Regional avian species declines estimated from volunteer-collected long-term data using List Length Analysis

Long-term systematic population monitoring data sets are rare but are essential in identifying changes in species abundance. In contrast, community groups and natural history organizations have collected many species lists. These represent a large, untapped source of information on changes in abundance but are generally considered of little value. The major problem with using species lists to detect population changes is that the amount of effort used to obtain the list is often uncontrolled and usually unknown. It has been suggested that using the number of species on the list, the "list length,'' can be a measure of effort. This paper significantly extends the utility of Franklin's approach using Bayesian logistic regression. We demonstrate the value of List Length Analysis to model changes in species prevalence (i.e., the proportion of lists on which the species occurs) using bird lists collected by a local bird club over 40 years around Brisbane, southeast Queensland, Australia. We estimate the magnitude and certainty of change for 269 bird species and calculate the probabilities that there have been declines and increases of given magnitudes

Optimal allocation of conservation effort among subpopulations of a threatened species: How important is patch quality?

Money is often a limiting factor in conservation, and attempting to conserve endangered species can be costly. Consequently, a framework for optimizing fiscally constrained conservation decisions for a single species is needed. In this paper we find the optimal budget allocation among isolated subpopulations of a threatened species to minimize local extinction probability. We solve the problem using stochastic dynamic programming, derive a useful and simple alternative guideline for allocating funds, and test its performance using forward simulation. The model considers subpopulations that persist in habitat patches of differing quality, which in our model is reflected in different relationships between money invested and extinction risk. We discover that, in most eases, subpopulations that are less efficient to manage should receive more money than those that are more efficient to manage, due to higher investment needed to reduce extinction risk. Our simple investment guideline performs almost as well as the exact optimal strategy. We illustrate our approach with a case study of the management of the Sumatran tiger, Panthera tigris sumatrae, in Kerinei Seblat National Park (KSNP), Indonesia. We find that different budgets should be allocated to the separate tiger subpopulations in KSNP. The subpopulation that is not at risk of extinction does not require any management investment. Based on the combination of risks of extinction and habitat quality, the optimal allocation for these particular tiger subpopulations is an unusual case: subpopulations that occur in higher-quality habitat (more efficient to manage) should receive more funds than the remaining subpopulation that is in lower-quality habitat. Because the yearly budget allocated to the KSNP for tiger conservation is small, to guarantee the persistence of all the subpopulations that are currently under threat we need to prioritize those that are easier to save. When allocating resources among subpopulations of a threatened species, the combined effects of differences in habitat quality, cost of action, and current subpopulation probability of extinction need to be integrated. We provide a useful guideline for allocating resources among isolated subpopulations of any threatened species

States insensitive to the Unruh effect in multi-level detectors

We give a general treatment of the spontaneous excitation rates and the non-relativistic Lamb shift of constantly accelerated multi-level atoms as a model for multi-level detectors. Using a covariant formulation of the dipole coupling between the atom and the electromagnetic field we show that new Raman-like transitions can be induced by the acceleration. Under certain conditions these transitions can lead to stable ground and excited states which are not affected by the non inertial motion. The magnitude of the Unruh effect is not altered by multi-level effects. Both the spontaneous excitation rates and the Lamb shift are not within the range of measurability.Comment: 9 Pages, late

Corner Exponents in the Two-Dimensional Potts Model

The critical behavior at a corner in two-dimensional Ising and three-state Potts models is studied numerically on the square lattice using transfer operator techniques. The local critical exponents for the magnetization and the energy density for various opening angles are deduced from finite-size scaling results at the critical point for isotropic or anisotropic couplings. The scaling dimensions compare quite well with the values expected from conformal invariance, provided the opening angle is replaced by an effective one in anisotropic systems.Comment: 11 pages, 2 eps-figures, uses LaTex and eps

Are Damage Spreading Transitions Generically in the Universality Class of Directed Percolation?

We present numerical evidence for the fact that the damage spreading transition in the Domany-Kinzel automaton found by Martins {\it et al.} is in the same universality class as directed percolation. We conjecture that also other damage spreading transitions should be in this universality class, unless they coincide with other transitions (as in the Ising model with Glauber dynamics) and provided the probability for a locally damaged state to become healed is not zero.Comment: 10 pages, LATE

The beginning of time? Evidence for catastrophic drought in Baringo in the early nineteenth century

New developments in the collection of palaeo-data over the past two decades have transformed our understanding of climate and environmental history in eastern Africa. This article utilises instrumental and proxy evidence of historical lake-level fluctuations from Baringo and Bogoria, along with other Rift Valley lakes, to document the timing and magnitude of hydroclimate variability at decadal to century time scales since 1750. These data allow us to construct a record of past climate variation not only for the Baringo basin proper, but also across a sizable portion of central and northern Kenya. This record is then set alongside historical evidence, from oral histories gathered amongst the peoples of northern Kenya and the Rift Valley and from contemporary observations recorded by travellers through the region, to offer a reinterpretation of human activity and its relationship to environmental history in the nineteenth century. The results reveal strong evidence of a catastrophic drought in the early nineteenth century, the effects of which radically alters our historical understanding of the character of settlement, mobility and identity within the Baringo–Bogoria basin

