ORCID Profile
0000-0002-3618-1144
Current Organisation
University of Miami
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Publisher: American Association for the Advancement of Science (AAAS)
Date: 18-10-2013
Abstract: Recent decades have seen a major international effort to inventory tree communities in the Amazon Basin and Guiana Shield (Amazonia), but the vast extent and record ersity of these forests have h ered an understanding of basinwide patterns. To overcome this obstacle, we compiled and standardized species-level data on more than half a million trees in 1170 plots s ling all major lowland forest types to explore patterns of commonness, rarity, and richness. The ~6-million-km 2 Amazonian lowlands were ided into 1° cells, and mean tree density was estimated for each cell by using a loess regression model that included no environmental data but had its basis exclusively in the geographic location of tree plots. A similar model, allied with a bootstrapping exercise to quantify s ling error, was used to generate estimated Amazon-wide abundances of the 4962 valid species in the data set. We estimated the total number of tree species in the Amazon by fitting the mean rank-abundance data to Fisher’s log-series distribution. Our analyses suggest that lowland Amazonia harbors 3.9 × 10 11 trees and ~16,000 tree species. We found 227 “hyperdominant” species (1.4% of the total) to be so common that together they account for half of all trees in Amazonia, whereas the rarest 11,000 species account for just 0.12% of trees. Most hyperdominants are habitat specialists that have large geographic ranges but are only dominant in one or two regions of the basin, and a median of 41% of trees in in idual plots belong to hyperdominants. A disproportionate number of hyperdominants are palms, Myristicaceae, and Lecythidaceae. The finding that Amazonia is dominated by just 227 tree species implies that most biogeochemical cycling in the world’s largest tropical forest is performed by a tiny sliver of its ersity. The causes underlying hyperdominance in these species remain unknown. Both competitive superiority and widespread pre-1492 cultivation by humans are compelling hypotheses that deserve testing. Although the data suggest that spatial models can effectively forecast tree community composition and structure of unstudied sites in Amazonia, incorporating environmental data may yield substantial improvements. An appreciation of how thoroughly common species dominate the basin has the potential to simplify research in Amazonian biogeochemistry, ecology, and vegetation mapping. Such advances are urgently needed in light of the ,000 rare, poorly known, and potentially threatened tree species in the Amazon.
Publisher: Springer Science and Business Media LLC
Date: 17-01-2018
DOI: 10.1038/S41598-017-18927-1
Abstract: Species distribution models (SDMs) are widely used in ecology and conservation. Presence-only SDMs such as MaxEnt frequently use natural history collections (NHCs) as occurrence data, given their huge numbers and accessibility. NHCs are often spatially biased which may generate inaccuracies in SDMs. Here, we test how the distribution of NHCs and MaxEnt predictions relates to a spatial abundance model, based on a large plot dataset for Amazonian tree species, using inverse distance weighting (IDW). We also propose a new pipeline to deal with inconsistencies in NHCs and to limit the area of occupancy of the species. We found a significant but weak positive relationship between the distribution of NHCs and IDW for 66% of the species. The relationship between SDMs and IDW was also significant but weakly positive for 95% of the species, and sensitivity for both analyses was high. Furthermore, the pipeline removed half of the NHCs records. Presence-only SDM applications should consider this limitation, especially for large bio ersity assessments projects, when they are automatically generated without subsequent checking. Our pipeline provides a conservative estimate of a species’ area of occupancy, within an area slightly larger than its extent of occurrence, compatible to e.g. IUCN red list assessments.
Publisher: Wiley
Date: 06-10-2020
DOI: 10.1111/BTP.12843
Publisher: Proceedings of the National Academy of Sciences
Date: 05-02-2018
Abstract: Identifying and explaining regional differences in tropical forest dynamics, structure, ersity, and composition are critical for anticipating region-specific responses to global environmental change. Floristic classifications are of fundamental importance for these efforts. Here we provide a global tropical forest classification that is explicitly based on community evolutionary similarity, resulting in identification of five major tropical forest regions and their relationships: ( i ) Indo-Pacific, ( ii ) Subtropical, ( iii ) African, ( iv ) American, and ( v ) Dry forests. African and American forests are grouped, reflecting their former western Gondwanan connection, while Indo-Pacific forests range from eastern Africa and Madagascar to Australia and the Pacific. The connection between northern-hemisphere Asian and American forests is confirmed, while Dry forests are identified as a single tropical biome.
