area and not outside of it. However, once mating occurs and offspring are produced, a relatively large proportion of these are dispersed to ‘Far’ distances off the fragment, leaving behind a small number of surviving individuals. The repetition of this process over time apparently leads to the creation of increasingly smaller and isolated subpopulations, which are more susceptible to increased inbreeding levels [70, 23]. The reverse of this gene dispersal condition (‘Near’ offspring and ‘Far’ pollen dispersal) does not, in this study, lead to such an increase in Fit values, suggesting the latter conditions are more effective in facilitating gene flow. These results demonstrate how varying offspring and pollen dispersal distances can interact to produce differences in the spatial genetic structure of a population [14].

Taken together, the results here suggest that changes in the abundance and/or behavior of pollinators and/or offspring dispersers occurring after fragmentation of populations of tropical long-lived canopy tree species, or the occurrence of such differences among species, will lead to complex and sometimes pronounced variation in the demographic trends and genetics for such populations or species going forward. NG-type analyses can be useful in predicting those species most at risk, and “best practices” for deforestation pattern, in fragments of a given size, although these simulations stress the importance of obtaining accurate life-history information (e.g., gene dispersal kernel estimates) for target species.

Spatial logging pattern

Variations in spatial logging pattern explained lower levels of variance in population growth and genetic diversity levels compared to that accounted for by gene dispersal condition. This suggests that alterations in the spatial pattern of logging will not bring about large differences in resulting population recovery and population genetic dynamics, as would shifts in gene dispersal distances under the conditions modeled in this study. The most notable effect of spatial logging pattern detected from the results was that logging pattern frequently seemed to be of most consequence for population growth and founding allele retention in recovery regions when logging was situated closer to the border (e.g., Corners, Strip). Specifically, in five of the nine gene dispersal scenarios modeled, population recovery and allele retention were lower for deforestation occurring nearer borders.

Selective logging of the top 10% of the oldest or largest individuals tended to yield the lowest rates of population recovery and founding allele retention relative to the other extraction patterns. This supports the notion that moderate harvesting of non-timber products obtainable from a range of size-class groups could be more sustainable than harvesting for the largest trees only, as the latter puts greater pressure on the more reproductive size-classes [71].

Conservation implications

NG modeling can be used to model population trends under different fragment sizes, gene dispersal conditions, deforestation patterns, or other life-history characteristics that can be characterized in NG input, for species of concern. Since trends in population growth, genetic diversity retention, and/or inbreeding and subdivision here were, in some cases, highly sensitive to post-fragmentation alterations in gene dispersal conditions, one practical management technique might include interventions to potentially alter offspring and/or pollen dispersal distances (manual manipulations or alteration of dispersal vector agents) to reflect those that maximize population size and the retention of genetic diversity for the population and conditions simulated in this study. Even if a newly established fragment has not undergone internal deforestation, any subsequent changes in gene dispersal distances due to fragmentation can affect population sizes and genetic diversity retention to a great extent as suggested by the results from the control trials simulating unlogged fragments under varying gene dispersal conditions.

In this study, which explores the effects of both spatial logging pattern and gene dispersal distances (including both offspring and pollen dispersal distances), it was simpler to begin with a control population initially in genetic equilibrium in a spatially uniform landscape to determine the general effects of these variables separately, as well their synergistic effects. We refrained from choosing one of the numerous alternative population-genetic and/or spatially heterogeneous scenarios that could be modeled, as this would have been an arbitrary decision and made interpretation more complex. Nevertheless, a multitude of other complex non-equilibrium population and genetic history scenarios that may have occurred at different locations (e.g., genetic bottlenecks, founder effects etc.) as well as different kinds of spatially heterogeneous landscapes can be modeled by NEWGARDEN when site-specific information is available concerning the various user-defined input parameters. For example, if the history of a particular forest fragment is known or suspected, NEWGARDEN can be used to modify the characteristics of the initial population and geographical aspects of the forest to explore the demographic and population genetic consequences of such modifications. As tropical low land rain forests share similar general abiotic (e.g., precipitation and solar radiation levels, humidity, etc.) and biotic (low population densities, species diversity in guilds of offspring and pollen dispersers servicing plant taxa, soil microbes, etc.) characteristics, the general scenarios and conclusions described from this study for this plant functional type are not confined to one region of the tropics, but theoretically to tropical low land rain forests across the equatorial belt [72]. The diversity of the life-history characteristics of TLRF trees suggests that fragmented and deforested populations may exhibit a wide variety of intra- and interspecific responses. Thus, we emphasize that the trial comparisons here are not meant to be exhaustive. Rather, population responses under scenarios where numerous other features vary, such as fragment sizes, shapes, establishment densities, gene dispersal or other life history characteristics, removal regimens, etc., can be explored with NG.

