Model vs. playback experiments: The impact of sensory mode on predator-specific escape responses in saki monkeys

Although experimentally simulating predator presence helps improve sample sizes in studies of free-ranging animals, few studies have examined whether auditory playbacks and visual models produce similar results. Additionally, it is unclear if anti-predator strategies are specific to predator hunting styles in understudied Neotropical pitheciid primates, limiting what we can generalize about this phenomenon across this taxonomic order. We conducted predator simulation experiments to assess whether wild Rylands' bald-faced saki monkeys (Pithecia rylandsi) recognize predators based solely on acoustic cues, exhibit predator-specific responses to different predator types, and vary responses to presentations in different sensory modes. In our playback experiments, sakis had weak responses to non-predator control vocalizations compared to jaguar growls and harpy eagle shrieks. In most predator playbacks, subjects' first glance corresponded to the direction from which simulated predators would typically attack (above vs. below). However, although sakis exhibited appropriate movement responses to harpy playbacks (i.e., descending canopy), they exhibited no clear movement patterns when presented with jaguar playbacks. In contrast, jaguar model experiments consistently elicited fast approaches, mobbing-style responses, and long alarm calling bouts. Thus, if we had relied on playbacks alone, we might have concluded that sakis have only generalized responses to terrestrial ambush predators. In fact, in all variables measured (e.g., latency, number of calls, and response duration), models of both predator species elicited stronger reactions than playbacks. Results indicate that bald-faced sakis can identify predators based solely on vocalizations, but do not exhibit predator-specific escape responses to terrestrial predators based on acoustic cues alone. The differential response to playbacks and models calls into question the reliability of using acoustic-only stimuli to assess the specificity of anti-predator behavior to predator hunting styles in some primate species.     

Sources of Mortality in Bald Eagles in Michigan, 1986–2017

As bald eagle populations recover, defining major sources of mortality provides managers important information to develop management plans and mitigation efforts. We obtained data from necropsies on 1,490 dead bald eagles (Haliaeetus leucocephalus) collected in Michigan, USA, conducted from 1986 to 2017 to determine causes of death (COD). Trauma and poisoning were the most common primary COD categories, followed by disease. Within trauma and poisoning, vehicular trauma (n = 532) and lead poisoning (n = 176) were the leading COD subcategories, respectively. Females comprised a greater number of carcasses for most COD diagnoses. The proportion of trauma and poisoning CODs significantly increased in the last few years of the study in comparison to a select few years at the beginning. Trauma CODs were greater in autumn months during whitetail deer (Odocoileus virginianus) breeding and hunting seasons and in February, when aquatic foraging is unavailable and eagles are likely forced to scavenge along roadsides. Poisoning CODs were greatest in late winter and early spring months, when deer carcasses containing lead ammunition, which are preserved by the cold weather, also become a supplemental food source. The major infectious disease CODs, West Nile virus and botulism (Clostridium botulinum type E), were more prevalent during summer months. We recommend moving road-killed carcasses, especially white-tailed deer, from the main thoroughfare to the back of the right-of-way, and the transition from lead ammunition and fishing tackle to non-toxic alternatives to decrease these main anthropogenic sources of mortality for bald eagles, and other scavenger species. © 2020 The Wildlife Society.     

Ultrasonication of insulin-loaded microgel particles produced by internal gelation: Impact on particle's size and insulin bioactivity

Alginate-dextran sulfate (ADS) microgel has been used to protect insulin from gastrointestinal attack and as a carrier to promote insulin permeation through intestinal epithelium. The throughput of ADS submicron particles generation by emulsification/internal gelation is limited by its wide size distribution.     

(−)-Reboxetine inhibits muscle nicotinic acetylcholine receptors by interacting with luminal and non-luminal sites

The interaction of (−)-reboxetine, a non-tricyclic norepinephrine selective reuptake inhibitor, with muscle-type nicotinic acetylcholine receptors (AChRs) in different conformational states was studied by functional and structural approaches. The results established that (−)-reboxetine: (a) inhibits (±)-epibatidine-induced Ca²⁺ influx in human (h) muscle embryonic (hα1β1γδ) and adult (hα1β1εδ) AChRs in a non-competitive manner and with potencies IC₅₀ = 3.86 ± 0.49 and 1.92 ± 0.48 μM, respectively, (b) binds to the [³H]TCP site with ∼13-fold higher affinity when the Torpedo AChR is in the desensitized state compared to the resting state, (c) enhances [³H]cytisine binding to the resting but activatableTorpedo AChR but not to the desensitized AChR, suggesting desensitizing properties, (d) overlaps the PCP luminal site located between rings 6′ and 13′ in the Torpedo but not human muscle AChRs. In silico mutation results indicate that ring 9′ is the minimum structural component for (−)-reboxetine binding, and (e) interacts to non-luminal sites located within the transmembrane segments from the Torpedo AChR γ subunit, and at the α1/ε transmembrane interface from the adult muscle AChR. In conclusion, (−)-reboxetine non-competitively inhibits muscle AChRs by binding to the TCP luminal site and by inducing receptor desensitization (maybe by interacting with non-luminal sites), a mechanism that is shared by tricyclic antidepressants.     

