Camera trap arrays improve detection probability of wildlife: Investigating study design considerations using an empirical dataset.

Camera trapping is a standard tool in ecological research and wildlife conservation. Study designs, particularly for small-bodied or cryptic wildlife species often attempt to boost low detection probabilities by using non-random camera placement or baited cameras, which may bias data, or incorrectly estimate detection and occupancy. We investigated the ability of non-baited, multi-camera arrays to increase detection probabilities of wildlife. Study design components were evaluated for their influence on wildlife detectability by iteratively parsing an empirical dataset (1) by different sizes of camera arrays deployed (1-10 cameras), and (2) by total season length (1-365 days). Four species from our dataset that represented a range of body sizes and differing degrees of presumed detectability based on life history traits were investigated: white-tailed deer (Odocoileus virginianus), bobcat (Lynx rufus), raccoon (Procyon lotor), and Virginia opossum (Didelphis virginiana). For all species, increasing from a single camera to a multi-camera array significantly improved detection probability across the range of season lengths and number of study sites evaluated. The use of a two camera array increased survey detection an average of 80% (range 40-128%) from the detection probability of a single camera across the four species. Species that were detected infrequently benefited most from a multiple-camera array, where the addition of up to eight cameras produced significant increases in detectability. However, for species detected at high frequencies, single cameras produced a season-long (i.e, the length of time over which cameras are deployed and actively monitored) detectability greater than 0.75. These results highlight the need for researchers to be critical about camera trap study designs based on their intended target species, as detectability for each focal species responded differently to array size and season length. We suggest that researchers a priori identify target species for which inference will be made, and then design camera trapping studies around the most difficult to detect of those species

Long-Term Assessment of the Effects of COVID-19 and Isolation Care on Survivor Disability and Anxiety

We conducted an assessment of disability, anxiety, and other life impacts of COVID-19 and isolation care in a unique cohort of individuals. These included both community admissions to a university hospital as well as some of the earliest international aeromedical evacuees. Among an initial 16 COVID-19 survivors that were interviewed 6-12 months following their admission into isolation care, perception of their isolation care experience was related to their reporting of long-term consequences. However, anxiety and disability assessed with standard scores had no relationship with each other. Both capture of the isolation care experience and caution relying on single scoring systems for assessing long-term consequences in survivors are important considerations for on-going and future COVID-19 and other pandemic survivor research

Influence of rare species on electrofishing distance when estimating species richness of stream and river reaches

Abstract.-The electrofishing distance needed to estimate fish species richness at the stream or river reach scale is an important question in fisheries science. This distance is governed by the shape of the species accumulation curve, which, in turn, is influenced by a combination of factors, including the number of species, their overall abundances, habitat associations, the efficiency of the sampling method, and the occurrence of rare species. In this study we document the influence of rare species on the species accumulation curves from stream and river sites in data sets from five dispersed regions of the USA. Spatial discontinuity (i.e., a noncontinuous distribution within reaches) was observed in four of the five data sets, and the four data sets contained numerically rare species represented by one or two individuals (termed singletons and doubletons, respectively). Numerically rare species were typically proportionately rare (i.e., ,1% of the total number of individuals captured), but proportionately rare species were not always numerically rare and were dependent on the total number of fish captured. Species richness asymptotes were reached at shorter electrofishing distances when singletons and doubletons were removed. The number of singletons and doubletons in the samples remained relatively constant with increasing sampling effort (i.e., sampling distance and total abundance). Simulation modeling indicated that individual aggregation within species was not a plausible reason for spatially discontinuous species distributions. When accurately detecting the presence of species is a sampling goal, the presence and prevalence of numerically rare species may need to be considered in determining sampling protocols

Innovation and the circular economy: A systematic literature review

The circular economy emerged as an alternative model to the linear system, which now appears to be reaching its physical limitations. To transition to a circular economy, companies must not only be aware of but also engage in more sustainable practices. For such a transition, companies must rethink and innovate their business models and the ways they propose value to their clients while simultaneously considering environmental and social facets. This systematic literature review sought to map out from the company perspective the key topics interrelated with innovation and the circular economy, describing the internal and external factors to consider in such transition processes. Key lines of research were identified, and suggestions for future research and for facilitating movement toward a circular economy are provided. This work contributes to deepening the literature by identifying the priority areas concerning the circular economy and encouraging future research that meets international standards of excellence.info:eu-repo/semantics/publishedVersio

Nanotools for Neuroscience and Brain Activity Mapping

Neuroscience is at a crossroads. Great effort is being invested into deciphering specific neural interactions and circuits. At the same time, there exist few general theories or principles that explain brain function. We attribute this disparity, in part, to limitations in current methodologies. Traditional neurophysiological approaches record the activities of one neuron or a few neurons at a time. Neurochemical approaches focus on single neurotransmitters. Yet, there is an increasing realization that neural circuits operate at emergent levels, where the interactions between hundreds or thousands of neurons, utilizing multiple chemical transmitters, generate functional states. Brains function at the nanoscale, so tools to study brains must ultimately operate at this scale, as well. Nanoscience and nanotechnology are poised to provide a rich toolkit of novel methods to explore brain function by enabling simultaneous measurement and manipulation of activity of thousands or even millions of neurons. We and others refer to this goal as the Brain Activity Mapping Project. In this Nano Focus, we discuss how recent developments in nanoscale analysis tools and in the design and synthesis of nanomaterials have generated optical, electrical, and chemical methods that can readily be adapted for use in neuroscience. These approaches represent exciting areas of technical development and research. Moreover, unique opportunities exist for nanoscientists, nanotechnologists, and other physical scientists and engineers to contribute to tackling the challenging problems involved in understanding the fundamentals of brain function

Season detection probability (p*) by season length (0–365 days) and array size (1, 2, 3, 5, 7, 10 cameras; lines) across four species (White-tailed deer, upper left; Raccoon, upper right; Virginia opossum, lower left; Bobcat, lower right), calculated by randomly parsing a yearlong data set with 5,000 Monte Carlo iterations.

<p>Lines represent best fit of nonlinear model in R using the <i>NLS</i> package and grey indicates 95% confidence intervals estimated from R package <i>confint</i>. Only a subset of the total array sizes are displayed to improve visual representation of data.</p