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Morelos Juàrez Citlalli - One of the best experts on this subject based on the ideXlab platform.
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Conservation of brown-headed spider monkeys (Ateles fusciceps fusciceps) in NW Ecuador: applying an agent-based model
2016Co-Authors: Morelos Juàrez CitlalliAbstract:Understanding the impacts of landscape fragmentation, degradation and hunting on arboreal species of conservation concern, such as the critically endangered brownheaded spider monkey (Ateles fusciceps fusciceps), remains a major challenge in conservation biology. Current research on the population status of this primate and the area it inhabits in the Ecuadorian Choco is urgently needed to aid in the design of specific and effective conservation strategies. I surveyed the population of A. f. fusciceps in the unprotected forest cooperative Tesoro Escondido in the buffer zone of the Cotacachi Cayapas Ecological Reserve during the year 2012-2013. Using the line transect method I estimated a population density of 15.79 individuals/km2. I found an average subgroup size of 3.42 individuals and a female biased population. Identifying key food resources for critically endangered species is vital in their conservation, particularly if these resources are also targeted by anthropogenic activities such as logging. The province where A. f. fusciceps is found is also heavily dependent on commercial logging with no information available on its impacts on key feeding resources for this primate. I characterised the oristic composition of the habitat of A. f. fusciceps and estimated the availability of fruit resources for the annual cycle of 2012-2013 in sixteen 0.1 hectare vegetation plots. I determined feeding preferences for A. f. fusciceps using behavioural observations applying the Chesson ε index to identify key feeding tree species. I reviewed regional logging permits to identify species targeted for extraction by the timber industry and calculated extraction volumes in primary forest for key feeding tree species to identify potential conflict between logging and primate diet. I identified 65 fruiting tree species from 34 families that formed the diet of A. f. fusciceps . The Chesson ε index identified twelve species as preferred species with further phenological observations identifying seven species as staple foods and two palms as potential foods consumed in times of fruit scarcity. Additionally, I found that the lipid rich fruits of Brosimum utile make this an important resource for this primate throughout the year. Furthermore, of 65 feeding tree species identified for A. f. fusciceps , 35 species are also targeted as sources of timber. Five key feeding species would be depleted under current sustainable management extraction protocols while two other species would be significantly impacted in terms of local abundance. Hunting pressure on A. f. fusciceps has been reported as one of the main causes of its population decline. However, no current research on the extent of this activity or its causes was available. I carried out semi-structured interviews in nine indigenous Chachi villages, as well as two Colono towns, to evaluate the occurrence of hunting activity and to identify drivers, attitudes and behaviour of hunters. In total I interviewed 62 people, 41 Chachis and 21 Colonos. From the Chachi interviewees 93% identified themselves as hunters, with subsistence hunting the main driver for this activity and central to their culture, especially for men. Colonos identified less with this activity (only 38%), and with more varied reasons, such as commerce and conflict. Only Chachis accepted the hunting of spider monkeys, with the main reason given as their taste. Keeping spider monkeys as pets was also a regular activity prior to tougher law enforcement by the Ministry of Environment (MAE). Information on medicinal uses from spider monkeys was also gathered, as well as information of other species hunted in the area. Even though Ecuadorian law recognises the right of indigenous peoples to hunt within their territories, it also forbids hunting critically endangered species. From the interviews it is evident that information and understanding of this law has not been successfully transmitted. Determining the effects of fragmentation, hunting and habitat degradation on populations viability of this primate is crucial before investing heavily in local sustainable livelihoods and conservation initiatives. A range of fragmentation Metrics are available to study habitat fragmentation, yet their relationship to survival of populations of conservation concern remains to be quantified. I applied an agent-based model (ABM), calibrated on field-collected datasets on forest fruit dynamics, behaviour and feeding ecology of A. f. fusciceps, to first identify an optimised fragmentation statistic to be used to screen satellite imagery and identify remaining priority conservation areas in unprotected, fragmented forests in NW Ecuador. I then used the ABM to further explore the combined impacts of fragmentation, hunting and logging. Mean Patch Area was the best fragmentation Metric Predictor of population numbers, I identified a MPA of 174.9 hectares as the cut-off point for the survival of brown-headed spider monkeys given the lowest combinations of logging activity and hunting pressure and I used it to identify priority conservation areas in NW Ecuador. Implementing conservation strategies in areas where people and nature interact is a challenging task. I designed a step by step framework for the conservation of critically endangered species. Based on my experience with Ateles fusciceps fusciceps as a case study, I present the design, assessment and implementation of different community-based strategies
John K. Kruschke - One of the best experts on this subject based on the ideXlab platform.
