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Following the passage of a tropical cyclone (TC) the changes in temperature, salinity, nutrient concentration, water clarity, pigments and phytoplankton taxa were assessed at 42 stations from eight sites ranging from the open ocean, through the coastal zone and into estuaries. The impacts of the TC were estimated relative to the long-term average (LTA) conditions as well as before and after the TC. Over all sites the most consistent environmental impacts associated with TCs were an average 41% increase in turbidity, a 13% decline in salinity and a 2% decline in temperature relative to the LTA. In the open ocean, the nutrient concentrations, cyanobacteria and picoeukaryote abundances increased at depths between 100 and 150 m for up to 3 months following a TC. While at the riverine end of coastal estuaries, the predominate short-term response was a strong decline in salinity and phytoplankton suggesting these impacts were initially dominated by advection. The more intermediate coastal water-bodies generally experienced declines in salinity, significant reductions in water clarity, plus significant increases in nutrient concentrations and phytoplankton abundance. These intermediate waters typically developed dinoflagellate, diatom or cryptophyte blooms that elevated phytoplankton biomass for 1–3 months following a TC.

 

Native Americans domesticated maize ( Zea mays ssp. mays ) from lowland teosinte parviglumis ( Zea mays ssp. parviglumis) in the warm Mexican southwest and brought it to the highlands of Mexico and South America where it was exposed to lower temperatures that imposed strong selection on flowering time. Phospholipids are important metabolites in plant responses to low-temperature and phosphorus availability and have been suggested to influence flowering time. Here, we combined linkage mapping with genome scans to identify High PhosphatidylCholine 1 ( HPC1 ), a gene that encodes a phospholipase A1 enzyme, as a major driver of phospholipid variation in highland maize. Common garden experiments demonstrated strong genotype-by-environment interactions associated with variation at HPC1, with the highland HPC1 allele leading to higher fitness in highlands, possibly by hastening flowering. The highland maize HPC1 variant resulted in impaired function of the encoded protein due to a polymorphism in a highly conserved sequence. A meta-analysis across HPC1 orthologs indicated a strong association between the identity of the amino acid at this position and optimal growth in prokaryotes. Mutagenesis of HPC1 via genome editing validated its role in regulating phospholipid metabolism. Finally, we showed that the highland HPC1 allele entered cultivated maize by introgression from the wild highland teosinte Zea mays ssp. mexicana and has been maintained in maize breeding lines from the Northern United States, Canada, and Europe. Thus, HPC1 introgressed from teosinte mexicana underlies a large metabolic QTL that modulates phosphatidylcholine levels and has an adaptive effect at least in part via induction of early flowering time. 

Designing an effective conservation strategy requires understanding where rare species are located. Because rare species can be difficult to find, ecologists often identify other species called conservation surrogates that can help inform the distribution of rare species. Species distribution models typically rely on environmental data when predicting the occurrence of species, neglecting the effect of species' co‐occurrences and biotic interactions. Here, we present a new approach that uses Bayesian networks to improve predictions by modeling environmental co‐responses among species. For species from a European peat bog community, our approach consistently performs better than single‐species models and better than conventional multi‐species approaches that include the presence of nontarget species as additional independent variables in regression models. Our approach performs particularly well with rare species and when calibration data are limited. Furthermore, we identify a group of “predictor species” that are relatively common, insensitive to the presence of other species, and can be used to improve occurrence predictions of rare species. Predictor species are distinct from other categories of conservation surrogates such as umbrella or indicator species, which motivates focused data collection of predictor species to enhance conservation practices.

 

Many important demographic processes are seasonal, including survival. For many species, mortality risk is significantly higher at certain times of the year than at others, whether because resources are scarce, susceptibility to predators or disease is high, or both. Despite the importance of survival modelling in wildlife sciences, no tools are available to estimate the peak, duration and relative importance of these ‘seasons of mortality’.

We presentcyclomort, anrpackage that estimates the timing, duration and intensity of any number of mortality seasons with reliable confidence intervals. The package includes a model selection approach to determine the number of mortality seasons and to test whether seasons of mortality vary across discrete grouping factors.

We illustrate the periodic hazard function model and workflow of cyclomort with simulated data. We then estimate mortality seasons of two caribouRangifer taranduspopulations that have strikingly different mortality patterns, including different numbers and timing of mortality peaks, and a marked change in one population over time.

Thecyclomortpackage was developed to estimate mortality seasons for wildlife, but the package can model any time‐to‐event processes with a periodic component.

 

Home range estimation is routine practice in ecological research. While advances in animal tracking technology have increased our capacity to collect data to support home range analysis, these same advances have also resulted in increasingly autocorrelated data. Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open. This question is particularly relevant given that most estimators assume independently sampled data. Here, we provide a comprehensive evaluation of the effects of autocorrelation on home range estimation. We base our study on an extensive data set ofGPSlocations from 369 individuals representing 27 species distributed across five continents. We first assemble a broad array of home range estimators, including Kernel Density Estimation (KDE) with four bandwidth optimizers (Gaussian reference function, autocorrelated‐Gaussian reference function [AKDE], Silverman's rule of thumb, and least squares cross‐validation), Minimum Convex Polygon, and Local Convex Hull methods. Notably, all of these estimators exceptAKDEassume independent and identically distributed (IID) data. We then employ half‐sample cross‐validation to objectively quantify estimator performance, and the recently introduced effective sample size for home range area estimation () to quantify the information content of each data set. We found thatAKDE95% area estimates were larger than conventionalIID‐based estimates by a mean factor of 2. The median number of cross‐validated locations included in the hold‐out sets byAKDE95% (or 50%) estimates was 95.3% (or 50.1%), confirming the largerAKDEranges were appropriately selective at the specified quantile. Conversely, conventional estimates exhibited negative bias that increased with decreasing . To contextualize our empirical results, we performed a detailed simulation study to tease apart how sampling frequency, sampling duration, and the focal animal's movement conspire to affect range estimates. Paralleling our empirical results, the simulation study demonstrated thatAKDEwas generally more accurate than conventional methods, particularly for small . While 72% of the 369 empirical data sets had >1,000 total observations, only 4% had an >1,000, where 30% had an <30. In this frequently encountered scenario of small ,AKDEwas the only estimator capable of producing an accurate home range estimate on autocorrelated data.