Classifying behavior from short‐interval biologging data: An example with GPS tracking of birds

1. 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.
2. 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.
3. 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.
4. 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.

     

Amyloid precursor protein regulates migration and metalloproteinase gene expression in prostate cancer cells

Amyloid precursor protein (APP) is a type I transmembrane protein, and one of its processed forms, β-amyloid, is considered to play a central role in the development of Alzheimer’s disease. We previously showed that APP is a primary androgen-responsive gene in prostate cancer and that its increased expression is correlated with poor prognosis for patients with prostate cancer. APP has also been implicated in several human malignancies. Nevertheless, the mechanism underlying the pro-proliferative effects of APP on cancers is still not well-understood. In the present study, we explored a pathophysiological role for APP in prostate cancer cells using siRNA targeting APP (siAPP). The proliferation and migration of LNCaP and DU145 prostate cancer cells were significantly suppressed by siAPP. Differentially expressed genes in siAPP-treated cells compared to control siRNA-treated cells were identified by microarray analysis. Notably, several metalloproteinase genes, such as ADAM10 and ADAM17, and epithelial–mesenchymal transition (EMT)-related genes, such as VIM, and SNAI2, were downregulated in siAPP-treated cells as compared to control cells. The expression of these genes was upregulated in LNCaP cells stably expressing APP when compared with control cells. APP-overexpressing LNCaP cells exhibited enhanced migration in comparison to control cells. These results suggest that APP may contribute to the proliferation and migration of prostate cancer cells by modulating the expression of metalloproteinase and EMT-related genes.     

Modelling habitat use and distribution of golden eagles <Emphasis Type="Italic">Aquila chrysaetos</Emphasis> in a low-density area of the Iberian Peninsula

We analyse the current situation of the Golden eagle (Aquila chrysaetos) in the region of Galicia in NW Spain. At present, the entire Galician population (five pairs) is located within an area of about 2000 km² in the province of Ourense. To identify high-priority areas for golden eagle conservation, we derived predictive models of habitat suitability using logistic regression and a Geographic Information System (GIS). Specifically, to model the distribution of the breeding population we considered topographic features, land use and degree of humanization, using a 10 × 10 km grid. Presence/absence of golden eagle nests was used as the dependent variable; analyses were performed both considering current nesting areas and considering old nesting areas (1960s and 70s). At the spatial scale considered, the best predictors of habitat suitability for breeding were topographical variables indicative of rugged relief. For current nesting areas the most parsimonious model included maximum altitude. We consider that the predictive models obtained may be of use for the monitoring and conservation management of the golden eagle population in this region. Conservation problems associated with habitat constraints such as food supply, availability of nesting sites, changes in land use and human disturbance are discussed.     

Finessing atlas data for species distribution models

Aim The spatial resolution of species atlases and therefore resulting model predictions are often too coarse for local applications. Collecting distribution data at a finer resolution for large numbers of species requires a comprehensive sampling effort, making it impractical and expensive. This study outlines the incorporation of existing knowledge into a conventional approach to predict the distribution of Bonelli’s eagle (Aquila fasciata) at a resolution 100 times finer than available atlas data. Location Malaga province, Andalusia, southern Spain. Methods A Bayesian expert system was proposed to utilize the knowledge from distribution models to yield the probability of a species being recorded at a finer resolution (1 × 1 km) than the original atlas data (10 × 10 km). The recorded probability was then used as a weight vector to generate a sampling scheme from the species atlas to enhance the accuracy of the modelling procedure. The maximum entropy for species distribution modelling (MaxEnt) was used as the species distribution model. A comparison was made between the results of the MaxEnt using the enhanced and, the random sampling scheme, based on four groups of environmental variables: topographic, climatic, biological and anthropogenic. Results The models with the sampling scheme enhanced by an expert system had a higher discriminative capacity than the baseline models. The downscaled (i.e. finer scale) species distribution maps using a hybrid MaxEnt/expert system approach were more specific to the nest locations and were more contrasted than those of the baseline model. Main conclusions The proposed method is a feasible substitute for comprehensive field work. The approach developed in this study is applicable for predicting the distribution of Bonelli’s eagle at a local scale from a national‐level occurrence data set; however, the usefulness of this approach may be limited to well‐known species.