RESUMO
Arbuscular mycorrhizal (AM) fungi in the soil may influence tropical tree dynamics and forest succession. The mechanisms are poorly understood, because the functional characteristics and abundances of tree species and AM fungi are likely to be codependent. We used generalized joint attribute modeling to evaluate if AM fungi are associated with three forest community metrics for a sub-tropical montane forest in Puerto Rico. The metrics chosen to reflect changes during forest succession are the abundance of seedlings of different successional status, the amount of foliar damage on seedlings of different successional status, and community-weighted mean functional trait values (adult specific leaf area [SLA], adult wood density, and seed mass). We used high-throughput DNA sequencing to identify fungal operational taxonomic units (OTUs) in the soil. Model predictions showed that seedlings of mid- and late-successional species had less leaf damage when the 12 most common AM fungi were abundant compared to when these fungi were absent. We also found that seedlings of mid-successional species were predicted to be more abundant when the 12 most common AM fungi were abundant compared to when these fungi were absent. In contrast, early-successional tree seedlings were predicted to be less abundant when the 12 most common AM fungi were abundant compared to when these fungi were absent. Finally, we showed that, among the 12 most common AM fungi, different AM fungi were correlated with functional trait characteristics of early- or late-successional species. Together, these results suggest that early-successional species might not rely as much as mid- and late-successional species on AM fungi, and AM fungi might accelerate forest succession.
Assuntos
Micorrizas/genética , Fungos , Porto Rico , Plântula/microbiologia , Microbiologia do Solo , Árvores/microbiologiaRESUMO
Tree growth response across environmental gradients is fundamental to understanding species distributional ecology and forest ecosystem ecology and to predict future ecosystem services. Cross-sectional patterns of ecosystem properties with respect to climatic gradients are often used to predict ecosystem responses to global change. Across sites in the tropics, primary productivity increases with temperature, suggesting that forest ecosystems will become more productive as temperature rises. However, this trend is confounded with a shift in species composition and so may not reflect the response of in situ forests to warming. In this study, we simultaneously studied tree diameter growth across the altitudinal ranges of species within a single genus across a geographically compact temperature gradient, to separate the direct effect of temperature on tree growth from that of species compositional turnover. Using a Bayesian state space modeling framework we combined data from repeated diameter censuses and dendrometer measurements from across a 1700-m altitudinal gradient collected over six years on over 2400 trees in Weinmannia, a dominant and widespread genus of cloud forest trees in the Andes. Within species, growth showed no consistent trend with altitude, but higher-elevation species had lower growth rates than lower-elevation species, suggesting that species turnover is largely responsible for the positive correlation between productivity and temperature in tropical forests. Our results may indicate a significant difference in how low- and high-latitude forests will respond to climate change, since temperate and boreal tree species are consistently observed to have a positive relationship between growth and temperature. If our results hold for other tropical species, a positive response in ecosystem productivity to increasing temperatures in the Andes will depend on the altitudinal migration of tree species. The rapid pace of climate change, and slow observed rates of migration, suggest a slow, or even initially negative response of ecosystem productivity to warming. Finally, this study shows how the observed scale of biological organization can affect conclusions drawn from studies of ecological phenomena across environmental gradients, and calls into question the common practice in tropical ecology of lumping species at higher taxonomic levels.
Assuntos
Ecossistema , Árvores/crescimento & desenvolvimento , Altitude , Clima , Demografia , Peru , Especificidade da EspécieRESUMO
BACKGROUND: A common challenge to the study of several infectious diseases consists in combining limited cross-sectional survey data, collected with a more sensitive detection method, with a more extensive (but biased) syndromic sentinel surveillance data, collected with a less sensitive method. Our article describes a novel modeling framework that overcomes this challenge, resulting in enhanced understanding of malaria in the Western Brazilian Amazon. METHODOLOGY/PRINCIPAL FINDINGS: A cohort of 486 individuals was monitored using four cross-sectional surveys, where all participants were sampled regardless of symptoms (aggressive-active case detection), resulting in 1,383 microscopy and 1,400 polymerase chain reaction tests. Data on the same individuals were also obtained from the local surveillance facility (i.e., passive and active case detection), totaling 1,694 microscopy tests. Our model accommodates these multiple pathogen and case detection methods. This model is shown to outperform logistic regression in terms of interpretability of its parameters, ability to recover the true parameter values, and predictive performance. We reveal that the main infection determinant was the extent of forest, particularly during the rainy season and in close proximity to water bodies, and participation on forest activities. We find that time residing in Acrelandia (as a proxy for past malaria exposure) decreases infection risk but surprisingly increases the likelihood of reporting symptoms once infected, possibly because non-naïve settlers are only susceptible to more virulent Plasmodium strains. We suggest that the search for asymptomatic carriers should focus on those at greater risk of being infected but lower risk of reporting symptoms once infected. CONCLUSIONS/SIGNIFICANCE: The modeling framework presented here combines cross-sectional survey data and syndromic sentinel surveillance data to shed light on several aspects of malaria that are critical for public health policy. This framework can be adapted to enhance inference on infectious diseases whenever asymptomatic carriers are important and multiple datasets are available.
Assuntos
Doenças Transmissíveis/epidemiologia , Inquéritos Epidemiológicos/métodos , Malária/epidemiologia , Brasil/epidemiologia , Estudos de Coortes , Bases de Dados Factuais , Humanos , Malária/etiologia , Microscopia , Saúde Pública , Fatores de RiscoRESUMO
1. Our understanding of the interplay between density dependence, climatic perturbations, and conservation practices on the dynamics of small populations is still limited. This can result in uninformed strategies that put endangered populations at risk. Moreover, the data available for a large number of populations in such circumstances are sparse and mined with missing data. Under the current climate change scenarios, it is essential to develop appropriate inferential methods that can make use of such data sets. 2. We studied a population of desert bighorn sheep introduced to Tiburon Island, Mexico in 1975 and subjected to irregular extractions for the last 10 years. The unique attributes of this population are absence of predation and disease, thereby permitting us to explore the combined effect of density dependence, environmental variability and extraction in a 'controlled setting.' Using a combination of nonlinear discrete models with long-term field data, we constructed three basic Bayesian state space models with increasing density dependence (DD), and the same three models with the addition of summer drought effects. 3. We subsequently used Monte Carlo simulations to evaluate the combined effect of drought, DD, and increasing extractions on the probability of population survival under two climate change scenarios (based on the Intergovernmental Panel on Climate Change predictions): (i) increase in drought variability; and (ii) increase in mean drought severity. 4. The population grew from 16 individuals introduced in 1975 to close to 700 by 1993. Our results show that the population's growth was dominated by DD, with drought having a secondary but still relevant effect on its dynamics. 5. Our predictions suggest that under climate change scenario (i), extraction dominates the fate of the population, while for scenario (ii), an increase in mean drought affects the population's probability of survival in an equivalent magnitude as extractions. Thus, for the long-term survival of the population, our results stress that a more variable environment is less threatening than one in which the mean conditions become harsher. Current climate change scenarios and their underlying uncertainty make studies such as this one crucial for understanding the dynamics of ungulate populations and their conservation.