The disproportionate importance of long-unburned forests and woodlands for reptiles.
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ABSTRACT:
SUBMITTER: Dixon KM
PROVIDER: S-EPMC6262929 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Fire is frequently used as a land management tool for cattle ranching and annual crops in the Amazon. However, these maintenance fires often escape into surrounding forests, with potentially severe impacts for forest biodiversity. We examined the effect of experimental fires on leaf-litter arthropod abundance in a seasonally-dry forest in the Brazilian Amazon. The study plots (50 ha each) included a thrice-burned forest and an unburned control forest. Pitfall-trap samples were collected at 160 randomly selected points in both plots, with sampling stratified across four intra-annual replicates across the dry and wet seasons, corresponding to 6, 8, 10 and 12 months after the most recent fire. Arthropods were identified to the level of order (separating Formicidae). In order to better understand the processes that determine arthropod abundance in thrice-burned forests, we measured canopy openness, understory density and litter depth. All arthropod taxa were significantly affected by fire and season. In addition, the interactions between burn treatment and season were highly significant for all taxa but Isoptera. The burned plot was characterized by a more open canopy, lower understory density and shallower litter depth. Hierarchical partitioning revealed that canopy openness was the most important factor explaining arthropod order abundances in the thrice-burned plot, whereas all three environmental variables were significant in the unburned control plot. These results reveal the marked impact of recurrent wildfires and seasonality on litter arthropods in this transitional forest, and demonstrate the overwhelming importance of canopy-openness in driving post-fire arthropod abundance.
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Project description:Background:Random Forests is a popular classification and regression method that has proven powerful for various prediction problems in biological studies. However, its performance often deteriorates when the number of features increases. To address this limitation, feature elimination Random Forests was proposed that only uses features with the largest variable importance scores. Yet the performance of this method is not satisfying, possibly due to its rigid feature selection, and increased correlations between trees of forest. Methods:We propose variable importance-weighted Random Forests, which instead of sampling features with equal probability at each node to build up trees, samples features according to their variable importance scores, and then select the best split from the randomly selected features. Results:We evaluate the performance of our method through comprehensive simulation and real data analyses, for both regression and classification. Compared to the standard Random Forests and the feature elimination Random Forests methods, our proposed method has improved performance in most cases. Conclusions:By incorporating the variable importance scores into the random feature selection step, our method can better utilize more informative features without completely ignoring less informative ones, hence has improved prediction accuracy in the presence of weak signals and large noises. We have implemented an R package "viRandomForests" based on the original R package "randomForest" and it can be freely downloaded from http://zhaocenter.org/software.
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Project description:Large mammalian grazers can alter the biotic and abiotic features of their environment through their impacts on vegetation. Grazing at moderate intensity has been recommended for biodiversity conservation. Few studies, however, have empirically tested the benefits of moderate grazing intensity in systems dominated by native grazers. Here we investigated the relationship between (1) density of native eastern grey kangaroos, Macropus giganteus, and grass structure, and (2) grass structure and reptiles (i.e. abundance, richness, diversity and occurrence) across 18 grassland and grassy Eucalyptus woodland properties in south-eastern Australia. There was a strong negative relationship between kangaroo density and grass structure after controlling for tree canopy cover. We therefore used grass structure as a surrogate for grazing intensity. Changes in grazing intensity (i.e. grass structure) significantly affected reptile abundance, reptile species richness, reptile species diversity, and the occurrence of several ground-dwelling reptiles. Reptile abundance, species richness and diversity were highest where grazing intensity was low. Importantly, no species of reptile was more likely to occur at high grazing intensities. Legless lizards (Delma impar, D. inornata) were more likely to be detected in areas subject to moderate grazing intensity, whereas one species (Hemiergis talbingoensis) was less likely to be detected in areas subject to intense grazing and three species (Menetia greyii, Morethia boulengeri, and Lampropholis delicata) did not appear to be affected by grazing intensity. Our data indicate that to maximize reptile abundance, species richness, species diversity, and occurrence of several individual species of reptile, managers will need to subject different areas of the landscape to moderate and low grazing intensities and limit the occurrence and extent of high grazing.
