Iterative model predictions for wildlife populations impacted by rapid climate change

To improve understanding and management of the consequences of current rapid environmental change, ecologists advocate using long-term monitoring data series to generate iterative near-term predictions of ecosystem responses. This approach allows scientific evidence to increase rapidly and management strategies to be tailored simultaneously. Iterative near-term forecasting may therefore be particularly useful for adaptive monitoring of ecosystems subjected to rapid climate change. Here, we show how to implement near-term forecasting in the case of a harvested population of rock ptarmigan in high-arctic Svalbard, a region subjected to the largest and most rapid climate change on Earth. We fitted state-space models to ptarmigan counts from point transect distance sampling during 2005–2019 and developed two types of predictions: (1) explanatory predictions to quantify the effect of potential drivers of ptarmigan population dynamics, and (2) anticipatory predictions to assess the ability of candidate models of increasing complexity to forecast next-year population density. Based on the explanatory predictions, we found that a recent increasing trend in the Svalbard rock ptarmigan population can be attributed to major changes in winter climate. Currently, a strong positive effect of increasing average winter temperature on ptarmigan population growth outweighs the negative impacts of other manifestations of climate change such as rain-on-snow events. Moreover, the ptarmigan population may compensate for current harvest levels. Based on the anticipatory predictions, the near-term forecasting ability of the models improved nonlinearly with the length of the time series, but yielded good forecasts even based on a short time series. The inclusion of ecological predictors improved forecasts of sharp changes in next-year population density, demonstrating the value of ecosystem-based monitoring. Overall, our study illustrates the power of integrating near-term forecasting in monitoring systems to aid understanding and management of wildlife populations exposed to rapid climate change. We provide recommendations for how to improve this approach

A review of the scientific knowledge of the seascape off Dronning Maud Land, Antarctica

Despite the exclusion of the Southern Ocean from assessments of progress towards achieving the Convention on Biological Diversity (CBD) Strategic Plan, the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR) has taken on the mantle of progressing efforts to achieve it. Within the CBD, Aichi Target 11 represents an agreed commitment to protect 10% of the global coastal and marine environment. Adopting an ethos of presenting the best available scientific evidence to support policy makers, CCAMLR has progressed this by designating two Marine Protected Areas in the Southern Ocean, with three others under consideration. The region of Antarctica known as Dronning Maud Land (DML; 20 degrees W to 40 degrees E) and the Atlantic sector of the Southern Ocean that abuts it conveniently spans one region under consideration for spatial protection. To facilitate both an open and transparent process to provide the vest available scientific evidence for policy makers to formulate management options, we review the body of physical, geochemical and biological knowledge of the marine environment of this region. The level of scientific knowledge throughout the seascape abutting DML is polarized, with a clear lack of data in its eastern part which is presumably related to differing levels of research effort dedicated by national Antarctic programmes in the region. The lack of basic data on fundamental aspects of the physical, geological and biological nature of eastern DML make predictions of future trends difficult to impossible, with implications for the provision of management advice including spatial management. Finally, by highlighting key knowledge gaps across the scientific disciplines our review also serves to provide guidance to future research across this important region.Peer reviewe

A review of the scientific knowledge of the seascape off Dronning Maud Land, Antarctica

Despite the exclusion of the Southern Ocean from assessments of progress towards achieving the Convention on Biological Diversity (CBD) Strategic Plan, the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR) has taken on the mantle of progressing efforts to achieve it. Within the CBD, Aichi Target 11 represents an agreed commitment to protect 10% of the global coastal and marine environment. Adopting an ethos of presenting the best available scientific evidence to support policy makers, CCAMLR has progressed this by designating two Marine Protected Areas in the Southern Ocean, with three others under consideration. The region of Antarctica known as Dronning Maud Land (DML; 20°W to 40°E) and the Atlantic sector of the Southern Ocean that abuts it conveniently spans one region under consideration for spatial protection. To facilitate both an open and transparent process to provide the vest available scientific evidence for policy makers to formulate management options, we review the body of physical, geochemical and biological knowledge of the marine environment of this region. The level of scientific knowledge throughout the seascape abutting DML is polarized, with a clear lack of data in its eastern part which is presumably related to differing levels of research effort dedicated by national Antarctic programmes in the region. The lack of basic data on fundamental aspects of the physical, geological and biological nature of eastern DML make predictions of future trends difficult to impossible, with implications for the provision of management advice including spatial management. Finally, by highlighting key knowledge gaps across the scientific disciplines our review also serves to provide guidance to future research across this important region.publishedVersio

