Roosevelt García-Villacorta

A floristic study of the white-sand forests of Peru.

Abstract Tropical forests occurring on white-sand soils have a unique structure and are famous for their endemism. Yet, no comprehensive floristic study has ever been undertaken in white-sand forests in the western Amazon. Here, we present the results of floristic inventories from 16 plots in seven sites from the Peruvian Amazon to investigate diversity, species composition, and endemism in white-sand forests. We compare our results to a large data set from terra firme forests from more fertile soils in the same region. We found that white-sand forest plots have extremely low average species diversity (41.5 species per 0.1-ha plot) and that white-sand plots have significantly different species composition from terra firme plots. We classify 114 species as endemic to white sand, with another 21 species that can be considered facultative specialists or cryptic endemics. These endemics and specialists are extremely dominant, accounting for more than 83% of the total number of stems surveyed in white-sand forest plots. We place our results in the context of the role of environmental heterogeneity influencing patterns of species diversity and the conservation of Amazonian forests.

Seasonal drought limits tree species across the Neotropics

Within the tropics, the species richness of tree communities is strongly and positively associated with precipitation. Previous research has suggested that this macroecological pattern is driven by the negative effect of water-stress on the physiological processes of most tree species. This process implies that the range limits of taxa are defined by their ability to occur under dry conditions, and thus in terms of species distributions it predicts a nested pattern of taxa distribution from wet to dry areas. However, this ‘dry-tolerance’ hypothesis has yet to be adequately tested at large spatial and taxonomic scales. Here, using a dataset of 531 inventory plots of closed canopy forest distributed across the Western Neotropics we investigated how precipitation, evaluated both as mean annual precipitation and as the maximum climatological water deficit, influences the distribution of tropical tree species, genera and families. We find that the distributions of tree taxa are indeed nested along precipitation gradients in the western Neotropics. Taxa tolerant to seasonal drought are disproportionally widespread across the precipitation gradient, with most reaching even the wettest climates sampled; however, most taxa analysed are restricted to wet areas. Our results suggest that the ‘dry tolerance’ hypothesis has broad applicability in the worlds most species-rich forests. In addition, the large number of species restricted to wetter conditions strongly indicates that an increased frequency of drought could severely threaten biodiversity in this region. Overall, this study establishes a baseline for exploring how tropical forest tree composition may change in response to current and future environmental changes in this region.

Are all species necessary to reveal ecologically important patterns

While studying ecological patterns at large scales, ecologists are often unable to identify all collections, forcing them to either omit these unidentified records entirely, without knowing the effect of this, or pursue very costly and time-consuming efforts for identifying them. These “indets” may be of critical importance, but as yet, their impact on the reliability of ecological analyses is poorly known. We investigated the consequence of omitting the unidentified records and provide an explanation for the results. We used three large-scale independent datasets, (Guyana/ Suriname, French Guiana, Ecuador) each consisting of records having been identified to a valid species name (identified morpho-species – IMS) and a number of unidentified records (unidentified morpho-species – UMS). A subset was created for each dataset containing only the IMS, which was compared with the complete dataset containing all morpho-species (AMS: = IMS + UMS) for the following analyses: species diversity (Fishers alpha), similarity of species composition, Mantel test and ordination (NMDS). In addition, we also simulated an even larger number of unidentified records for all three datasets and analyzed the agreement between similarities again with these simulated datasets. For all analyses, results were extremely similar when using the complete datasets or the truncated subsets. IMS predicted ≥91% of the variation in AMS in all tests/analyses. Even when simulating a larger fraction of UMS, IMS predicted the results for AMS rather well. Using only IMS also out-performed using higher taxon data (genus-level identification) for similarity analyses. Finding a high congruence for all analyses when using IMS rather than AMS suggests that patterns of similarity and composition are very robust. In other words, having a large number of unidentified species in a dataset may not affect our conclusions as much as is often thought.

Spatial Scales of Genetic Structure in Free-Standing and Strangler Figs (Ficus, Moraceae) Inhabiting Neotropical Forests

Wind-borne pollinating wasps (Agaonidae) can transport fig (Ficus sp., Moraceae) pollen over enormous distances (> 100 km). Because of their extensive breeding areas, Neotropical figs are expected to exhibit weak patterns of genetic structure at local and regional scales. We evaluated genetic structure at the regional to continental scale (Panama, Costa Rica, and Peru) for the free-standing fig species Ficus insipida. Genetic differentiation was detected only at distances > 300 km (Jost´s Dest = 0.68 ± 0.07 & FST = 0.30 ± 0.03 between Mesoamerican and Amazonian sites) and evidence for phylogeographic structure (R ST>>permuted R ST) was only significant in comparisons between Central and South America. Further, we assessed local scale spatial genetic structure (SGS, d ≤ 8 km) in Panama and developed an agent-based model parameterized with data from F. insipida to estimate minimum pollination distances, which determine the contribution of pollen dispersal on SGS. The local scale data for F. insipida was compared to SGS data collected for an additional free-standing fig, F. yoponensis (subgenus Pharmacosycea), and two species of strangler figs, F. citrifolia and F. obtusifolia (subgenus Urostigma) sampled in Panama. All four species displayed significant SGS (mean Sp = 0.014 ± 0.012). Model simulations indicated that most pollination events likely occur at distances > > 1 km, largely ruling out spatially limited pollen dispersal as the determinant of SGS in F. insipida and, by extension, the other fig species. Our results are consistent with the view that Ficus develops fine-scale SGS primarily as a result of localized seed dispersal and/or clumped seedling establishment despite extensive long-distance pollen dispersal. We discuss several ecological and life history factors that could have species- or subgenus-specific impacts on the genetic structure of Neotropical figs.