Supporting Information

S1 Appendix. This file contains detailed methods which further elaborate on how the NEWGARDEN program functions and the life-history characteristics of the simulated population.

It also contains supporting information from the results section as well the complete figure legends for Figs 2 and 6, and the supporting figures S2 Fig and S5 Fig

https://doi.org/10.1371/journal.pone.0127745.s001

(DOCX)

S1 Dataset. This file contains the raw datasets from this research study.

https://doi.org/10.1371/journal.pone.0127745.s002

(XLSX)

S1 Fig. Nested offspring and pollen dispersal distance frames around a hypothetical offspring-producing individual (“x”) selected for mating.

This individual can be situated anywhere in the 500 ha fragment. The following distances are relative to individual “x” in the x and y direction: a = 32 grid points or 448 m, b = 64 grid points or 896 m, c = 96grid points or 1344 m, d = 128 grid points or 1792 m, e = 160 grid points or 2240 m. Diagram is approximately to scale.

https://doi.org/10.1371/journal.pone.0127745.s003

(TIF)

S2 Fig. Mean population growth across 600 generations in recovery regions (all logged areas combined).

Trials varied in spatial logging patterns and gene dispersal distance (offspring and pollen). Groups labelled A-I indicate trials grouped according to gene dispersal condition, with similar growth trajectories and endpoints. In descending order of mean population size: Logging pattern description is followed by gene dispersal condition abbreviated as follows: ‘N’ = Near, ‘E’ = Equal, ‘F’ = Far, ‘P’ = Pollen, ‘O’ = Offspring, ‘Equal’, ‘Near’ and ‘Far’ refer to the probability of offspring and/or pollen being dispersed to or being received from a particular distance frame relative to a pistillate individual (for more detail see S1 Fig and S4 Table); A = NONP, B = NOEP, C = NOFP, D = EONP, E = EOEP, F = EOFP, G = FONP, FOEP, FOFP. For a complete list of logging pattern and gene dispersal condition for each trial above, see S1 Appendix.

https://doi.org/10.1371/journal.pone.0127745.s004

(TIF)

S3 Fig. Mean founding allele retention after 600 bouts of mating in the entire fragment.

Trials varied in spatial logging patterns and gene dispersal distance (offspring and pollen). Groups labelled A-I indicate trials grouped according to gene dispersal condition in descending order of mean population size: Logging pattern description is followed by gene dispersal condition abbreviated as follows: ‘N’ = Near, ‘E’ = Equal, ‘F’ = Far, ‘P’ = Pollen, ‘O’ = Offspring, ‘Equal’, ‘Near’ and ‘Far’ refer to the probability of offspring and/or pollen being dispersed to or being received from a particular distance frame relative to a pistillate individual (for more detail see S1 Fig and S4 Table); A = NONP, B = NOEP, C = NOFP, D = EONP, E = EOEP, F = EOFP, G = FONP, H = FOEP, I = FOFP. “Control” refers to un-logged fragment, and “Selective Logging” refers to 10% removal of only those individuals belonging to the highest age classes.

https://doi.org/10.1371/journal.pone.0127745.s005

(TIF)

S4 Fig. Mean Fit after 600 bouts of mating in the entire fragment.