First results from the L3+C experiment at CERN

The L3+C experiment combines the high-precision spectrometer of the L3 detector at LEP, CERN, with a small air shower array. The momenta of cosmic ray induced muons can be measured from 20 to 2000 GeV/c. During the 1999 data taking period 5 billion muon events were recorded in the spectrometer. From April until mid Summer 2000 an additional 3 billion muon events have been recorded as well as 25 million air shower events. Here the first results on the muon momentum spectrum and charge ratio will be presented

Investigations of the Potential Application of k-out-of-n Systems in Oil and Gas Industry Objects

The purpose of this paper was to demonstrate the possibilities of assessing the reliability of oil and gas industry structures with the help of mathematical models of k-out-of-n systems. We show how the reliability of various structures in the oil and gas complex can be described and investigated using k-out-of-n models. Because the initial information about the life and repair time of components of systems is only usually known on the scale of one and/or two moments, we focus on the problem of the sensitivity analysis of the system reliability indices to the shape of its components repair time distributions. To address this problem, we used the so-called markovization method, based on the introduction of supplementary variables, to model the system behavior with the help of the two-dimensional Markov process with discrete-continuous states. On the basis of the forward Kolmogorov equations for the time-dependent process’ state probabilities, relevant balance equations for the process’ stationary probabilities are presented. Using these equations, stationary probabilities and some reliability indices for two examples from the oil and gas industry were calculated and their sensitivity to the system component’s repair time distributions was analyzed. Calculations show that under “rare” component failures, most system reliability indices become practically insensitive to the shape of the components repair time distributions

PyTroll : An Open-Source, Community-Driven Python Framework to Process Earth Observation Satellite Data

AbstractPyTroll (http://pytroll.org) is a suite of open-source easy-to-use Python packages to facilitate processing and efficient sharing of Earth Observation (EO) satellite data. The PyTroll software is intended for both 24/7 real-time operations as well as research and development. PyTroll grew out of the need to provide a resilient and agile platform that can respond quickly to new user needs and new data sources. PyTroll, being open source, stimulates international collaboration, which is vital with the rapid increase of satellite information availability. The PyTroll software development is strongly user driven and has grown over the past eight years from a collaborative effort between the Danish and Swedish national meteorological services to encompass a worldwide community with active contributors. PyTroll is being used at least operationally in the national meteorological services of Denmark, Norway, Sweden, Finland, Germany, Switzerland, Italy, Estonia, and Latvia. However, given its simplicity, minimal demand on user resources, and community-driven approach, it also encourages and facilitates usage of EO data for individual applications. While PyTroll was originally developed to cater to the needs of the atmospheric remote sensing community, it could be equally useful for land and ocean applications and within hydrology. This article provides an overview of PyTroll, with examples showing the capability of some of the core packages.</jats:p

pyproj4/pyproj: 3.6.1 Release

What's Changed WHL: Update to PROJ 9.3 by @snowman2 in https://github.com/pyproj4/pyproj/pull/1334 DEP: Add Python 3.12 support by @snowman2 in https://github.com/pyproj4/pyproj/pull/1341 BUG: Cython 3 compatibility fixes by @snowman2 in https://github.com/pyproj4/pyproj/pull/1322 BUG: Remove pkg_resources from setup.py by @snowman2 in https://github.com/pyproj4/pyproj/pull/1314 DOC: Fixed typos by @djm93dev in https://github.com/pyproj4/pyproj/pull/1305 & https://github.com/pyproj4/pyproj/pull/1306 DOC: Fix logo view on Pypi by @cyschneck in https://github.com/pyproj4/pyproj/pull/1308 DOC: Spelling permimeter -> perimeter by @zanejgr in https://github.com/pyproj4/pyproj/pull/1310 New Contributors @djm93dev made their first contribution in https://github.com/pyproj4/pyproj/pull/1305 @cyschneck made their first contribution in https://github.com/pyproj4/pyproj/pull/1308 @zanejgr made their first contribution in https://github.com/pyproj4/pyproj/pull/1310 Other contributions: @sebastic - testing Debian builds @jdkloe - testing Fedora builds Full Changelog: https://github.com/pyproj4/pyproj/compare/3.6.0...3.6.

pyproj4/pyproj: 3.6.1rc0

Python interface to PROJ (cartographic projections and coordinate transformations library