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Abstract Aim The climate variability hypothesis proposes that species subjected to wide variation in climatic conditions will evolve wider niches, resulting in larger distributions. We test this hypothesis in tropical plants across a broad elevational gradient; specifically, we use a species‐level approach to evaluate whether elevational range sizes are explained by the levels of thermal variability experienced by species.
Location Central Andes.
Time Period Present day.
Taxon Woody plants.
Methods Combining data from 479 forest plots, we determined the elevational distributions of nearly 2300 species along an elevational gradient (~209–3800 m). For each species, we calculated the maximum annual variation in temperature experienced across its elevational distribution. We used phylogenetic generalized least square models to evaluate the effect of thermal variability on range size. Our models included additional covariates that might affect range size: body size, local abundance, mean temperature and total precipitation. We also considered interactions between thermal variability and mean temperature or precipitation. To account for geometric constraints, we repeated our analyses with a standardized measure of range size, calculated by comparing observed range sizes with values obtained from a null model.
Results Our results supported the main prediction of the climate variability hypothesis. Thermal variability had a strong positive effect on the range size, with species exposed to higher thermal variability having broader elevational distributions. Body size and local abundance also had positive, yet weak effects, on elevational range size. Furthermore, there was a strong positive interaction between thermal variability and mean annual temperature.
Main Conclusions Thermal variability had an overriding importance in driving elevational range sizes of woody plants in the Central Andes. Moreover, the relationship between thermal variability and range size might be even stronger in warmer regions, underlining the potential vulnerability of tropical montane floras to the effects of global warming.
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This item contains version 5.0 of the Madidi Project's full dataset. The zip file contains (1) raw data, which was downloaded from Tropicos (www.tropicos.org) on August 18, 2020; (2) R scripts used to modify, correct, and clean the raw data; (3) clean data that are the output of the R scripts, and which are the point of departure for most uses of the Madidi Dataset; (4) post-cleaning scripts that obtain additional but non-essential information from the clean data (e.g. by extracting environmental data from rasters); and (5) a miscellaneous collection of additional non-essential information and figures. This item also includes the Data Use Policy for this dataset.
The core dataset of the Madidi Project consists of a network of ~500 forest plots distributed in and around the Madidi National Park in Bolivia. This network contains 50 permanently marked large plots (1-ha), as well as >450 temporary small plots (0.1-ha). Within the large plots, all woody individuals with a dbh ≥10 cm have been mapped, tagged, measured, and identified. Some of these plots have also been re-visited and information on mortality, recruitment, and growth exists. Within the small plots, all woody individuals with a dbh ≥2.5 cm have been measured and identified. Each plot has some edaphic and topographic information, and some large plots have information on various plant functional traits.
The Madidi Project is a collaborative research effort to document and study plant biodiversity in the Amazonia and Tropical Andes of northwestern Bolivia. The project is currently lead by the Missouri Botanical Garden (MBG), in collaboration with the Herbario Nacional de Bolivia. The management of the project is at MBG, where J. Sebastian Tello (sebastian.tello@mobot.org) is the scientific director. The director oversees the activities of a research team based in Bolivia. MBG works in collaboration with other data contributors (currently: Manuel J. Macía [manuel.macia@uam.es], Gabriel Arellano [gabriel.arellano.torres@gmail.com] and Beatriz Nieto [sonneratia@gmail.com]), with a representative from the Herbario Nacional de Bolivia (LPB; Carla Maldonado [carla.maldonado1@gmail.com]), as well as with other close associated researchers from various institutions. For more information regarding the organization and objectives of the Madidi Project, you can visit the project’s website (www.madidiproject.weebly.com).
The Madidi project has been supported by generous grants from the National Science Foundation (DEB 0101775, DEB 0743457, DEB 1836353), and the National Geographic Society (NGS 7754-04 and NGS 8047-06). Additional financial support for the Madidi Project has been provided by the Missouri Botanical Garden, the Comunidad de Madrid (Spain), the Universidad Autónima de Madrid, and the Taylor and Davidson families. -
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Abstract Many ecological applications, like the study of mortality rates, require the estimation of proportions and confidence intervals for them. The traditional way of doing this applies the binomial distribution, which describes the outcome of a series of Bernoulli trials. This distribution assumes that observations are independent and the probability of success is the same for all the individual observations. Both assumptions are obviously false in many cases.
