The need for research and development of effective approaches to weed control continues to increase globally. Adaptive protocols using diverse control methods are often required in ecological restoration as recruitment of native species is highly site‐specific, species‐specific, and experimental. The use of composted weed refuse to control other weeds may be a practical option; yet, the option is not well studied due to the accompanied risk of introducing weed propagules to areas where weed control is desired. Here, we tested the effectiveness of different physical control techniques including the use of mulch made by composting weed refuse on‐site. English ivy ( Hedera helix ), a non‐native, invasive species in the Pacific northwestern United States, was removed from a heavily invaded site, shredded, and composted. The mulch was compared with other methods of suppressing herb Robert ( Geranium robertianum ), another invasive species on‐site. Five treatments were tested: flame‐weeding, hand‐pulling, mulching, hand‐pulling followed by mulching, and flame‐weeding followed by mulching. The mulch and pull/mulch treatments were the most effective, reducing G. robertianum cover by 92 and 86% of pre‐treatment levels, respectively, and suppressing G. robertianum 2.9 and 1.6 times more than the control, respectively. The mechanism behind the effectiveness of the mulch is uncertain, but may be related to weed seed burial or the allelopathic potential of the mulch. Composting one invasive species to use as mulch to control another can be effective and merits trial elsewhere.
Recent advances in digital data collection have spurred accumulation of immense quantities of data that have potential to lead to remarkable ecological insight, but that also present analytic challenges. In the case of biologging data from birds, common analytical approaches to classifying movement behaviors are largely inappropriate for these massive data sets.We apply a framework for using K-means clustering to classify bird behavior using points from short time interval GPS tracks. K-means clustering is a well-known and computationally efficient statistical tool that has been used in animal movement studies primarily for clustering segments of consecutive points. To illustrate the utility of our approach, we apply K-means clustering to six focal variables derived from GPS data collected at 1-11 s intervals from free-flying bald eagles (Haliaeetus leucocephalus) throughout the state of Iowa, USA. We illustrate how these data can be used to identify behaviors and life-stage- and age-related variation in behavior.After filtering for data quality, the K-means algorithm identified four clusters in >2 million GPS telemetry data points. These four clusters corresponded to three movement states: ascending, flapping, and gliding flight; and one non-moving state: perching. Mapping these states illustrated how they corresponded tightly to expectations derived from natural history observations; for example, long periods of ascending flight were often followed by long gliding descents, birds alternated between flapping and gliding flight.The K-means clustering approach we applied is both an efficient and effective mechanism to classify and interpret short-interval biologging data to understand movement behaviors. Furthermore, because it can apply to an abundance of very short, irregular, and high-dimensional movement data, it provides insight into small-scale variation in behavior that would not be possible with many other analytical approaches.
File List
Xeq0_Rcode.R (MD5: 0189df9bc7f877f78943a34f377c950c)
Description
The Xeq0_Rcode.R file is an R script file that calculates the posterior distribution of M, the total fatality at a wind facility, assuming observed count of carcasses=0. The posterior distribution is a function of the prior, the overall probability of detecting a carcass killed at the facility (g), and the number of carcasses counted during the search process (x). Since we are assuming x = 0, we only need posterior distributions for each combination of g and prior. For a given prior, the code below calculates an array with a posterior for each value of g. There are three possibilities for g: fixed, uncertain (1.732x), highly uncertain (3x). The degree of uncertainty about g is given in terms of the width of the CI in terms of odds ratios: if Upr and Lwr are upper and lower bounds on CI for g, then odds(Upr)/odds(Lwr) is a measure of uncertainty about g.
