Abstract
Determining how tropical tree populations subject to selective felling (logging) pressure may be conserved is a crucial issue for forest management and studying this issue requires a comprehensive understanding of the relationships between population demography and gene flow. We used a simulation model, SELVA, to study (1) the relative impact of demographic factors (juvenile mortality, felling regime) and genetic factors (selfing, number and location of fathers, mating success) on long-term genetic diversity; and (2) the impact of different felling regimes on population size versus genetic diversity. Impact was measured by means of model sensitivity analyses. Juvenile mortality had the highest impact on the number of alleles and genotypes, and on the genetic distance between the original and final populations. Selfing had the greatest impact on observed heterozygote frequency and fixation index. Other factors and interactions had only minor effects. Overall, felling had a greater impact on population size than on genetic diversity. Interestingly, populations under relatively low felling pressure even had a somewhat lower fixation index than undisturbed populations (no felling). We conclude that demographic processes such as juvenile mortality should be modelled thoroughly to obtain reliable long-term predictions of genetic diversity. Mortality in selfed and outcrossed progenies should be modelled explicitly by taking inbreeding depression into account. The modelling of selfing based on population rate appeared to be oversimplifying and should account for inter-tree variation. Forest management should pay particular attention to the regeneration capacities of felled species.
Similar content being viewed by others
References
André T, Lemes MR, Grogan J et al (2008) Post-logging loss of genetic diversity in a mahogany (Swietenia macrophylla King, Meliaceae) population in Brazilian Amazonia. For Ecol Manag 255:340–345
Asner GP, Knapp DE, Broadbent EN et al (2005) Selective logging in the Brazilian Amazon. Science 310:480–482
Caron H, Dutech C, Bandou E (1998) Variations spatiotemporelles du régime de reproduction de Dicorynia guianensis Amshoff (Caesalpiniaceae) en forêt guyanaise. Genet Sel Evol 30(Suppl 1):S153–S166
Caron H, Dutech C, Bandou E (2004) Reproductive phenology and mating system of six tree species in Paracou stands. In: Gourlet-Fleury S, Guehl J-M, Laroussinie O (eds) Ecology and management of a Neotropical Rainforest. Elsevier, Paris, pp 149–159
Cloutier D, Kanashiro M, Ciampi AY et al (2007) Impact of selective logging on inbreeding and gene dispersal in an Amazonian tree population of Carapa guianensis Aubl. Mol Ecol 16:797–809
Cornelius JP, Navarro CM, Wightman KE et al (2005) Is mahogany dysgenically selected? Environ Conserv 32:129–139
de Coligny F (2007) Efficient building of forestry modelling software with the Capsis methodology. In: Fourcaud T, Zhang XP (eds) Plant growth modeling and applications. Proceedings of PMA06. IEEE Computer Society, Los Alamitos, California, pp 216–222
de Lacerda AEB, Kanashiro M, Sebbenn AM (2008) Effects of reduced impact logging on genetic diversity and spatial genetic structure of a Hymenaea courbaril population in the Brazilian Amazon Forest. For Ecol Manag 255:1034–1043
Degen B, Roubik DW (2004) Effects of animal pollination on pollen dispersal, selfing and effective population size of tropical trees: a simulation study. Biotropica 36:165–179
Degen B, Gregorius HR, Scholz F (1996) ECO-GENE, a model for simulation studies on the spatial and temporal dynamics of genetic structures of tree populations. Silvae Genet 45:323–329
Degen B, Blanc L, Caron H et al (2006) Impact of selective logging on genetic composition and demographic structure of four tropical tree species. Biol Conserv 131:386–401
Dessard H, Picard N, Pélissier R et al (2004) Spatial patterns of the most abundant tree species. In: Gourlet-Fleury S, Guehl J-M, Laroussinie O (eds) Ecology and management of a Neotropical Rainforest. Elsevier, Paris, pp 177–190
Dreyfus P, Pichot C, de Coligny F et al (2005) Couplage de modèles de flux de gènes et de modèles de dynamique forestière. Actes BRG 5:231–250
Finkeldey R, Ziehe M (2004) Genetic implications of silvicultural regimes. For Ecol Manag 197:231–244
Gillies ACM, Navarro C, Lowe AJ et al (1999) Genetic diversity in Mesoamerican populations of mahogany (Swietenia macrophylla), assessed using RAPDs. Heredity 83:722–732
Gourlet-Fleury S (1997) Modélisation individuelle spatialement explicite de la dynamique d’un peuplement de forêt dense tropicale humide (dispositif de Paracou – Guyane française). Doctoral Thesis, Université Lyon 1, Villeurbanne, France
Gourlet-Fleury S, Houllier F (2000) Modelling diameter increment in a lowland evergreen rain forest in French Guiana. For Ecol Manag 131:269–289
Gourlet-Fleury S, Guehl J-M, Laroussinie O (eds) (2004a) Ecology and management of a Neotropical Rainforest. Elsevier, Paris
