Multigenerational genetic analysis of tropical secondary regeneration in a canopy palm

Iriartea deltoidea (Arecaceae) is an abundant canopy palm with a wide geographic distribution in Neotropical wet forests. We analyzed the genetic profile across three generations of Iriartea within a 43-ha area encompassing two areas of second-growth and adjoining old-growth forest at La Selva Biological Field Station in northeastern Costa Rica. A total of 311 reproductively mature trees, 99 large saplings, 207 small saplings, and 601 seedlings were genotyped using 141 AFLP loci. Parentage analysis revealed high dispersal distances, both for seed (over 2.3 km) and pollen (over 3.8 km), indicating a large genetic neighborhood within La Selva Biological Station. In a 20-ha area of second growth, the founding palm population was dominated by a small number of parental trees located in the adjacent old-growth forest; two old-growth trees contributed 48% of the second-growth genes. The genetic diversity of reproductively mature trees in this second-growth forest was significantly reduced compared to adjacent old-growth forest. Within 400 m of the border with old-growth forest, we observed a similar reduction of genetic diversity in saplings, and an even greater loss of genetic diversity in the second generation of seedlings. Nearly half of these seedlings were offspring of local parents. In contrast, in the distant portion of second-growth forest (400-800 m from the old-growth border), parentage analysis showed that 40% of seedlings originated from outside the study area and only 10% were offspring of local parents. These high levels of gene flow maintained genetic diversity in saplings and seedlings similar to levels observed in old-growth forest. Our findings highlight the importance of gene flow from diverse seed and pollen sources for sustaining levels of genetic diversity of tree populations in second-growth forests.     

Ethnobotany of Woody Species in Second-Growth, Old-Growth, and Selectively Logged Forests of Northeastern Costa Rica

Abstract: We assessed quantitatively the woody species used for timber, medicine, and other products in 10 tropical wet-forest stands with different land-use histories in the Atlantic lowlands of northeastern Costa Rica. Species were classified into 20 use categories based on regional ethnobotanical studies. Three size classes of woody vegetation were sampled in nested, contiguous plots along transects: trees (≥5 cm diameter at breast height [dbh]), saplings (>1 m high, <5 cm dbh), and seedlings (>20 cm high, <1 m high). Our study included five second-growth stands, three old-growth stands, and two selectively logged stands. Of the 459 woody species surveyed, 70% of the species and 86% of the total number of individuals had at least one use. Overall, species richness was highest for medicinal species (167 species). Absolute and relative abundance of medicinal and timber trees was significantly higher in second-growth stands than in old-growth and selectively logged stands. For 8 of the 15 use categories examined statistically, stem density showed no significant differences across forest types for any stem size class. Young, tropical, second-growth forests and selectively logged forests have high utilitarian as well as conservation value and will likely become important sources of forest products. The success of secondary forest regeneration, however, depends critically upon conservation of genetically diverse source populations in forest fragments and protected old-growth stands.     

A novel statistical method for classifying habitat generalists and specialists

We develop a novel statistical approach for classifying generalists and specialists in two distinct habitats. Using a multinomial model based on estimated species relative abundance in two habitats, our method minimizes bias due to differences in sampling intensities between two habitat types as well as bias due to insufficient sampling within each habitat. The method permits a robust statistical classification of habitat specialists and generalists, without excluding rare species a priori. Based on a user-defined specialization threshold, the model classifies species into one of four groups: (1) generalist; (2) habitat A specialist; (3) habitat B specialist; and (4) too rare to classify with confidence. We illustrate our multinomial classification method using two contrasting data sets: (1) bird abundance in woodland and heath habitats in southeastern Australia and (2) tree abundance in second-growth (SG) and old-growth (OG) rain forests in the Caribbean lowlands of northeastern Costa Rica. We evaluate the multinomial model in detail for the tree data set. Our results for birds were highly concordant with a previous nonstatistical classification, but our method classified a higher fraction (57.7%) of bird species with statistical confidence. Based on a conservative specialization threshold and adjustment for multiple comparisons, 64.4% of tree species in the full sample were too rare to classify with confidence. Among the species classified, OG specialists constituted the largest class (40.6%), followed by generalist tree species (36.7%) and SG specialists (22.7%). The multinomial model was more sensitive than indicator value analysis or abundance-based phi coefficient indices in detecting habitat specialists and also detects generalists statistically. Classification of specialists and generalists based on rarefied subsamples was highly consistent with classification based on the full sample, even for sampling percentages as low as 20%. Major advantages of the new method are (1) its ability to distinguish habitat generalists (species with no significant habitat affinity) from species that are simply too rare to classify and (2) applicability to a single representative sample or a single pooled set of representative samples from each of two habitat types. The method as currently developed can be applied to no more than two habitats at a time.     

Predicting landscape-scale biodiversity recovery by natural tropical forest regrowth

Natural forest regrowth is a cost-effective, nature-based solution for biodiversity recovery, yet different socioenvironmental factors can lead to variable outcomes. A critical knowledge gap in forest restoration planning is how to predict where natural forest regrowth is likely to lead to high levels of biodiversity recovery, which is an indicator of conservation value and the potential provisioning of diverse ecosystem services. We sought to predict and map landscape-scale recovery of species richness and total abundance of vertebrates, invertebrates, and plants in tropical and subtropical second-growth forests to inform spatial restoration planning. First, we conducted a global meta-analysis to quantify the extent to which recovery of species richness and total abundance in second-growth forests deviated from biodiversity values in reference old-growth forests in the same landscape. Second, we employed a machine-learning algorithm and a comprehensive set of socioenvironmental factors to spatially predict landscape-scale deviation and map it. Models explained on average 34% of observed variance in recovery (range 9–51%). Landscape-scale biodiversity recovery in second-growth forests was spatially predicted based on socioenvironmental landscape factors (human demography, land use and cover, anthropogenic and natural disturbance, ecosystem productivity, and topography and soil chemistry); was significantly higher for species richness than for total abundance for vertebrates (median range-adjusted predicted deviation 0.09 vs. 0.34) and invertebrates (0.2 vs. 0.35) but not for plants (which showed a similar recovery for both metrics [0.24 vs. 0.25]); and was positively correlated for total abundance of plant and vertebrate species (Pearson r = 0.45, p = 0.001). Our approach can help identify tropical and subtropical forest landscapes with high potential for biodiversity recovery through natural forest regrowth.