Application of habitat thresholds in conservation: Considerations, limitations, and future directions

AbstractHabitat thresholds are often interpreted as the minimum required area of habitat, and subsequently promoted as conservation targets in natural resource policies and planning. Unfortunately, several recent reviews and messages of caution on the application of habitat thresholds in conservation have largely fallen on deaf ears, leading to a dangerous oversimplification and generalization of the concept. We highlight the prevalence of oversimplification/over-generalization of results from habitat threshold studies in policy documentation, the consequences of such over-generalization, and directions for habitat threshold studies that have conservation applications without risking overgeneralization. We argue that in order to steer away from misapplication of habitat thresholds in conservation, we should not focus on generalized nominal habitat values (i.e., amounts or percentages of habitat), but on the use of habitat threshold modeling for comparative exercises of area-sensitivity or the identification of environmental dangers. In addition, we should remain focused on understanding the processes and mechanisms underlying species responses to habitat change. Finally, studies could that focus on deriving nominal value threshold amounts should do so only if the thresholds are detailed, species-specific, and translated to conservation targets particular to the study area only

Opportunities and challenges for big data ornithology

Recent advancements in information technology and data acquisition have created both new research opportunities and new challenges for using big data in ornithology. We provide an overview of the past, present, and future of big data in ornithology, and explore the rewards and risks associated with their application. Structured data resources (e.g., North American Breeding Bird Survey) continue to play an important role in advancing our understanding of bird population ecology, and the recent advent of semistructured (e.g., eBird) and unstructured (e.g., weather surveillance radar) big data resources has promoted the development of new empirical perspectives that are generating novel insights. For example, big data have been used to study and model bird diversity and distributions across space and time, explore the patterns and determinants of broad-scale migration strategies, and examine the dynamics and mechanisms associated with geographic and phenological responses to global change. The application of big data also holds a number of challenges wherein high data volume and dimensionality can result in noise accumulation, spurious correlations, and incidental endogeneity. In total, big data resources continue to add empirical breadth and detail to ornithology, often at very broad spatial extents, but how the challenges underlying this approach can best be mitigated to maximize inferential quality and rigor needs to be carefully considered. Los avances recientes en la tecnolog´ıa de la informaci ´on y la adquisici ´on de datos han creado tanto nuevas oportunidades de investigaci ´on como desaf´ıos para el uso de datos masivos (big data) en ornitolog´ıa. Brindamos una visi ´on general del pasado, presente y futuro de los datos masivos en ornitolog´ıa y exploramos las recompensas y desaf´ıos asociados a su aplicaci ´ on. Los recursos de datos estructurados (e.g., Muestreo de Aves Reproductivas de Am´erica del Norte) siguen jugando un rol importante en el avance de nuestro entendimiento de la ecolog´ıa de poblaciones de las aves, y el advenimiento reciente de datos masivos semi-estructurados (e.g., eBird) y desestructurados (e.g., radar de vigilancia clima´tica) han promovido el desarrollo de nuevas perspectivas emp´ıricas que esta´n generando miradas novedosas. Por ejemplo, los datos masivos han sido usados para estudiar y modelar la diversidad y distribuci ´on de las aves a trav´es del tiempo y del espacio, explorar los patrones y los determinantes de las estrategias de migraci ´on a gran escala, y examinar las dina´micas y los mecanismos asociados con las respuestas geogra´ficas y fenol ´ ogicas al cambio global. La aplicaci ´on de datos masivos tambi´en contiene una serie de desaf´ıos donde el gran volumen de datos y la dimensionalidad pueden generar una acumulaci ´on de ruido, correlaciones espurias y endogeneidad incidental. En total, los recursos de datos masivos contin ´uan agregando amplitud y detalle emp´ırico a la ornitolog´ıa, usualmente a escalas espaciales muy amplias, pero necesita considerarse cuidadosamente c ´omo los desaf´ıos que subyacen este enfoque pueden ser mitigados del mejor modo para maximizar su calidad inferencial y rigor

The Psychology of Privacy in the Digital Age

Privacy is a psychological topic suffering from historical neglect – a neglect that is increasingly consequential in an era of social media connectedness, mass surveillance and the permanence of our electronic footprint. Despite fundamental changes in the privacy landscape, social and personality psychology journals remains largely unrepresented in debates on the future of privacy. By contrast, in disciplines like computer science and media and communication studies, engaging directly with socio- technical developments, interest in privacy has grown considerably. In our review of this interdisciplinary literature we suggest four domains of interest to psychologists. These are: sensitivity to individual differences in privacy disposition; a claim that privacy is fundamentally based in social interactions; a claim that privacy is inherently contextual; and a suggestion that privacy is as much about psychological groups as it is about individuals. Moreover, we propose a framework to enable progression to more integrative models of the psychology of privacy in the digital age, and in particular suggest that a group and social relations based approach to privacy is needed

OPN/CD44v6 overexpression in laryngeal dysplasia and correlation with clinical outcome

Laryngeal dysplasia is a common clinical concern. Despite major advancements, a significant number of patients with this condition progress to invasive squamous cell carcinoma. Osteopontin (OPN) is a secreted glycoprotein, whose expression is markedly elevated in several types of cancers. We explored OPN as a candidate biomarker for laryngeal dysplasia. To this aim, we examined OPN expression in 82 cases of dysplasia and in hyperplastic and normal tissue samples. OPN expression was elevated in all severe dysplasia samples, but not hyperplastic samples, with respect to matched normal mucosa. OPN expression levels correlated positively with degree of dysplasia (P=0.0094) and negatively with disease-free survival (P<0.0001). OPN expression was paralleled by cell surface reactivity for CD44v6, an OPN functional receptor. CD44v6 expression correlated negatively with disease-free survival, as well (P=0.0007). Taken as a whole, our finding identify OPN and CD44v6 as predictive markers of recurrence or aggressiveness in laryngeal intraepithelial neoplasia, and overall, point out an important signalling complex in the evolution of laryngeal dysplasia