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doing bayesian data analysis a tutorial with r jags and stan
2014Co-Authors: John K. KruschkeAbstract:There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis obtainable to a wide audience. Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan provides an accessible approach to Bayesian data analysis, as material is explained clearly with concrete examples. The book begins with the basics, including essential concepts of probability and random sampling, and gradually progresses to advanced hierarchical modeling methods for realistic data. Included are step-by-step instructions on how to conduct Bayesian data analyses in the popular and free software R and WinBugs. This book is intended for first-year graduate students or advanced undergraduates. It provides a bridge between undergraduate training and modern Bayesian methods for data analysis, which is becoming the accepted research standard. Knowledge of algebra and basic calculus is a prerequisite. New to this Edition (partial list): * There are all new programs in JAGS and Stan. The new programs are designed to be much easier to use than the scripts in the first edition. In particular, there are now compact high-level scripts that make it easy to run the programs on your own data sets. This new programming was a major undertaking by itself.* The introductory Chapter 2, regarding the basic ideas of how Bayesian inference re-allocates credibility across possibilities, is completely rewritten and greatly expanded.* There are completely new chapters on the programming languages R (Ch. 3), JAGS (Ch. 8), and Stan (Ch. 14). The lengthy new chapter on R includes explanations of data files and structures such as lists and data frames, along with several utility functions. (It also has a new poem that I am particularly pleased with.) The new chapter on JAGS includes explanation of the RunJAGS package which executes JAGS on parallel computer cores. The new chapter on Stan provides a novel explanation of the concepts of Hamiltonian Monte Carlo. The chapter on Stan also explains conceptual differences in program flow between it and JAGS.* Chapter 5 on Bayes' rule is greatly revised, with a new emphasis on how Bayes' rule re-allocates credibility across parameter values from prior to posterior. The material on model comparison has been removed from all the early chapters and integrated into a compact presentation in Chapter 10.* What were two separate chapters on the Metropolis algorithm and Gibbs sampling have been consolidated into a single chapter on MCMC methods (as Chapter 7). There is extensive new material on MCMC convergence diagnostics in Chapters 7 and 8. There are explanations of autocorrelation and effective sample size. There is also exploration of the stability of the estimates of the HDI limits. New computer programs display the diagnostics, as well.* Chapter 9 on hierarchical models includes extensive new and unique material on the crucial concept of shrinkage, along with new examples.* All the material on model comparison, which was spread across various chapters in the first edition, in now consolidated into a single focused chapter (Ch. 10) that emphasizes its conceptualization as a case of hierarchical modeling.* Chapter 11 on null hypothesis significance testing is extensively revised. It has new material for introducing the concept of sampling distribution. It has new illustrations of sampling distributions for various stopping rules, and for multiple tests.* Chapter 12, regarding Bayesian approaches to null value assessment, has new material about the region of practical equivalence (ROPE), new examples of accepting the null value by Bayes factors, and new explanation of the Bayes factor in terms of the Savage-Dickey method.* Chapter 13, regarding statistical power and sample size, has an extensive new section on sequential testing, and making the research goal be precision of estimation instead of rejecting or accepting a particular value.* Chapter 15, which introduces the generalized linear model, is fully revised, with more complete tables showing combinations of predicted and Predictor variable types.* Chapter 16, regarding estimation of means, now includes extensive discussion of comparing two groups, along with explicit estimates of effect size.* Chapter 17, regarding regression on a single Metric Predictor, now includes extensive examples of robust regression in JAGS and Stan. New examples of hierarchical regression, including quadratic trend, graphically illustrate shrinkage in estimates of individual slopes and curvatures. The use of weighted data is also illustrated.* Chapter 18, on multiple linear regression, includes a new section on Bayesian variable selection, in which various candidate Predictors are probabilistically included in the regression model.* Chapter 19, on one-factor ANOVA-like analysis, has all new examples, including a completely worked out example analogous to analysis of covariance (ANCOVA), and a new example involving heterogeneous variances.* Chapter 20, on multi-factor ANOVA-like analysis, has all new examples, including a completely worked out example of a split-plot design that involves a combination of a within-subjects factor and a between-subjects factor.* Chapter 21, on logistic regression, is expanded to include examples of robust logistic regression, and examples with nominal Predictors.* There is a completely new chapter (Ch. 22) on multinomial logistic regression. This chapter fills in a case of the generalized linear model (namely, a nominal predicted variable) that was missing from the first edition.* Chapter 23, regarding ordinal data, is greatly expanded. New examples illustrate single-group and two-group analyses, and demonstrate how interpretations differ from treating ordinal data as if they were Metric.* There is a new section (25.4) that explains how to model censored data in JAGS.* Many exercises are new or revised. * Accessible, including the basics of essential concepts of probability and random sampling* Examples with R programming language and JAGS software* Comprehensive coverage of all scenarios addressed by non-Bayesian textbooks: t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis)* Coverage of experiment planning* R and JAGS computer programming code on website* Exercises have explicit purposes and guidelines for accomplishment* Provides step-by-step instructions on how to conduct Bayesian data analyses in the popular and free software R and WinBugs
Frank W. Davis - One of the best experts on this subject based on the ideXlab platform.
