Habitat use of the ocelot (Leopardus pardalis) in Brazilian Amazon

Amazonia forest plays a major role in providing ecosystem services for human and sanctuaries for wildlife. However, ongoing deforestation and habitat fragmentation in the Brazilian Amazon has threatened both. The ocelot is an ecologically important mesopredator and a potential conservation ambassador species, yet there are no previous studies on its habitat preference and spatial patterns in this biome. From 2010 to 2017, twelve sites were surveyed, totaling 899 camera trap stations, the largest known dataset for this species. Using occupancy modeling incorporating spatial autocorrelation, we assessed habitat use for ocelot populations across the Brazilian Amazon. Our results revealed a positive sigmoidal correlation between remote‐sensing derived metrics of forest cover, disjunct core area density, elevation, distance to roads, distance to settlements and habitat use, and that habitat use by ocelots was negatively associated with slope and distance to river/lake. These findings shed light on the regional scale habitat use of ocelots and indicate important species–habitat relationships, thus providing valuable information for conservation management and land‐use planning.     

Quantile regression estimates of animal population trends

Ecologists often estimate population trends of animals in time series of counts using linear regression to estimate parameters in a linear transformation of multiplicative growth models, where logarithms of rates of change in counts in time intervals are used as response variables. We present quantile regression estimates for the median (0.50) and interquartile (0.25, 0.75) relationships as an alternative to mean regression estimates for common density-dependent and density-independent population growth models. We demonstrate that the quantile regression estimates are more robust to outliers and require fewer distributional assumptions than conventional mean regression estimates and can provide information on heterogeneous rates of change ignored by mean regression. We provide quantile regression trend estimates for 2 populations of greater sage-grouse (Centrocercus urophasianus) in Wyoming, USA, and for the Crawford population of Gunnison sage-grouse (Centrocercus minimus) in southwestern Colorado, USA. Our selected Gompertz models of density dependence for both populations of greater sage-grouse had smaller negative estimates of density-dependence terms and less variation in corresponding predicted growth rates (λ) for quantile than mean regression models. In contrast, our selected Gompertz models of density dependence with piecewise linear effects of years for the Crawford population of Gunnison sage-grouse had predicted changes in λ across years from quantile regressions that varied more than those from mean regression because of heterogeneity in estimated λs that were both less and greater than mean estimates. Our results add to literature establishing that quantile regression provides better behaved estimates than mean regression when there are outlying growth rates, including those induced by adjustments for zeros in the time series of counts. The 0.25 and 0.75 quantiles bracketing the median provide robust estimates of population changes (λ) for the central 50% of time series data and provide a 50% prediction interval for a single new prediction without making parametric distributional assumptions or assuming homogeneous λs. Compared to mean estimates, our quantile regression trend estimates for greater sage-grouse indicated less variation in density-dependent λs by minimizing sensitivity to outlying values, and for Gunnison sage-grouse indicated greater variation in density-dependent λs associated with heterogeneity among quantiles.     

Productivity,obesity, and human capital: Panel data evidence

The consequences of obesity for adults and children are well documented in the extant literature. We use panel data of 105 countries from 1990 to 2019 to estimate the effect of obesity on economic performance. We predict obesity using lagged values of child obesity as instruments. Predicted obesity has a negative and significant effect on productivity. This effect is independent of the effect of human capital and other macroeconomic determinants of economic performance. There is only weak evidence that this effect operates through the deterioration of human capital formation caused by childhood obesity.     

Prediction analysis for microbiome sequencing data

One goal of human microbiome studies is to relate host traits with human microbiome compositions. The analysis of microbial community sequencing data presents great statistical challenges, especially when the samples have different library sizes and the data are overdispersed with many zeros. To address these challenges, we introduce a new statistical framework, called predictive analysis in metagenomics via inverse regression (PAMIR), to analyze microbiome sequencing data. Within this framework, an inverse regression model is developed for overdispersed microbiota counts given the trait, and then a prediction rule is constructed by taking advantage of the dimension‐reduction structure in the model. An efficient Monte Carlo expectation‐maximization algorithm is proposed for maximum likelihood estimation. The method is further generalized to accommodate other types of covariates. We demonstrate the advantages of PAMIR through simulations and two real data examples.     

Bayesian hierarchical models suggest oldest known plant-visiting bat was omnivorous

The earliest record of plant visiting in bats dates to the Middle Miocene of La Venta, the world''s most diverse tropical palaeocommunity. Palynephyllum antimaster is known from molars that indicate nectarivory. Skull length, an important indicator of key traits such as body size, bite force and trophic specialization, remains unknown. We developed Bayesian models to infer skull length based on dental measurements. These models account for variation within and between species, variation between clades, and phylogenetic error structure. Models relating skull length to trophic level for nectarivorous bats were then used to infer the diet of the fossil. The skull length estimate for Palynephyllum places it among the larger lonchophylline bats. The inferred diet suggests Palynephyllum fed on nectar and insects, similar to its living relatives. Omnivory has persisted since the mid-Miocene. This is the first study to corroborate with fossil data that highly specialized nectarivory in bats requires an omnivorous transition.     

Robustness of Taylor’s law under spatial hierarchical groupings of forest tree samples

Testing how well Taylor's law (TL) describes spatial variation of the population density of a species requires grouping sampling areas (patches of habitat) into blocks so that a mean and a variance of the population density can be calculated over the patches in each block. The relationship between specific groupings and TL remains largely unknown. Here, using tree counts from a deciduous forest, we studied the effect of four biological methods of grouping sampling areas into blocks on the form and parameters of TL. Regardless of the method of grouping, the species-specific basal area densities obeyed TL, and the estimated slopes were not significantly different from one grouping method to another. Surprisingly, TL remained valid when four kinds of randomizations were performed to the biological groupings and tree census. These randomizations randomly assigned sampling areas to blocks, and/or randomized the species composition within or across sampling areas. We found that the form of TL was robust to different grouping methods and species randomizations, but its parameter values depended significantly on species compositions at sampling areas.     

Acceleration of cyanobacterial dominance in north temperate‐subarctic lakes during the Anthropocene

Increases in atmospheric temperature and nutrients from land are thought to be promoting the expansion of harmful cyanobacteria in lakes worldwide, yet to date there has been no quantitative synthesis of long‐term trends. To test whether cyanobacteria have increased in abundance over the past ~ 200 years and evaluate the relative influence of potential causal mechanisms, we synthesised 108 highly resolved sedimentary time series and 18 decadal‐scale monitoring records from north temperate‐subarctic lakes. We demonstrate that: (1) cyanobacteria have increased significantly since c. 1800 ce , (2) they have increased disproportionately relative to other phytoplankton, and (3) cyanobacteria increased more rapidly post c. 1945 ce . Variation among lakes in the rates of increase was explained best by nutrient concentration (phosphorus and nitrogen), and temperature was of secondary importance. Although cyanobacterial biomass has declined in some managed lakes with reduced nutrient influx, the larger spatio‐temporal scale of sedimentary records show continued increases in cyanobacteria throughout the north temperate‐subarctic regions.