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Project description:The persistence of wildlife species in fire-prone ecosystems is under increasing pressure from global change, including alterations in fire regimes caused by climate change. However, unburned islands might act to mitigate negative effects of fire on wildlife populations by providing habitat in which species can survive and recolonize burned areas. Nevertheless, the characteristics of unburned islands and their role as potential refugia for the postfire population dynamics of wildlife species remain poorly understood.We used a newly developed unburned island database of the northwestern United States from 1984 to 2014 to assess the postfire response of the greater sage-grouse (Centrocercus urophasianus), a large gallinaceous bird inhabiting the sagebrush ecosystems of North America, in which wildfires are common. Specifically, we tested whether prefire and postfire male attendance trends at mating locations (leks) differed between burned and unburned areas, and to what extent postfire habitat composition at multiple scales could explain such trends.Using time-series of male counts at leks together with spatially explicit fire history information, we modeled whether male attendance was negatively affected by fire events. Results revealed that burned leks often exhibit sustained decline in male attendance, whereas leks within unburned islands or >1.5 km away from fire perimeters tend to show stable or increasing trends.Analyses of postfire habitat composition further revealed that sagebrush vegetation height within 0.8 km around leks, as well elevation within 0.8 km, 6.4 km, and 18 km around leks, had a positive effect on male attendance trends. Moreover, the proportion of the landscape with cheatgrass (Bromus tectorum) cover >8% had negative effects on male attendance trends within 0.8 km, 6.4 km, and 18 km of leks, respectively. Synthesis and applications. Our results indicate that maintaining areas of unburned vegetation within and outside fire perimeters may be crucial for sustaining sage-grouse populations following wildfire. The role of unburned islands as fire refugia requires more attention in wildlife management and conservation planning because their creation, protection, and maintenance may positively affect wildlife population dynamics in fire-prone ecosystems.
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Project description:Restoring fire regimes is a major goal of biodiversity conservation efforts in fire-prone ecosystems from which fire has been excluded. In the southeastern U.S.A., nearly a century of fire exclusion in pine savannas has led to significant biodiversity declines in one of the most species-rich ecosystems of North America. In these savannas, frequent fires that support biodiversity are driven by vegetation-fire feedbacks. Understory grasses are key components of these feedbacks, fueling the spread of fires that keep tree density low and maintain a high-light environment. When fire is reintroduced to long-unburned sites, however, remnant populations of bunchgrasses might experience high mortality from fuel accumulation during periods of fire exclusion. Our objective was to quantify fire effects on wiregrass (Aristida beyrichiana), a key component of vegetation-fire feedbacks, following 16 years without fire in a dry pine savanna typically considered to burn every 1-3 years. We examined how wiregrass size and fuel (duff depth and presence of pinecones) affected post-fire survival, inflorescence and seed production, and seed germination. Wiregrass exhibited high survival regardless of size or fuels. Probability of flowering and inflorescence number per plant were unaffected by fuel treatments but increased significantly with plant size (p = 0.016). Germination of filled seeds was consistent (29-43%) regardless of fuels, although plants in low duff produced the greatest proportion of filled seeds. The ability of bunchgrasses to persist and reproduce following fire exclusion could jumpstart efforts to reinstate frequent-fire regimes and facilitate biodiversity restoration where remnant bunchgrass populations remain.
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Project description:MotivationHigh-dimensional data are frequently generated in genome-wide association studies (GWAS) and other studies. It is important to identify features such as single nucleotide polymorphisms (SNPs) in GWAS that are associated with a disease. Random forests represent a very useful approach for this purpose, using a variable importance score. This importance score has several shortcomings. We propose an alternative importance measure to overcome those shortcomings.ResultsWe characterized the effect of multiple SNPs under various models using our proposed importance measure in random forests, which uses maximal conditional chi-square (MCC) as a measure of association between a SNP and the trait conditional on other SNPs. Based on this importance measure, we employed a permutation test to estimate empirical P-values of SNPs. Our method was compared to a univariate test and the permutation test using the Gini and permutation importance. In simulation, the proposed method performed consistently superior to the other methods in identifying of risk SNPs. In a GWAS of age-related macular degeneration, the proposed method confirmed two significant SNPs (at the genome-wide adjusted level of 0.05). Further analysis showed that these two SNPs conformed with a heterogeneity model. Compared with the existing importance measures, the MCC importance measure is more sensitive to complex effects of risk SNPs by utilizing conditional information on different SNPs. The permutation test with the MCC importance measure provides an efficient way to identify candidate SNPs in GWAS and facilitates the understanding of the etiology between genetic variants and complex diseases.Contactheping.zhang@yale.eduSupplementary informationSupplementary data are available at Bioinformatics online.
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Project description:Forests are experiencing significant changes; studying geographic patterns in forests is critical in understanding the impact of forest dynamics to biodiversity, soil erosion, water chemistry and climate. Few studies have examined forest geographic pattern changes other than fragmentation; however, other spatial processes of forest dynamics are of equal importance. Here, we study forest attrition, the complete removal of forest patches, that can result in complete habitat loss, severe decline of population sizes and species richness, and shifts of local and regional environmental conditions. We aim to develop a simple yet insightful proximity-based spatial indicator capturing forest attrition that is independent of spatial scale and boundaries with worldwide application potential. Using this proximity indicator, we evaluate forest attrition across ecoregions, land ownership and urbanization stratifications across continental United States of America. Nationally, the total forest cover loss was approximately 90,400 km2, roughly the size of the state of Maine, constituting a decline of 2.96%. Examining the spatial arrangement of this change the average FAD was 3674m in 1992 and increased by 514m or 14.0% in 2001. Simulations of forest cover loss indicate only a 10m FAD increase suggesting that the observed FAD increase was more than an order of magnitude higher than expected. Furthermore, forest attrition is considerably higher in the western United States, in rural areas and in public lands. Our mathematical model (R2 = 0.93) supports estimation of attrition for a given forest cover. The FAD metric quantifies forest attrition across spatial scales and geographic boundaries and assesses unambiguously changes over time. The metric is applicable to any landscape and offers a new complementary insight on forest landscape patterns from local to global scales, improving future exploration of drivers and repercussions of forest cover changes and supporting more informative management of forest carbon, changing climate and species biodiversity.