Publisher: Wiley
Date: 04-11-2021
Abstract: Mountains are cradles for bio ersity and crucibles for climate‐driven species loss, particularly for tropical ectotherms. Constriction on activity and lified heat stress are two key sources of warming‐driven vulnerability in tropical montane ectotherms. These threats, however, might be counterbalanced if rising temperatures also release organisms from limits on activity induced by cold stress. We used biophysical modelling to estimate activity patterns and thermal stress under warming in a group of summit‐dwelling Anolis lizards ( A . armouri and A . shrevei ) endemic to the Caribbean island of Hispaniola. Activity is currently constrained by the prevalence of temperatures too cold for activity. Under warming, our models predict expanded hours of potential activity and reduced cold stress, especially under a high emission scenario. Crucially, release from cold stress does not come at the expense of heightened exposure to heat stress. This result arises from a steep mismatch between these species’ warm‐adapted ecology and the surprisingly cold environments they occupy. Yet, resilience in some dimensions belies vulnerability along others, particularly with regard to critical macrohabitat. We capitalized on a long‐term monitoring dataset to predict forest distributions under warming. Our models predict upslope shifts in montane cloud forests that may constrict the high‐elevation pine forests to which these lizards are inexorably linked. Warming‐driven macrohabitat loss can ‘pin’ the montane endemics into progressively shrinking ranges, especially since a rising cloud forest also facilitates upslope transport of a close relative, A . cybotes (a species associated with broadleaf forests). Many tropical ectotherms (including these anoles) are adapted to forest edges, a feature often associated with a relatively warm‐adapted ecophysiology. When such species are also found in cool environments, such as those found on mountaintops, warming‐ lified thermal stress is surprisingly limited. Therefore, the direct effects of warming on tropical ectotherms are quite broad, and can even include potential benefits to fitness‐based activities. Rising temperatures may often present a dual‐edged sword: warming simultaneously releases these organisms from constraints on activity while exposing them to other threats. Whether due to the direct or indirect effects of climate warming, exceptional vulnerability may indeed reside where bio ersity is highest.
Publisher: Cold Spring Harbor Laboratory
Date: 04-2021
DOI: 10.1101/2021.03.31.437717
Abstract: In a time of rapid global change, the question of what determines patterns in species abundance distribution remains a priority for understanding the complex dynamics of ecosystems. The constrained maximization of information entropy provides a framework for the understanding of such complex systems dynamics by a quantitative analysis of important constraints via predictions using least biased probability distributions. We apply it to over two thousand hectares of Amazonian tree inventories across seven forest types and thirteen functional traits, representing major global axes of plant strategies. Results show that constraints formed by regional relative abundances of genera explain eight times more of local relative abundances than constraints based on directional selection for specific functional traits, although the latter does show clear signals of environmental dependency. These results provide a quantitative insight by inference from large-scale data using cross-disciplinary methods, furthering our understanding of ecological dynamics.
Publisher: Springer Science and Business Media LLC
Date: 23-06-2020
DOI: 10.1038/S41598-020-66686-3
Abstract: Amazonian forests are extraordinarily erse, but the estimated species richness is very much debated. Here, we apply an ensemble of parametric estimators and a novel technique that includes conspecific spatial aggregation to an extended database of forest plots with up-to-date taxonomy. We show that the species abundance distribution of Amazonia is best approximated by a logseries with aggregated in iduals, where aggregation increases with rarity. By averaging several methods to estimate total richness, we confirm that over 15,000 tree species are expected to occur in Amazonia. We also show that using ten times the number of plots would result in an increase to just ~50% of those 15,000 estimated species. To get a more complete s le of all tree species, rigorous field c aigns may be needed but the number of trees in Amazonia will remain an estimate for years to come.
Publisher: Wiley
Date: 2019
DOI: 10.1111/DDI.12885
No related grants have been discovered for Kenneth Feeley.