Trials varied in spatial logging patterns and gene dispersal conditions (offspring and pollen). Groups labelled 1–9 indicate trials grouped according to descending order of mean population, with spatial logging patterns in the legends appearing from left to right within each gene dispersal scenario: Gene dispersal conditions are abbreviated as follows: ‘N’ = Near, ‘E’ = Equal, ‘F’ = Far, ‘P’ = Pollen, ‘O’ = Offspring, ‘Equal’, ‘Near’ and ‘Far’ refer to the probability of offspring and/or pollen being dispersed to or being received from a particular distance frame relative to a pistillate individual (for more detail see S1 Fig and S4 Table); 1 = FONP, 2 = EOFP, 3 = EONP, 4 = NOFP, 5 = NONP, 6 = FOEP, 7 = EOEP, 8 = NOEP, 9 = FOFP. “Control” refers to un-logged fragment, and “Selective Logging” refers to 10% removal of only those individuals belonging to the highest age classes.

https://doi.org/10.1371/journal.pone.0127745.s006

(TIF)

S5 Fig. Mean Fit across 600 bouts of mating for recovery regions (all logged areas combined).

Trials varied in spatial logging patterns and gene dispersal distance (offspring and pollen dispersal). Group 1 indicates trials grouped according to gene dispersal condition, with similar growth trajectories and endpoints. In descending order of mean population size: Logging pattern description is followed by gene dispersal condition abbreviated as follows: ‘N’ = Near, ‘E’ = Equal, ‘F’ = Far, ‘P’ = Pollen, ‘O’ = Offspring, ‘Equal’, ‘Near’ and ‘Far’ refer to the probability of offspring and/or pollen being dispersed to or being received from a particular distance frame relative to a pistillate individual (for more detail see S1 Fig and S4 Table); 1 = FONP. For a complete list of logging pattern and gene dispersal condition for each trial above, see S1 Appendix.

https://doi.org/10.1371/journal.pone.0127745.s007

(TIF)

S6 Fig. Mean Fit after 600 bouts of mating for recovery regions (all logged areas combined).

Trials varied in spatial logging patterns and gene dispersal distance (offspring and pollen). Groups labelled 1–9 indicate trials grouped according to gene dispersal condition in descending order of mean population: Gene dispersal conditions are abbreviated as follows, with spatial logging pattern in the legend appearing from left to right within each gene dispersal scenario: ‘N’ = Near, ‘E’ = Equal, ‘F’ = Far, ‘P’ = Pollen, ‘O’ = Offspring, ‘Equal’, ‘Near’ and ‘Far’ refer to the probability of offspring and/or pollen being dispersed to or being received from a particular distance frame relative to a pistillate individual (S1 Fig and S4 Table); 1 = FONP, 2 = FOFP, 3 = FOEP, 4 = NOFP, 5 = EOFP, 6 = EONP, 7 = EOEP, 8 = NONP, 9 = NOEP. “Control” refers to un-logged fragment, and “Selective Logging” refers to 10% removal of only those individuals belonging to the highest age classes.

https://doi.org/10.1371/journal.pone.0127745.s008

(TIF)

S1 Table. Similarities and differences between NEWGARDEN (NG) and other forest growth models including stand models, process-based models, empirical models, gap models and hybrid models.

https://doi.org/10.1371/journal.pone.0127745.s009

(DOCX)

S2 Table. Age-specific rates of reproduction and pollen provisioning for the control equilibrium population.

https://doi.org/10.1371/journal.pone.0127745.s010

(DOCX)

S3 Table. Age-specific mortality rate for the control equilibrium population.

https://doi.org/10.1371/journal.pone.0127745.s011

(DOCX)

S4 Table. Offspring and pollen dispersal distance dimensions.