Mast cells modulate the pathogenesis of elastase-induced abdominal aortic aneurysms in mice

Abdominal aortic aneurysm (AAA), an inflammatory disease, involves leukocyte recruitment, immune responses, inflammatory cytokine production, vascular remodeling, neovascularization, and vascular cell apoptosis, all of which contribute to aortic dilatation. This study demonstrates that mast cells, key participants in human allergic immunity, participate in AAA pathogenesis in mice. Mast cells were found to accumulate in murine AAA lesions. Mast cell–deficient KitW-sh/KitW-sh mice failed to develop AAA elicited by elastase perfusion or periaortic chemical injury. KitW-sh/KitW-sh mice had reduced aortic expansion and internal elastic lamina degradation; decreased numbers of macrophages, CD3+ T lymphocytes, SMCs, apoptotic cells, and CD31+ microvessels; and decreased levels of aortic tissue IL-6 and IFN-γ. Activation of mast cells in WT mice via C48/80 injection resulted in enhanced AAA growth while mast cell stabilization with disodium cromoglycate diminished AAA formation. Mechanistic studies demonstrated that mast cells participated in angiogenesis, aortic SMC apoptosis, and matrix-degrading protease expression. Reconstitution of KitW-sh/KitW-sh mice with bone marrow–derived mast cells from WT or TNF-α–/– mice, but not from IL-6–/– or IFN-γ–/– mice, caused susceptibility to AAA formation to be regained. These results demonstrate that mast cells participate in AAA pathogenesis in mice by releasing proinflammatory cytokines IL-6 and IFN-γ, which may induce aortic SMC apoptosis, matrix-degrading protease expression, and vascular wall remodeling, important hallmarks of arterial aneurysms

Quantum quenches in the anisotropic spin-1/2 Heisenberg chain: different approaches to many-body dynamics far from equilibrium

Recent experimental achievements in controlling ultracold gases in optical lattices open a new perspective on quantum many-body physics. In these experimental setups it is possible to study coherent time evolution of isolated quantum systems. These dynamics reveal new physics beyond the low-energy properties usually relevant in solid-state many-body systems. In this paper we study the time evolution of antiferromagnetic order in the Heisenberg chain after a sudden change of the anisotropy parameter, using various numerical and analytical methods. As a generic result we find that the order parameter, which can show oscillatory or non-oscillatory dynamics, decays exponentially except for the effectively non-interacting case of the XX limit. For weakly ordered initial states we also find evidence for an algebraic correction to the exponential law. The study is based on numerical simulations using a numerical matrix product method for infinite system sizes (iMPS), for which we provide a detailed description and an error analysis. Additionally, we investigate in detail the exactly solvable XX limit. These results are compared to approximative analytical approaches including an effective description by the XZ-model as well as by mean-field, Luttinger-liquid and sine-Gordon theories. This reveals which aspects of non-equilibrium dynamics can as in equilibrium be described by low-energy theories and which are the novel phenomena specific to quantum quench dynamics. The relevance of the energetically high part of the spectrum is illustrated by means of a full numerical diagonalization of the Hamiltonian.Comment: 28 page

Integration of Machine Learning and Mechanistic Models Accurately Predicts Variation in Cell Density of Glioblastoma Using Multiparametric MRI

Glioblastoma (GBM) is a heterogeneous and lethal brain cancer. These tumors are followed using magnetic resonance imaging (MRI), which is unable to precisely identify tumor cell invasion, impairing effective surgery and radiation planning. We present a novel hybrid model, based on multiparametric intensities, which combines machine learning (ML) with a mechanistic model of tumor growth to provide spatially resolved tumor cell density predictions. The ML component is an imaging data-driven graph-based semi-supervised learning model and we use the Proliferation-Invasion (PI) mechanistic tumor growth model. We thus refer to the hybrid model as the ML-PI model. The hybrid model was trained using 82 image-localized biopsies from 18 primary GBM patients with pre-operative MRI using a leave-one-patient-out cross validation framework. A Relief algorithm was developed to quantify relative contributions from the data sources. The ML-PI model statistically significantly outperformed (p \u3c 0.001) both individual models, ML and PI, achieving a mean absolute predicted error (MAPE) of 0.106 ± 0.125 versus 0.199 ± 0.186 (ML) and 0.227 ± 0.215 (PI), respectively. Associated Pearson correlation coefficients for ML-PI, ML, and PI were 0.838, 0.518, and 0.437, respectively. The Relief algorithm showed the PI model had the greatest contribution to the result, emphasizing the importance of the hybrid model in achieving the high accuracy