Peatland forests are the least diverse tree communities documented in Amazonia, but contribute to high regional beta‐diversity

Western Amazonia is known to harbour some of Earths most diverse forests, but previous floristic analyses have excluded peatland forests which are extensive in northern Peru and are among the most environmentally extreme ecosystems in the lowland tropics. Understanding patterns of tree species diversity in these ecosystems is important both for quantifying beta‐diversity in this region, and for understanding determinants of diversity more generally in tropical forests. Here we explore patterns of tree diversity and composition in two peatland forest types – palm swamps and peatland pole forests – using 26 forest plots distributed over a large area of northern Peru. We place our results in a regional context by making comparisons with three other major forest types: terra firme forests (29 plots), white‐sand forests (23 plots) and seasonally‐flooded forests (11 plots). Peatland forests had extremely low (within‐plot) alpha‐diversity compared with the other forest types that were sampled. In particular, peatland pole forests had the lowest levels of tree diversity yet recorded in Amazonia (20 species per 500 stems, Fishers alpha 4.57). However, peatland pole forests and palm swamps were compositionally different from each other as well as from other forest types in the region. Few species appeared to be peatland endemics. Instead, peatland forests were largely characterised by a distinctive combination of generalist species and species previously thought to be specialists of other habitats, especially white‐sand forests. We suggest that the transient nature and extreme environmental conditions of Amazonian peatland ecosystems have shaped their current patterns of tree composition and diversity. Despite their low alpha‐diversity, the unique combination of species found in tree communities in Amazonian peatlands augment regional beta‐diversity. This contribution, alongside their extremely high carbon storage capacity and lack of protection at national level, strengthens their status as a conservation priority.

Boosting Efficiency for Computing the Pareto Frontier on Tree Structured Networks

Multi-objective optimization plays a key role in the study of real-world problems, as they often involve multiple criteria. In multi-objective optimization it is important to identify the so-called Pareto frontier, which characterizes the trade-offs between the objectives of different solutions. We show how a divide-and-conquer approach, combined with batched processing and pruning, significantly boosts the performance of an exact and approximation dynamic programming (DP) algorithm for computing the Pareto frontier on tree-structured networks, proposed in [18]. We also show how exploiting restarts and a new instance selection strategy boosts the performance and accuracy of a mixed integer programming (MIP) approach for approximating the Pareto frontier. We provide empirical results demonstrating that our DP and MIP approaches have complementary strengths and outperform previous algorithms in efficiency and accuracy. Our work is motivated by a problem in computational sustainability concerning the evaluation of trade-offs in ecosystem services due to the proliferation of hydropower dams throughout the Amazon basin. Our approaches are general and can be applied to computing the Pareto frontier of a variety of multi-objective problems on tree-structured networks.

Efficiently Optimizing for Dendritic Connectivity on Tree-Structured Networks in a Multi-Objective Framework

We provide an exact and approximation algorithm based on Dynamic Programming and an approximation algorithm based on Mixed Integer Programming for optimizing for the so-called dendritic connectivity on tree-structured networks in a multi-objective setting. Dendritic connectivity describes the degree of connectedness of a network. We consider different variants of dendritic connectivity to capture both network connectivity with respect to long and short-to-middle distances. Our work is motivated by a problem in computational sustainability concerning the evaluation of trade-offs in ecosystem services due to the proliferation of hydropower dams throughout the Amazon basin. In particular, we consider trade-offs between energy production and river connectivity. River fragmentation can dramatically affect fish migrations and other ecosystem services, such as navigation and transportation. In the context of river networks, different variants of dendritic connectivity are important to characterize the movements of different fish species and human populations. Our approaches are general and can be applied to optimizing for dendritic connectivity for a variety of multi-objective problems on tree-structured networks.

Estimating and interpreting migration of Amazonian forests using spatially implicit and semi-explicit neutral models

Abstract With many sophisticated methods available for estimating migration, ecologists face the difficult decision of choosing for their specific line of work. Here we test and compare several methods, performing sanity and robustness tests, applying to large‐scale data and discussing the results and interpretation. Five methods were selected to compare for their ability to estimate migration from spatially implicit and semi‐explicit simulations based on three large‐scale field datasets from South America (Guyana, Suriname, French Guiana and Ecuador). Space was incorporated semi‐explicitly by a discrete probability mass function for local recruitment, migration from adjacent plots or from a metacommunity. Most methods were able to accurately estimate migration from spatially implicit simulations. For spatially semi‐explicit simulations, estimation was shown to be the additive effect of migration from adjacent plots and the metacommunity. It was only accurate when migration from the metacommunity outweighed that of adjacent plots, discrimination, however, proved to be impossible. We show that migration should be considered more an approximation of the resemblance between communities and the summed regional species pool. Application of migration estimates to simulate field datasets did show reasonably good fits and indicated consistent differences between sets in comparison with earlier studies. We conclude that estimates of migration using these methods are more an approximation of the homogenization among local communities over time rather than a direct measurement of migration and hence have a direct relationship with beta diversity. As betadiversity is the result of many (non)‐neutral processes, we have to admit that migration as estimated in a spatial explicit world encompasses not only direct migration but is an ecological aggregate of these processes. The parameter m of neutral models then appears more as an emerging property revealed by neutral theory instead of being an effective mechanistic parameter and spatially implicit models should be rejected as an approximation of forest dynamics.