Distance frame dimensions and selection probabilities for offspring and pollen dispersal distance from or to (respectively) an offspring-producing individual in a given mating event for the “Equal”, “Near” and “Far” gene dispersal categories.

https://doi.org/10.1371/journal.pone.0127745.s012

(DOCX)

Acknowledgments

We thank T. Culley, S. Matter, E. Maurer, E. Teal, the Department of Biological Sciences and the Department of Mathematical Sciences at the University of Cincinnati. We also thank the Ohio Plant Biotechnology Consortium.

Author Contributions

Conceived and designed the experiments: YK SP SR. Performed the experiments: YK SP SR. Analyzed the data: YK SP SR. Contributed reagents/materials/analysis tools: YK SP SR. Wrote the paper: YK SP SR.

References

  1. 1. Connell JH (1978) Diversity in tropical rain forests and coral reefs. Science 199: 1302–1310. pmid:17840770
  2. 2. Gentry AH (1988) Changes in plant community diversity and floristic composition on environmental and geographical gradients. Annals of Missouri Botanical Garden 75: 1–34.
  3. 3. Myers N (1988) Threatened biotas: “hot spots” in tropical forests. Environmentalist 8:187–208. pmid:12322582
  4. 4. Kettle CJ (2014) Fragmentation genetics in tropical ecosystems: from fragmentation genetics to fragmentation genomics. Conservation Genetics 15: 1265–1268.
  5. 5. Rosa IM, Ahmed SE, Ewers RM (2014) The transparency, reliability and utility of tropical rainforest land-use and land-cover change models. Global Change Biology 20: 1707–1722. pmid:24399778
  6. 6. Figueroa-Esquivel EM, Puebla-Olivares F, Eguiarte LE, Nunez-Farfan J (2010) Genetic structure of a bird-dispersed tropical tree (Dendropanax arboreus) in a fragmented landscape in Mexico. Revista Mexciana de Biodiversidad 81: 789–800.
  7. 7. Hill JK, Gray MA, Khen CV, Benedick S, Tawatao N, Hamer KC, et al. (2011) Ecological impacts of tropical forest fragmentation: how consistent are patterns in species richness and nestedness?. Phil. Trans. R. Soc. B. 366:3265–3276. pmid:22006967
  8. 8. Da Silva EF, de Oliveira CA, Lins-e-Silva AC, Rodal MJ (2008) Diversity and genetic structure of naturally fragmented populations of Tapirira guianensis Aubl. in Northeaster Brazil. Bioremediation, Biodiversity and Bioavailability 2: 35–40.
  9. 9. Laurance WF, Sayer J, Cassman KG (2014) Agricultural expansion and its impacts on tropical nature. Trends in Ecology and Evolution 29: 107–116. pmid:24388286
  10. 10. Grimes A, Loomis S, Jahnige P, Burnham M, Onthank K, Alarcón R, et al. (1994) Valuing the rain forest: the economic value of nontimber forest products in Ecuador. Royal Swedish Academy of Sciences 23: 405:410.
  11. 11. Laurance W (1999) Reflections on the tropical deforestation crisis. Biological Conservation. 91:109–117.
  12. 12. Soares-Filho BS, Nepstad DC, Curran LM, Cerqueira GC, Garcia RA, Ramos CA, et al. (2006) Modelling conservation in the Amazon basin. Nature. 440:520–523. pmid:16554817
  13. 13. Ward M, Dick CW, Gribel R, Lowe aj (2005) To self, or not to self…a review of outcrossing and pollen-mediated gene flow in neotropical trees. Heredity 95: 246–254. pmid:16094304
  14. 14. Zanella CM, Bruxel M, Paggi GM, Goetze M, Buttow MV, Cidade FW, et al. (2011) Genetic structure and phenotypic variation in wild populations of the medicinal tetraploid species Bromelia antiacantha (Bromeliaceae). American Journal of Botany 98: 1511–1519. pmid:21865505
  15. 15. Nathan R, Schurr FM, Spiegel O, Steinitz O, Trakhtenbrot A, Tsoar A (2008) Mechanisms of long-distance seed dispersal. Trends in Ecology and Evolution 23: 638–647. pmid:18823680