I show how to apply bootstrap and the Poisson binomial distribution (a generalization of the binomial distribution) to the estimation of proportions. Any information at the individual level would result in better (narrower) confidence intervals around the estimation of proportions. As a case study, I applied this method to the calculation of mortality rates in a forest plot of tropical trees in Lambir Hills National Park, Malaysia.
I calculated central estimates and 95% confidence intervals for species‐level mortality rates for 1,007 tree species. I used a very simple model of spatial dependence in survival to estimate individual‐level risk of mortality. The results obtained by accounting for heterogeneity in individual‐level risk of mortality were comparable to those obtained with the binomial distribution in terms of central estimates, but the precision increased in virtually all cases, with an average reduction in the width of the confidence interval of ~20%.
Spatial information allows the estimation of individual‐level probabilities of survival, and this increases the precision in the estimates of mortality rates. The general method described here, with modifications, could be applied to reduce uncertainty in the estimation of proportions related to any spatially structured phenomenon with two possible outcomes. More sophisticated approaches can yield better estimates of individual‐level mortality and thus narrower confidence intervals.
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The arboreal ecosystem is vitally important to global and local biogeochemical processes, the maintenance of biodiversity in natural systems, and human health in urban environments. The ability to collect samples, observations, and data to conduct meaningful scientific research is similarly vital. The primary methods and modes of access remain limited and difficult. In an online survey, canopy researchers ( n = 219) reported a range of challenges in obtaining adequate samples, including ∼10% who found it impossible to procure what they needed. Currently, these samples are collected using a combination of four primary methods: (1) sampling from the ground; (2) tree climbing; (3) constructing fixed infrastructure; and (4) using mobile aerial platforms, primarily rotorcraft drones. An important distinction between instantaneous and continuous sampling was identified, allowing more targeted engineering and development strategies. The combination of methods for sampling the arboreal ecosystem provides a range of possibilities and opportunities, particularly in the context of the rapid development of robotics and other engineering advances. In this study, we aim to identify the strategies that would provide the benefits to a broad range of scientists, arborists, and professional climbers and facilitate basic discovery and applied management. Priorities for advancing these efforts are (1) to expand participation, both geographically and professionally; (2) to define 2–3 common needs across the community; (3) to form and motivate focal teams of biologists, tree professionals, and engineers in the development of solutions to these needs; and (4) to establish multidisciplinary communication platforms to share information about innovations and opportunities for studying arboreal ecosystems.more » « less
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Abstract Aim We examined tree beta diversity in four biogeographical regions with contrasting environmental conditions, latitude, and diversity. We tested: (a) the influence of the species pool on beta diversity; (b) the relative contribution of niche‐based and dispersal‐based assembly to beta diversity; and (c) differences in the importance of these two assembly mechanisms in regions with differing productivity and species richness.
Location Lowland and montane tropical forests in the Madidi region (Bolivia), lowland temperate forests in the Ozarks (USA), and montane temperate forests in the Cantabrian Mountains (Spain).
Methods We surveyed woody plants with a diameter ≥2.5 cm following a standardized protocol in 236 0.1‐ha forest plots in four different biogeographical regions. We estimated the species pool at each region and used it to recreate null communities determined entirely by the species pool. Observed patterns of beta diversity smaller or greater than the null‐expected patterns of beta diversity implies the presence of local assembly mechanisms beyond the influence of the species pool. We used variation‐partitioning analyses to compare the contribution of niche‐based and dispersal‐based assembly to patterns of observed beta diversity and their deviations from null models among the four regions.
Results (a) Differences in species pools alone did not explain observed differences in beta diversity among biogeographic regions. (b) In 3/4 regions, the environment explained more of the variation in beta diversity than spatial variables. (c) Spatial variables explained more of the beta diversity in more diverse and more productive regions with more rare species (tropical and lower‐elevation regions) compared to less diverse and less productive regions (temperate and higher‐elevation regions). (d) Greater alpha or gamma diversity did not result in higher beta diversity or stronger correlations with the environment.
Conclusion Overall, the observed differences in beta diversity are better explained by differences in community assembly mechanism than by biogeographical processes that shaped the species pool.