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Project description:We ascertained villagers' perceptions about the importance of forests for their livelihoods and health through 1,837 reliably answered interviews of mostly male respondents from 185 villages in Indonesian and Malaysian Borneo. Variation in these perceptions related to several environmental and social variables, as shown in classification and regression analyses. Overall patterns indicated that forest use and cultural values are highest among people on Borneo who live close to remaining forest, and especially among older Christian residents. Support for forest clearing depended strongly on the scale at which deforestation occurs. Deforestation for small-scale agriculture was generally considered to be positive because it directly benefits people's welfare. Large-scale deforestation (e.g., for industrial oil palm or acacia plantations), on the other hand, appeared to be more context-dependent, with most respondents considering it to have overall negative impacts on them, but with people in some areas considering the benefits to outweigh the costs. The interviews indicated high awareness of negative environmental impacts of deforestation, with high levels of concern over higher temperatures, air pollution and loss of clean water sources. Our study is unique in its geographic and trans-national scale. Our findings enable the development of maps of forest use and perceptions that could inform land use planning at a range of scales. Incorporating perspectives such as these could significantly reduce conflict over forest resources and ultimately result in more equitable development processes.
| S-EPMC3767661 | biostudies-literature
Project description:BackgroundThe random forest (RF) method is a commonly used tool for classification with high dimensional data as well as for ranking candidate predictors based on the so-called random forest variable importance measures (VIMs). However the classification performance of RF is known to be suboptimal in case of strongly unbalanced data, i.e. data where response class sizes differ considerably. Suggestions were made to obtain better classification performance based either on sampling procedures or on cost sensitivity analyses. However to our knowledge the performance of the VIMs has not yet been examined in the case of unbalanced response classes. In this paper we explore the performance of the permutation VIM for unbalanced data settings and introduce an alternative permutation VIM based on the area under the curve (AUC) that is expected to be more robust towards class imbalance.ResultsWe investigated the performance of the standard permutation VIM and of our novel AUC-based permutation VIM for different class imbalance levels using simulated data and real data. The results suggest that the new AUC-based permutation VIM outperforms the standard permutation VIM for unbalanced data settings while both permutation VIMs have equal performance for balanced data settings.ConclusionsThe standard permutation VIM loses its ability to discriminate between associated predictors and predictors not associated with the response for increasing class imbalance. It is outperformed by our new AUC-based permutation VIM for unbalanced data settings, while the performance of both VIMs is very similar in the case of balanced classes. The new AUC-based VIM is implemented in the R package party for the unbiased RF variant based on conditional inference trees. The codes implementing our study are available from the companion website: http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/070_drittmittel/janitza/index.html.
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Project description:BackgroundA total of 290 mosquito species are recorded in Cambodia among which 43 are known vectors of pathogens. As Cambodia is heavily affected by deforestation, a potential change in the dynamic of vector-borne diseases (VDBs) could occur through alteration of the diversity and density of sylvatic vector mosquitoes and induce an increase in their interactions with humans. Understanding mosquito diversity is therefore critical, providing valuable data for risk assessments concerning the (re)emergence of local VBDs. Consequently, this study mainly aimed to understand the spatial and temporal distribution of sylvatic mosquito populations of Cambodia by determining which factors impact on their relative abundance and presence.MethodsA study was conducted in 12 sites from four forests in Cambodia. All mosquitoes, collected during the dry and rainy seasons, were morphologically identified. The diversity and relative density of mosquito species in each site were calculated along with the influence of meteorological and geographical factors using a quasi-Poisson generalized linear model.ResultsA total of 9392 mosquitoes were collected belonging to 13 genera and 85 species. The most represented genera were Culex, accounting for 46% of collected mosquitoes, and Aedes (42%). Besides being the most abundant species, Culex pseudovishnui and Aedes albopictus, which are known vectors of numerous arboviruses, were present in all sites during both dry and rainy seasons. The presence of mosquito species reported to be zoo-anthropophilic feeders was also observed in both forested and urban areas. Finally, this study demonstrated that altitude, temperature and precipitation impacted the abundance of mosquitoes but also influenced species community composition.ConclusionThe results indicate an important diversity of mosquitoes in the four forests and an influence of meteorological and geographical factors on their community. Additionally, this work highlights in parallel the abundance of species considered to be of medical importance and therefore underlines the high risk of pathogen emergence/re-emergence in the region.
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