  16. 16. Berens DG, Griebeler EM, Braun C, Chituyi BB, Nathan R, Böhning-Gaese K (2013) Changes of effective gene dispersal distances by pollen and seeds across successive life stages in a tropical tree. Oikos 122: 1616–1625.
  17. 17. Ghazoul J (2005) Pollen and seed dispersal among dispersed plants. Biological Reviews 80: 413–443. pmid:16094807
  18. 18. Beckman NG, Rogers HS (2013) Consequences of seed dispersal for plant recruitment in tropical forests: Interactions within the seedscape. Biotropica 45: 666–681.
  19. 19. Schulke B, Waser NM (2001) Long-distance pollinator flights and pollen dispersal between populations of Delphinium nuttallianum. Oecologia 127: 239–245. pmid:24577655
  20. 20. Laurance WF, Goosem M, Laurance SG (2009) Impacts of roads and linear clearings on tropical forests. Trends in Ecology and Evolution 24: 659–669. pmid:19748151
  21. 21. Korfanta NM, Newmark WD, Kauffman MJ (2012) Long-term demographic consequences of habitat fragmentation to a tropical understory bird community. Ecology 93: 2548–2559. pmid:23431586
  22. 22. Guariguata MR, Arias-Le Claire H, Jones G (2002) Tree seed fate in a logged and fragmented forest landscape, Northeastern Costa Rica. Biotropica 34: 405–415.
  23. 23. Quesada M, Herrerias-Diego Y, Lobo JA, Sanchez-Montoya G, Rosas F, Aguilar R (2013) Long-term effects of habitat fragmentation on mating patterns and gene flow of a tropical dry forest tree, Ceiba aesculifolia (Malvaceae: Bombacoideae). American Journal of Botany 11: 1095–1101.
  24. 24. Huth A, Ditzer T (2001) Long-term impacts of logging in a tropical rain forest—a simulation study. Forest Ecology and Management 142: 33–51.
  25. 25. Putz F, Sist P, Fredericksen T, Dykstra D (2009) Reduced-impact logging: Challenges and opportunities. Forest Ecology and Management. 256: 1427–1433.
  26. 26. Corlett T, Primack R (2010) Tropical rain forests: An ecological and biogeographical comparison. Blackwell Science Ltd., West Sussex, UK pp33–75.
    • 27. Carneiro FS, Lacerda AE, Lemes MR, Gribel R, Kanashiro M, Wadt LH, et al. (2011) Effects of selective logging on the mating system and pollen dispersal of Hymenaea courbaril L. (Leguminosae) in the Eastern Brazilian Amazon as revealed by microsatellite analysis. Forest Ecology and Management 262:1758–1765.
    • 28. Gaveau DL, Sloan S, Molidena E, Yaen H, Sheil D, Abram NK, et al. (2014) Four decades of forest persistence, clearance and logging in Borneo. PLOS ONE (9):e101654. pmid:25029192
    • 29. Sist P, Mazzei L, Blanc L, Rutishauser E (2014) Large trees as key elements of carbon storage and dynamics after selective logging in the Eastern Amazon. Forest Ecology and Management. 318: 103–109.
    • 30. Baroloto C, Herault B, Paine CE, Massot H, Blanc L, Bonal D, et al. (2012) Contrasting taxonomic and functional responses of a tropical tree community to selective logging. Journal of Applied Ecology. 49:861–870,
    • 31. Vinson CC, Kanashiro M, Harris SA, Boshier DH (2015) Impacts of selective logging on inbreeding and gene flow in two Amazonian timber species with contrasting ecological and reproductive characterisitcs. Molecular Ecology 24: 38–53. pmid:25402015
    • 32. Jennings S, Brown N, Boshier D, Whitmore T, Lopes J (2001) Ecology provides a pragmatic solution to the maintenance of genetic diversity in sustainably managed tropical rain forests. Forest Ecology and Management 154: 1–10.
    • 33. West TA, Vidal E, Putz FE (2014) Forest biomass recovery after conventional and reduced-impact logging in Amazonian Brazil. Forest Ecology and Management 314: 59–63.