Gourlet-Fleury S, Ferry B, Molino JF et al (2004b) Experimental plots: key features. In: Gourlet-Fleury S, Guehl J-M, Laroussinie O (eds) Ecology and management of a Neotropical Rainforest. Elsevier, Paris, pp 3–30
Gourlet-Fleury S, Cornu G, Jésel S et al (2005) Using models to predict recovery and assess tree species vulnerability in logged tropical forests: a case study from French Guiana. For Ecol Manag 209:69–86
Hufford KM, Hamrick JL (2003) Viability selection at three early life stages of the tropical tree, Platypodium elegans (Fabaceae, Papilionoideae). Evolution 57:518–526
Jennings SB, Brown ND, Boshier DH et al (2001) Ecology provides a pragmatic solution to the maintenance of genetic diversity in sustainably managed tropical rainforest. For Ecol Manag 154:1–10
Jésel S (2005) Ecologie et dynamique de la régénération de Dicorynia guianensis (Caesalpiniaceae) dans une forêt guyanaise. Doctoral Thesis, INA P-G, Paris, France
Latouche-Hallé C, Ramboer A, Bandou E et al (2002) Isolation and characterization of microsatellite markers in the tropical tree species Dicorynia guianensis (Caesalpinaceae). Mol Ecol Notes 2:228–230
Latouche-Hallé C, Ramboer A, Bandou E et al (2003) Nuclear and chloroplast genetic structure indicate fine-scale spatial dynamics in a neotropical tree population. Heredity 91:181–190
Latouche-Hallé C, Ramboer A, Bandou E et al (2004) Long-distance pollen flow and tolerance to selfing in a neotropical tree species. Mol Ecol 13:1055–1064
Loubry D (1993) Les paradoxes de l’Angélique (Dicorynia guianensis Amshoff): dissémination et parasitisme des graines avant dispersion chez un arbre anémochore de forêt guyanaise. Rev Ecol (Terre Vie) 48:353–363
Lourmas M, Kjellberg F, Dessard H et al (2007) Reduced density due to logging and its consequences on mating system and pollen flow in the African mahogany Entandrophragma cylindricum. Heredity 99:151–160
Lowe AJ, Boshier D, Ward M et al (2005) Genetic resource impacts of habitat loss and degradation; reconciling empirical evidence and predicted theory for neotropical trees. Heredity 95:255–273
Nei M (1972) Genetic distance between populations. Am Nat 106:283–292
Neuenschwander S, Hospital F, Guillaume F et al (2008) quantiNemo: an individual-based program to simulate quantitative traits with explicit genetic architecture in a dynamic metapopulation. Bioinformatics 24:1552–1553
Oostermeijer JGB, Luijten SH, den Nijs JCM (2003) Integrating demographic and genetic approaches in plant conservation. Biol Conserv 113:389–398
Pfrender ME, Spitze K, Hicks J et al (2000) Lack of concordance between genetic diversity estimates at the molecular and quantitative trait levels. Conserv Genet 1:263–269
Phillips PD, Thompson IS, Silva JNM et al (2004) Scaling up models of tree competition for tropical forest population genetics simulation. Ecol Model 180:419–434
Reed DH, Frankham R (2001) How closely correlated are molecular and quantitative measures of genetic variation? A meta-analysis. Evolution 55:1095–1103
Saltelli A, Tarantola S, Campolongo F et al (2004) Sensitivity analysis in practice. Wiley, Chichester
Scofield DG, Schultz ST (2006) Mitosis, stature and evolution of plant mating systems: low-Φ and high-Φ plants. Proc R Soc B 273:275–282
Sebbenn AM, Degen B, Azevedo VCR et al (2008) Modelling the long-term impacts of selective logging on genetic diversity and demographic structure of four tropical tree species in the Amazon forest. For Ecol Manag 254:335–349
Silva MB, Kanashiro M, Ciampi AY et al (2008) Genetic effects of selective logging and pollen gene flow in a low-density population of the dioecious tropical tree Bagassa guianensis in the Brazilian Amazon. For Ecol Manag 255:1548–1558
van Gardingen PR, Valle D, Thompson I (2006) Evaluation of yield regulation options for primary forest in Tapajós National Forest, Brazil. For Ecol Manag 231:184–195
Ward M, Dick CW, Gribel R et al (2005) To self, or not to self…a review of outcrossing and pollen-mediated gene flow in neotropical trees. Heredity 95:246–254
Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution 38:1358–1370
Wernsdörfer H, Rossi V, Cornu G et al (2008) Impact of uncertainty in tree mortality on the predictions of a tropical forest dynamics model. Ecol Model 218:290–306
Acknowledgements
We are grateful to Sylvie Oddou-Muratorio of INRA (French National Institute for Agricultural Research) Avignon and to Ivan Scotti of INRA Kourou (French Guiana) for valuable discussions on the demography and gene flow of forest tree species. Moreover, we thank two anonymous reviewers for constructive and helpful comments on an earlier version of the manuscript. The work was funded by a joint post-doctoral fellowship from CIRAD (French Agricultural Research Centre for International Development) and INRA.
Author information
Authors and Affiliations
Corresponding author
Appendices
Appendix 1: Gene flow sub-model
The description of the D. guianensis gene flow sub-model refers to the grey boxes in the flow chart (Fig. 1).