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Evaluating Drought Impact on Postfire Recovery of Chaparral Across Southern California
Ecosystems, 2020Co-Authors: Emanuel A. Storey, Douglas A. Stow, Dar A. Roberts, John F. O’leary, Frank W. DavisAbstract:Chaparral shrubs in southern California may be vulnerable to frequent fire and severe drought. Drought may diminish postfire recovery or worsen impact of short-interval fires. Field-based studies have not shown the extent and magnitude of drought effects on recovery, which may vary among chaparral types and climatic zones. We tracked regional patterns of shrub cover based on June-solstice Landsat Normalized Difference Vegetation Index series, compared between the periods 1984–1989 and 2014–2018. High spatial resolution ortho-imagery was used to map shrub cover in distributed sample plots, to empirically constrain the Landsat-based estimates of mature-stage lateral canopy recovery. We evaluated precipitation, climatic water deficit (CWD), and Palmer Drought Severity Index in summer and wet seasons preceding and following fire, as regional Predictors of recovery in 982 locations between the Pacific Coast and inland deserts. Wet-season CWD was the strongest drought-Metric Predictor of recovery, contributing 34–43% of explanatory power in multivariate regressions ( R ^2 = 0.16–0.42). Limited recovery linked to drought was most prevalent in transmontane chamise chaparral; impacts were minor in montane areas, and in mixed and montane chaparral types. Elevation was correlated negatively to recovery of transmontane chamise; this may imply acute drought sensitivity in resprouts which predominate seedlings at higher elevations. Landsat Visible Atmospherically Resistant Index (sensitive to live-fuel moisture) was evaluated as a landscape-scale Predictor of recovery and explained the greatest amount of variance in a multivariate regression ( R ^2 = 0.53). We find that drought severity was more closely related to recovery differences among twice-burned sites than was fire-return interval. Summarily, drought has a major role in long-term shrub cover reduction within xeric chaparral ecotones bounding the Mojave Desert and Colorado Desert, likely in tandem with other global change stressors.
Jacob Klos - One of the best experts on this subject based on the ideXlab platform.
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influences of low frequency energy and testing environment on annoyance responses to supersonic aircraft noise when heard indoors
Journal of the Acoustical Society of America, 2020Co-Authors: Daniel Carr, Alexandra Loubeau, Patricia Davies, Jonathan Rathsam, Jacob KlosAbstract:The research reported is part of a larger effort to develop models to predict community response to transient sounds, including sonic booms. Such models can be used along with aircraft sound predictions to guide the design of supersonic aircraft to produce generally acceptable sounds. A test was conducted to examine the influence of low frequencies on people's responses to recorded and simulated booms and other environmental transients, heard indoors over earphones. The results of this test and a companion test conducted in a sonic boom simulator were compared to see if the playback environment affected responses. Annoyance models were also examined. E-weighted Sound Exposure level (ESEL) was the sound Metric most highly correlated to mean annoyance with B-weighted Sound Exposure Level (BSEL) and Perceived Level performing similarly. Predictions were improved by including Heaviness, Duration, and rate of change of Loudness in models with a loudness Metric. Models were also estimated by using the average responses from both tests and Metrics generated from outdoor versions of the sounds. These models also produced accurate annoyance predictions. BSEL was the best single-Metric Predictor, with ESEL close behind. Including Heaviness, Duration, and rate of change of Loudness resulted in R2 values as high as 0.90.
Emanuel A. Storey - One of the best experts on this subject based on the ideXlab platform.
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Evaluating Drought Impact on Postfire Recovery of Chaparral Across Southern California
Ecosystems, 2020Co-Authors: Emanuel A. Storey, Douglas A. Stow, Dar A. Roberts, John F. O’leary, Frank W. DavisAbstract:Chaparral shrubs in southern California may be vulnerable to frequent fire and severe drought. Drought may diminish postfire recovery or worsen impact of short-interval fires. Field-based studies have not shown the extent and magnitude of drought effects on recovery, which may vary among chaparral types and climatic zones. We tracked regional patterns of shrub cover based on June-solstice Landsat Normalized Difference Vegetation Index series, compared between the periods 1984–1989 and 2014–2018. High spatial resolution ortho-imagery was used to map shrub cover in distributed sample plots, to empirically constrain the Landsat-based estimates of mature-stage lateral canopy recovery. We evaluated precipitation, climatic water deficit (CWD), and Palmer Drought Severity Index in summer and wet seasons preceding and following fire, as regional Predictors of recovery in 982 locations between the Pacific Coast and inland deserts. Wet-season CWD was the strongest drought-Metric Predictor of recovery, contributing 34–43% of explanatory power in multivariate regressions ( R ^2 = 0.16–0.42). Limited recovery linked to drought was most prevalent in transmontane chamise chaparral; impacts were minor in montane areas, and in mixed and montane chaparral types. Elevation was correlated negatively to recovery of transmontane chamise; this may imply acute drought sensitivity in resprouts which predominate seedlings at higher elevations. Landsat Visible Atmospherically Resistant Index (sensitive to live-fuel moisture) was evaluated as a landscape-scale Predictor of recovery and explained the greatest amount of variance in a multivariate regression ( R ^2 = 0.53). We find that drought severity was more closely related to recovery differences among twice-burned sites than was fire-return interval. Summarily, drought has a major role in long-term shrub cover reduction within xeric chaparral ecotones bounding the Mojave Desert and Colorado Desert, likely in tandem with other global change stressors.