    • 34. Alvarez-Buyalla ER, Garcia-Barrios R, Lara-Moreno C, Martinez-Ramos M (1996) Demographic and genetic models in conservation biology: applications and perspectives for tropical rain forest tree species. Annual Review of Ecology and Systematics 27: 387–421.
    • 35. Lowe AJ, Boshier D, Ward M, Bacles CF, Navarro C (2005) Genetic resource impacts of habitat loss and degradation; reconciling empirical evidence and predicted theory for neotropical trees. Heredity 95: 255–273. pmid:16094300
    • 36. Pearce D, Putz FE, Vanclay JK (2003) Sustainable forestry in the tropics: panacea or folly? Forest Ecology and Management 172: 229–247.
    • 37. Reisch C, Bernhardt-Romermann M (2014) The impact of study design and life history traits on genetic variation of plants determined with AFLPs. Plant Ecology 215: 1493–1511.
    • 38. Babweteera F, Brown N (2010) Spatial patterns of tree recruitment in East African tropical forests that have lost their vertebrate seed dispersers. Journal of Tropical Ecology 26: 193–203.
    • 39. Köhler P, Ditzer T, Huth A (2000) Concepts for the aggregation of tropical tree species into functional types and the application to Sabah’s lowland rain forests. Journal of Tropical Ecology 16: 591–602.
    • 40. Rumana R, Langenfeld-Heyser R, Finkeldey R, Polle A (2009) Functional anatomy of five endangered tropical species of the family Dipterocarpaceae. Trees 23: 521–529.
    • 41. Carneiro FD, Degen B, Kanashiro M, de Lacerda AE, Sebbenn AM (2009) High levels of pollen dispersal detected through paternity analysis from a continuous Symphonia globulifera population in the Brazilian Amazon. Forest Ecology and Management 258: 1260–1266.
    • 42. Vanclay JK (2003) Realizing opportunities in forest growth modeling. Canadian Journal of Forest Research 33: 536–541.
    • 43. Haymes KL, Fox GA (2012) Variation among individuals in cone production in Pinus palustris (Pinaceae). American Journal of Botany 99: 640–545. pmid:22434774
    • 44. Pelikan S, Heywood S, Rogstad SH (In press) Weed suppression success can depend on removal pattern and gene dispersal distance: Modeling callery pear. Weed Science.
      • 45. Rogstad SH, Pelikan S (2011) Genetic diversity in establishing plant populations: Founder number and geometry. Enfield: Science Publishers. 360 pp.
        • 46. Kashimshetty Y, Simkins M, Pelikan S, Rogstad SH (2012) Founder placement and gene dispersal affect population growth and genetic diversity in restoration plantings of American chestnut. In: Caliskan M, editor. Genetic Diversity in Plants. Shanghai: InTech Press. pp 375–390.
          • 47. Pelikan S, Rogstad SH (2013) NEWGARDEN: a computer program to model the population dynamics and genetics of establishing and fragmented plant populations. Conservation Genetics Resources 5: 857–862.
          • 48. Rogstad SH, Pelikan s (2013a) Plant species restoration: effects of different founding patterns on sustaining future population size and genetic diversity. Sustainability 5: 1304–1316.
          • 49. Rogstad SH, Pelikan s (2013b) Monitoring endangered species populations: Gene dispersal can have pronounced effects on the relationship between census size and genetic diversity. American Journal of Plant Sciences 4: 1932–1937.
          • 50. Hartl DL, Clark AG (1997) Principles of population genetics. Sunderland: Sinauer Associates. pp 257–287
            • 51. Liu J, Ashton PS (1995) Individual-based simulation models for forest succession and management. Forest Ecology and Management 73: 157–175.
            • 52. Bugmann H. (2001) A review of forest gap models. Climatic Change 51:259–305.