Number and clustering of seeds
The number of seeds, N seed, produced by a given mother tree at a given point in time was governed by forest dynamics. All seeds possessed the maternal genotype of the mother tree. To attribute the genotypes of several fathers, the seeds were subdivided into clusters with the number of clusters corresponding to the number of fathers, N father (=15 by default). The number of seeds per cluster, N cluster, was equal between clusters: N cluster = N seed/N father.
Search for fathers
One father was determined for each seed cluster by subsequently checking the occurrence of selfing and, if applicable, the location of the father.
Selfing
Selfing was a random event occurring with the probability P selfing (Table 1). In the event of selfing, the paternal genotype was the same as the maternal genotype. In the event of outcrossing (i.e. no selfing), the next step was to determine the location of the father.
Location of fathers
Fathers could be located both outside and inside the study area. The random event of a father being located outside the area occurred with the probability P outside (Table 1). If this event occurred, a paternal genotype was drawn at random from an allele frequency distribution, called a pollen cloud. The pollen cloud included allele frequencies for six loci, where the number of alleles per locus ranged between 5 and 15. Allele frequencies were based on 246 seeds collected inside the study area (Latouche-Hallé et al. 2004; samples from outside the area were not available), hypothesising that the observed seed genotypes represented the male allele frequencies occurring outside the area. In the event of the father being located inside the area, which occurred with the probability 1 − P outside, a father tree was drawn at random from among the population of potential father trees inside the area. This population included all trees ≥25 cm dbh apart from the mother tree, as selfing had already been checked in the previous step.
Mating success
For the random drawing of a father tree inside the area, the default setting accounted for an effect of dbh on mating success (Latouche-Hallé et al. 2004; Table 1). Potential father trees were classified by dbh and a weight was attributed to each tree according to its dbh class. Larger trees had higher mating success. As an alternative setting, a father was drawn independently of its traits. For both settings, father trees were drawn with replacement.
Miscellaneous
If the setting was such that fathers could be located both outside and inside the study area (i.e. P outside ≠ 0 and P outside ≠ 1), then P outside was adjusted to account for changes in the population of potential father trees occurring inside the area. Such changes could occur in the course of a simulation run due to mortality or felling, for instance. We assumed that P outside increased if the potential amount of pollen arriving from inside the area decreased, and vice versa. We also assumed an ideal D. guianensis population outside the area, which was undisturbed or managed in a manner such that pollen production was unaffected; the pollen cloud was constant at all times in a simulation run. We used two methods to calculate the change in P outside, depending on the mating success setting.
If mating success was independent of tree traits, P outside was adjusted to the number of potential father trees occurring inside the area at a given point in time during a simulation run, N pot. Let N pot_mean be the mean number of potential father trees occurring inside the area during a simulation run. Then, a fictitious number of potential father trees occurring outside the area can be calculated as N fict = N pot_mean × P outside/(1 − P outside). We assumed N fict to be constant at all time points during a simulation run. Based on this, at a given point in time, the probability of a father being located outside the area was calculated as P′outside = N fict/(N fict + N pot). To illustrate this, we plotted P′outside in relation to N pot for P outside = 0.62 and N pot_mean = 117 (Fig. 4). Note that P′outside clearly increased as values of N pot decreased. In contrast, a considerable pollen flow from outside the area was maintained even if N pot reached very high (unrealistic) values, e.g. P′outside > 0.3 for N pot = 400.
If mating success was weighted by dbh class, a similar method was applied. But instead of adjusting P outside to N pot, P outside was adjusted to the sum of the weights of the potential father trees occurring inside the area at a given point in time during a simulation run, ∑ pot. Let ∑ pot_mean be the sum of the weights of the average population of potential father trees occurring inside the area during a simulation run, and let ∑ fict = ∑ pot_mean × P outside/(1 − P outside) be the fictitious sum of the weights of potential fathers occurring outside the area, then P″outside = ∑ fict/(∑ fict + ∑ pot).
In the case of P outside = 0, father trees were always drawn from inside the area. Thus, for numerical reasons, we had to consider the special case where only one mother tree but no potential father tree was left inside the area (e.g. due to high mortality). In this special case, the father tree corresponded to the mother tree (selfing).
Appendix 2: Sensitivity measures
The sensitivity of an output variable Y to one input factor X i (first order effect) is measured as the ratio between the output variance V i, due to X i, and the total output variance V(Y) (Saltelli et al. 2004; Wernsdörfer et al. 2008):
Similarly, the sensitivity of Y to two input factors X i, X j (second order effect) and three input factors X i, X j, X m (third order effect) is measured as
and
where V ij and V ijm are the output variances due to X i, X j and X i, X j, X m, respectively. The variances V i, V ij and V ijm are calculated as
and
where the expectation E is approximated as a mean.
Rights and permissions
About this article
Cite this article
Wernsdörfer, H., Caron, H., Gerber, S. et al. Relationships between demography and gene flow and their importance for the conservation of tree populations in tropical forests under selective felling regimes. Conserv Genet 12, 15–29 (2011). https://doi.org/10.1007/s10592-009-9983-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10592-009-9983-0