Abstract: The rainforests of the Neotropics shelter a vast diversity of plant, animal and microscopic species that provide critical ecosystem goods and services for both local and worldwide populations. These environments face a major crisis due to increased deforestation, pollution, and climate change, emphasizing the need for more effective conservation efforts. The adequate monitoring of these ecosystems has proven a complex and time consuming endeavour, which depends on ever dwindling taxonomic expertise. To date, many species remain undiscovered, let alone described, with otherwise limited information regarding known species population distributions and densities. Overcoming these knowledge shortfalls and practical limitations is becoming increasingly possible through techniques based on environmental DNA (eDNA), i.e., DNA that can be obtained from environmental samples (e.g. tissues, soil, sediment, water, etc.). When coupled with high-throughput sequencing, these techniques now enable realistic, cost-effective, and standardisable biodiversity assessments. This opens up enormous opportunities for advancing our understanding of complex and species-rich tropical communities, but also in facilitating large-scale biomonitoring programs in the neotropics. In this review, we provide a brief introduction to eDNA methods, and an overview of their current and potential uses in both terrestrial and aquatic ecosystems of neotropical rainforests. We also discuss the limits and challenges of these methods for our understanding and monitoring of biodiversity, as well as future research and applied perspectives of these techniques in neotropical rainforests, and beyond. © 2020 Elsevier Ltd

Abstract: There is a high international demand for timber from the genus Dipteryx, or “shihuahuaco” as it is known in Peru. Developing tools that allow the identification and discrimination of Dipteryx species is therefore important for supporting management of natural populations and to underpin legal trade of its timber. The objective of this study was the molecular characterization of Dipteryx species in the Peruvian Amazonia. Two plastid regions (cpDNA: trnH–psbA and matK) were sequenced and 11 microsatellite markers (nDNA) were genotyped for 32 individuals identified as Dipteryx charapilla, D. micrantha morphotype 1 and D. micrantha morphotype 2. Using the concatenated sequences of the plastid genes, we identified ten haplotypes that were not shared between the species or between the D. micrantha morphotypes. Haplotypic diversity was greater in D. micrantha morphotype 2 and D. charapilla than in D. micrantha morphotype 1, which presented only one haplotype with a wide distribution in Peru. The microsatellites allowed the discrimination of the same three clades and identified diagnostic alleles for each clade. These results allowed us to demonstrate that the two morphotypes of D. micrantha are different at both the plastid and nuclear markers, which supports the existence of three genetically distinct species in Peru. This study provides information for the genetic discrimination of Dipteryx species and emphasises the importance of conserving the genetic variability of this genus in the Peruvian Amazonia. © 2019, Springer Nature Switzerland AG.

Abstract: High-throughput sequencing of amplicons from environmental DNA samples permits rapid, standardized and comprehensive biodiversity assessments. However, retrieving and interpreting the structure of such data sets requires efficient methods for dimensionality reduction. Latent Dirichlet Allocation (LDA) can be used to decompose environmental DNA samples into overlapping assemblages of co-occurring taxa. It is a flexible model-based method adapted to uneven sample sizes and to large and sparse data sets. Here, we compare LDA performance on abundance and occurrence data, and we quantify the robustness of the LDA decomposition by measuring its stability with respect to the algorithm's initialization. We then apply LDA to a survey of 1,131 soil DNA samples that were collected in a 12-ha plot of primary tropical forest and amplified using standard primers for bacteria, protists, fungi and metazoans. The analysis reveals that bacteria, protists and fungi exhibit a strong spatial structure, which matches the topographical features of the plot, while metazoans do not, confirming that microbial diversity is primarily controlled by environmental variation at the studied scale. We conclude that LDA is a sensitive, robust and computationally efficient method to detect and interpret the structure of large DNA-based biodiversity data sets. We finally discuss the possible future applications of this approach for the study of biodiversity. © 2019 John Wiley & Sons Ltd

Abstract: There is growing recognition that ecosystems may be more impacted by infrequent extreme climatic events than by changes in mean climatic conditions. This has led to calls for experiments that explore the sensitivity of ecosystems over broad ranges of climatic parameter space. However, because such response surface experiments have so far been limited in geographic and biological scope, it is not clear if differences between studies reflect geographic location or the ecosystem component considered. In this study, we manipulated rainfall entering tank bromeliads in seven sites across the Neotropics, and characterized the response of the aquatic ecosystem in terms of invertebrate functional composition, biological stocks (total invertebrate biomass, bacterial density) and ecosystem fluxes (decomposition, carbon, nitrogen). Of these response types, invertebrate functional composition was the most sensitive, even though, in some sites, the species pool had a high proportion of drought-tolerant families. Total invertebrate biomass was universally insensitive to rainfall change because of statistical averaging of divergent responses between functional groups. The response of invertebrate functional composition to rain differed between geographical locations because (1) the effect of rainfall on bromeliad hydrology differed between sites, and invertebrates directly experience hydrology not rainfall and (2) the taxonomic composition of some functional groups differed between sites, and families differed in their response to bromeliad hydrology. These findings suggest that it will be difficult to establish thresholds of “safe ecosystem functioning” when ecosystem components differ in their sensitivity to climatic variables, and such thresholds may not be broadly applicable over geographic space. In particular, ecological forecast horizons for climate change may be spatially restricted in systems where habitat properties mediate climatic impacts, and those, like the tropics, with high spatial turnover in species composition. © 2020 by the Ecological Society of America

Abstract: For more than a decade, distance-based methods have been widely employed and constantly improved in spatial economics. These methods are a very useful tool for accurately evaluating the spatial distribution of economic activity. We introduce a new distance-based statistical measure for evaluating the spatial concentration of industries. The m function is the first relative density function to be proposed in economics. This tool supplements the typology of distance-based methods recently drawn up by Marcon and Puech (J Econ Geogr 3(4):409–428, 2003). By considering several simulated and real examples, we show the advantages and the limits of the m function for detecting spatial structures in economics. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.