            • 53. Landsberg JJ, Sands P (2011) Modelling tree growth: concepts and review. In: Physiological ecology of forest production: Principles, Processes and Models. Burlington: Academic Press Elsevier Inc. pp 221–240.
              • 54. Chudnoff M (1980) Tropical timbers of the world. Madison: U.S. Department of Agriculture. 831 p.
                • 55. Berryman A. (1991) Stabilization or regulation: what it all means!. Oecologia 86: 140–143.
                • 56. Gill GE Jr., Fowler RT, Mori SA (1998) Pollination biology of Symphonia globulifera (Clusiaceae) in Central and French Guiana. Biotropica 30: 139–144.
                • 57. Budde KB, Gonzalez-Martinez SC, Hardy OJ, Heuertz M (2013) The ancient tropical rainforest tree Symphonia globulifera L. f (Clusiaceae) was not restricted to postulated Pleistocene refugia in Atlantic Equatorial Africa. Heredity 111: 66–76. pmid:23572126
                • 58. Forest Research Institute of Malaysia (FRIM) (1993) Distribution of Tree Species in the Fifty Hectare Research Plot at Pasoh Forest Reserve. Selangor Darul Ehsan: FRIM. 454pp
                  • 59. Degen B, Bandou E, Caron H (2004) Limited pollen dispersal and biparental inbreeding in Symphonia globulifera in French Guiana. Heredity 93: 585–59. pmid:15316558
                  • 60. Metcalf C, Horvitz C, Tuljapurkar S, Clark D (2009) A time to grow and a time to die: a new way to analyze the dynamics of size, light, age, and death of tropical trees. Ecology 90: 2766–2778. pmid:19886486
                  • 61. Condit R, Ashton PS, Manokaran N, LaFrankie JV, Hubbell SP, Foster RB (1999) Dynamics of the forest communities at Pasoh and Barro Colorado: comparing two 50-ha plots. Phil. Trans. R. Soc. Lond. B 354: 1739–1748. pmid:11605618
                  • 62. Clark DA, Clark DB (1994) Climate-induced annual variation in canopy tree growth in a Costa Rican tropical rain forest. Journal of Ecology 82: 856–872.
                  • 63. Allendorf FW, Luikart G (2001) Conservation and the genetics of populations. MA: Blackwell publishing. pp124–130.
                    • 64. Blount ZD, Barrick JE, Davidson CJ, Lenski RE (2012) Genomic analysis of a key innovation in an experimental Escherichia coli population. Nature 489: 513–518. pmid:22992527
                    • 65. Soria-Carrasco V, Gompert Z, Comeault AA, Farkas TE, Parchman TL, Johnston JS, et al. (2014) Stick Insect Genomes Reveal Natural Selection’s Role in Parallel Speciation Science 344:738–742. pmid:24833390
                    • 66. Zhao L, Saelao P, Jones CD, Begun DJ (2014) Origin and Spread of de Novo Genes in Drosophila melanogaster populations. Science 343:769–772. pmid:24457212
                    • 67. Aldrich P, Hamrick J (1998) Reproductive dominance of pasture trees in a fragmented tropical forest mosaic. Science 281:103–105. pmid:9651242
                    • 68. Hamilton MB (1999) Tropical tree gene flow and seed dispersal. Nature 401: 129–130.
                    • 69. Aguilée R, Shaw FH, Rousset F, Shaw RG, Ronce O (2013) How does pollen versus seed dispersal affect niche evolution. Evolution 67:792–805. pmid:23461328
                    • 70. Paul J, Budd C, Freeland J (2013) Conservation genetics of an endangered orchid in eastern China. Conservation Genetics 14: 195–204.
                    • 71. Laurance WF, Useche DC, Rendeiro J, Kalka M, Bradshaw CJ, Sloan SP, et al. (2012) Averting biodiversity collapse in tropical forest protected areas. Nature 489: 290–294. pmid:22832582
                    • 72. Kircher J (2011) Tropical Ecology. Princeton University Press. New Jersey USA. pp-6, 79–100, 154–188