Opportunities and challenges for big data ornithology

Recent advancements in information technology and data acquisition have created both new research opportunities and new challenges for using big data in ornithology. We provide an overview of the past, present, and future of big data in ornithology, and explore the rewards and risks associated with their application. Structured data resources (e.g., North American Breeding Bird Survey) continue to play an important role in advancing our understanding of bird population ecology, and the recent advent of semistructured (e.g., eBird) and unstructured (e.g., weather surveillance radar) big data resources has promoted the development of new empirical perspectives that are generating novel insights. For example, big data have been used to study and model bird diversity and distributions across space and time, explore the patterns and determinants of broad-scale migration strategies, and examine the dynamics and mechanisms associated with geographic and phenological responses to global change. The application of big data also holds a number of challenges wherein high data volume and dimensionality can result in noise accumulation, spurious correlations, and incidental endogeneity. In total, big data resources continue to add empirical breadth and detail to ornithology, often at very broad spatial extents, but how the challenges underlying this approach can best be mitigated to maximize inferential quality and rigor needs to be carefully considered. Los avances recientes en la tecnolog´ıa de la informaci ´on y la adquisici ´on de datos han creado tanto nuevas oportunidades de investigaci ´on como desaf´ıos para el uso de datos masivos (big data) en ornitolog´ıa. Brindamos una visi ´on general del pasado, presente y futuro de los datos masivos en ornitolog´ıa y exploramos las recompensas y desaf´ıos asociados a su aplicaci ´ on. Los recursos de datos estructurados (e.g., Muestreo de Aves Reproductivas de Am´erica del Norte) siguen jugando un rol importante en el avance de nuestro entendimiento de la ecolog´ıa de poblaciones de las aves, y el advenimiento reciente de datos masivos semi-estructurados (e.g., eBird) y desestructurados (e.g., radar de vigilancia clima´tica) han promovido el desarrollo de nuevas perspectivas emp´ıricas que esta´n generando miradas novedosas. Por ejemplo, los datos masivos han sido usados para estudiar y modelar la diversidad y distribuci ´on de las aves a trav´es del tiempo y del espacio, explorar los patrones y los determinantes de las estrategias de migraci ´on a gran escala, y examinar las dina´micas y los mecanismos asociados con las respuestas geogra´ficas y fenol ´ ogicas al cambio global. La aplicaci ´on de datos masivos tambi´en contiene una serie de desaf´ıos donde el gran volumen de datos y la dimensionalidad pueden generar una acumulaci ´on de ruido, correlaciones espurias y endogeneidad incidental. En total, los recursos de datos masivos contin ´uan agregando amplitud y detalle emp´ırico a la ornitolog´ıa, usualmente a escalas espaciales muy amplias, pero necesita considerarse cuidadosamente c ´omo los desaf´ıos que subyacen este enfoque pueden ser mitigados del mejor modo para maximizar su calidad inferencial y rigor

Emulsifier of Arthrobacter RAG-1: isolation and emulsifying properties.

The oil-degrading Arthrobacter sp. RAG-1 produced an extracellular nondialyzable emulsifying agent when grown on hexadecane, ethanol, or acetate medium. The emulsifier was prepared by two procedures: (i) heptane extraction of the cell-free culture medium and (ii) precipitation with ammonium sulfate. A convenient assay was developed for measurement of emulsifier concentrations between 3 and 75 micrograms/ml. The rate of emulsion fromation was proportional to both hydrocarbon and emulsifier concentrations. Above pH 6, activity was dependent upon divalent cations; half-maximum activity was obtained in the presence of 1.5 mM Mg2+. With a ratio of gas oil to emulsifier of 50, stable emulsions were formed with average droplet sizes of less than 1 micron. Emulsifier production was parallel to growth on either hydrocarbon or nonhydrocarbon substrates during the exponential phase; however, production continued after growth ceased

Emulsifier of Arthrobacter RAG-1: Chemical and Physical Properties

The extracellular emulsifier of Arthrobacter RAG-1 was deproteinized by hot phenol treatment and purified by fractional precipitation with (NH(4))(2)SO(4). The active fraction, precipitating between 30 and 35% saturation [EF-RAG(UET) WA], appeared to be homogeneous by immunodiffusion and sedimentation analysis. EF-RAG(UET) WA had an intrinsic viscosity of 750 cm(3)/g, a sedimentation constant of 6.06S, a diffusion constant of 5.25 × 10(−8) cm(2) s(−1), and a partial molar volume of 0.712 cm(3) g(−1). From these data a weight average molecular weight of 9.76 × 10(5) and a viscosity average molecular weight of 9.88 × 10(5) were calculated. EF-RAG(UET)WA contained 46.7% C, 7.01% H, and 6.06% N. Titration of the nonreducing polymer gave a single inflection point (pK′ = 3.05), corresponding to 1.5 μmol of carboxyl groups per mg. Direct estimation of O-ester and hexose content of the highly acidic polymer yielded 0.65 and 0.29 μmol/mg, respectively. Mild alkaline hydrolysis released fatty acids with an average molecular weight of about 231. Strong acid hydrolysis of EF-RAG(UET)WA yielded d-glucose (minor), d-galactosamine (major), and an unidentified amino uronic acid (major)