Natural reforestation after regional forest clearance is a globally common land-use sequence. The genetic recovery of tree populations in these recolonized forests may depend on the biogeographic setting of the landscape, for instance whether they are in the core or in the marginal part of the species' range. Using data from 501 individuals genotyped across 7 microsatellites, we investigated whether regional differences in habitat quality affected the recovery of genetic variation in a wind-pollinated tree species, American beech (Fagus grandifolia) in Massachusetts. We compared populations in forests that were recolonized following agricultural abandonment to those in remnant forests that have only been logged in both central inland and marginal coastal regions. Across all populations in our entire study region, recolonized forests showed limited reduction of genetic diversity as only observed heterozygosity was significantly reduced in these forests (H O = 0.520 and 0.590, respectively). Within inland region, this pattern was observed, whereas in the coast, recolonized populations exhibited no reduction in all genetic diversity estimates. However, genetic differentiation among recolonized populations in marginal coastal habitat increased (F st logged = 0.072; F st secondary = 0.249), with populations showing strong genetic structure, in contrast to inland region. These results indicate that the magnitude of recovery of genetic variation in recolonized populations can vary at different habitats.
Quaternary plant ecology in much of the world has historically relied on morphological identification of macro- and microfossils from sediments of small freshwater lakes. Here, we report new protocols that reliably yield DNA sequence data from Holocene plant macrofossils and bulk lake sediment used to infer ecological change. This will allow changes in census populations, estimated from fossils and associated sediment, to be directly associated with population genetic changes.
The spatial distribution of genetic diversity is a product of recent and historical ecological processes, as well as anthropogenic activities. A current challenge in population and conservation genetics is to disentangle the relative effects of these processes, as a first step in predicting population response to future environmental change. In this investigation, we compare the influence of contemporary population decline, contemporary ecological marginality and postglacial range shifts. Using classical model comparison procedures and Bayesian methods, we have identified postglacial range shift as the clear determinant of genetic diversity, differentiation and bottlenecks in 29 populations of butternut, Juglans cinerea L., a North American outcrossing forest tree. Although butternut has experienced dramatic 20th century decline because of an introduced fungal pathogen, our analysis indicates that recent population decline has had less genetic impact than postglacial recolonization history. Location within the range edge vs. the range core also failed to account for the observed patterns of diversity and differentiation. Our results suggest that the genetic impact of large-scale recent population losses in forest trees should be considered in the light of Pleistocene-era large-scale range shifts that may have had long-term genetic consequences. The data also suggest that the population dynamics and life history of wind-pollinated forest trees may provide a buffer against steep population declines of short duration, a result having important implications for habitat management efforts, ex situ conservation sampling and population viability analysis.
Ecologists use the relative abundance of fossil pollen in sediments to estimate how tree species abundances change over space and time. To predict historical forest composition and quantify the available information, we build a Bayesian hierarchical model of forest composition in central New England, USA, based on pollen in a network of ponds. The critical relationships between abundances of taxa in the pollen record and abundances as actual vegetation are estimated for the modern and colonial periods, for which both pollen and direct vegetation data are available, based on a latent multivariate spatial process representing forest composition. For time periods in the past with only pollen data, we use the estimated model parameters to constrain predictions about the latent spatio-temporal process conditional on the pollen data. We develop an innovative graphical assessment of feature significance to help to infer which spatial patterns are reliably estimated. The model allows us to estimate the spatial distribution and relative abundances of tree species over the last 2500 years, with an assessment of uncertainty, and to draw inference about how these patterns have changed over time. Cross-validation suggests that our feature significance approach can reliably indicate certain large-scale spatial features for many taxa, but that features on scales smaller than 50 km are difficult to distinguish, as are large-scale features for some taxa. We also use the model to quantitatively investigate ecological hypotheses, including covariate effects on taxa abundances and questions about pollen dispersal characteristics. The critical advantages of our modeling approach over current ecological analyses are the explicit spatio-temporal representation, quantification of abundance on the scale of trees rather than pollen, and uncertainty characterization.
Managed relocation (MR) has rapidly emerged as a potential intervention strategy in the toolbox of biodiversity management under climate change. Previous authors have suggested that MR (also referred to as assisted colonization, assisted migration, or assisted translocation) could be a last-alternative option after interrogating a linear decision tree. We argue that numerous interacting and value-laden considerations demand a more inclusive strategy for evaluating MR. The pace of modern climate change demands decision making with imperfect information, and tools that elucidate this uncertainty and integrate scientific information and social values are urgently needed. We present a heuristic tool that incorporates both ecological and social criteria in a multidimensional decision-making framework. For visualization purposes, we collapse these criteria into 4 classes that can be depicted in graphical 2-D space. This framework offers a pragmatic approach for summarizing key dimensions of MR: capturing uncertainty in the evaluation criteria, creating transparency in the evaluation process, and recognizing the inherent tradeoffs that different stakeholders bring to evaluation of MR and its alternatives.
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