Studies examining the effects of human disturbance on avian parental behavior and reproductive success are fundamental to bird conservation. However, many such studies fail to also consider the influence of natural threats, a variable environment, and parental roles. Our work examines interactive relationships of cyclical (time of day, tide, temperature, seasonality) and stochastic (natural/human disturbance) processes with incubation patterns (attendance, bout lengths, recess rates) of the Black Oystercatcher (Haematopus bachmani), a shorebird of conservation concern. We used 24-hr-per-day video monitoring of 13 molecularly sexed breeding pairs to systematically examine incubation, revealing previously undocumented information that may inform conservation practices for the genus. Seven of 22 video-monitored nests failed, primarily from egg depredation by nocturnal mammals. Analyses of 3177 hr of video footage indicated a near doubling of incubation-bout lengths at night, corresponding to the increased risk of nighttime egg predation. Females had higher overall nest attendance (54% vs. 42%) and longer mean incubation bouts than did males (88 min vs. 73 min). Uninterrupted incubation bouts were over twice as long as bouts interrupted by disturbance. Incubating males departed nests substantially more frequently because of nest-area disturbances than did females in one but not both years of our study. Our findings suggest that the sexes incubate in different but complementary patterns, facilitating efficient egg care in a dynamic environment with several nest threats. We emphasize the importance of considering natural influences when human threats to shorebird reproductive behavior and success are evaluated.
First posted November 7, 2018 For additional information, contact: Director, Forest and Rangeland Ecosystem Science CenterU.S. Geological Survey777 NW 9th St., Suite 400Corvallis, Oregon 97330 GenEst (a generalized estimator of mortality) is a suite of statistical models and software tools for generalized mortality estimation. It was specifically designed for estimating the number of bird and bat fatalities at solar and wind power facilities, but both the software (Dalthorp and others, 2018) and the underlying statistical models are general enough to be useful in various situations to estimate the size of open populations when detection probabilities and search coverages are less than 1. In this report, we outline the statistical models and data structures underlying the estimator. The models are numerous, complex, and intricately interwoven. Discussion begins with broad, high-level overviews of the general models. The lower-level technical details are then gradually added. Broader and less technical discussions on the general context and applications of the models and the use of the software are available in the software user guide (Simonis and others, 2018), vignettes bundled with the software, and the help files within the software itself.
This article is a tutorial for the R‐package carcass . It starts with a short overview of common methods used to estimate mortality based on carcass searches. Then, it guides step by step through a simple example. First, the proportion of animals that fall into the search area is estimated. Second, carcass persistence time is estimated based on experimental data. Third, searcher efficiency is estimated. Fourth, these three estimated parameters are combined to obtain the probability that an animal killed is found by an observer. Finally, this probability is used together with the observed number of carcasses found to obtain an estimate for the total number of killed animals together with a credible interval.
Abstract Estimating bird and bat mortality at wind facilities typically involves searching for carcasses on the ground near turbines. Some fraction of carcasses inevitably lie outside the search plots, and accurate mortality estimation requires accounting for those carcasses using models to extrapolate from searched to unsearched areas. Such models should account for variation in carcass density with distance, and ideally also for variation with direction (anisotropy). We compare five methods of accounting for carcasses that land outside the searched area (ratio, weighted distribution, non-parametric, and two generalized linear models ( glm )) by simulating spatial arrival patterns and the detection process to mimic observations which result from surveying only, or primarily, roads and pads (R&P) and applying the five methods. Simulations vary R&P configurations, spatial carcass distributions (isotropic and anisotropic), and per turbine fatality rates. Our results suggest that the ratio method is less accurate with higher variation relative to the other four methods which all perform similarly under isotropy. All methods were biased under anisotropy; however, including direction covariates in the glm method substantially reduced bias. In addition to comparing methods of accounting for unsearched areas, we suggest a semiparametric bootstrap to produce confidence-based bounds for the proportion of carcasses that land in the searched area.
First posted April 13, 2020 For additional information, contact: Director, Western Fisheries Research CenterU.S. Geological Survey6505 NE 65th StreetSeattle, Washington 98115-5016 We evaluate three approaches to accounting for incidental carcasses when estimating an upper bound on total mortality (𝑀) as 𝑀∗ using the Evidence of Absence model (EoA; Dalthorp and others, 2017) to assess compliance with an Incidental Take Permit (ITP) (Dalthorp & Huso, 2015) under a monitoring protocol that includes formal, dedicated carcass surveys that achieve an overall detection probability of 𝑔𝑠=0.15 in the first year, followed by 4 years with no formal monitoring but with carcasses potentially discovered incidentally by operations and maintenance crews in their normal course of activity or otherwise discovered outside the formal searches. We refer to carcasses discovered incidentally as “incidentals” and define 𝑥𝑖 as the count of incidentals. Similarly, we define 𝑥𝑠 as the number of carcasses found during the formal searches conducted the first year.
