Roadblocks and the Law of Arrest in Montana

Roadblocks and the Law of Arrest in Montan

Rogers v. Richmond, 365 U.S. 534 (1961)

Rogers v. Richmon

Plath v. Hi-Ball Contractors, Inc., 362 P.2d 1021 (Mont. 1961)

Plath v. Hi-Ball Contractors, Inc

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Route Fidelity during Marine Megafauna Migration

The conservation and protection of marine megafauna require robust knowledge of where and when animals are located. Yet, our ability to predict animal distributions in space and time remains limited due to difficulties associated with studying elusive animals with large home ranges. The widespread deployment of satellite telemetry technology creates unprecedented opportunities to remotely monitor animal movements and to analyse the spatial and temporal trajectories of these movements from a variety of geophysical perspectives. Reproducible patterns in movement trajectories can help elucidate the potential mechanisms by which marine megafauna navigate across vast expanses of open-ocean. Here, we present an empirical analysis of humpback whale (Megaptera novaeangliae), great white shark (Carcharodon carcharias), and northern elephant seal (Mirounga angustirostris) satellite telemetry-derived route fidelity movements in magnetic and gravitational coordinates. Our analyses demonstrate that: (1) humpback whales, great white sharks and northern elephant seals are capable of performing route fidelity movements across millions of square kilometers of open ocean with a spatial accuracy of better than 150 km despite temporal separations as long as 7 years between individual movements; (2) route fidelity movements include significant (p < 0.05) periodicities that are comparable in duration to the lunar cycles and semi-cycles; (3) latitude and bedrock-dependent gravitational cues are stronger predictors of route fidelity movements than spherical magnetic coordinate cues when analyzed with respect to the temporally dependent moon illumination cycle. We further show that both route fidelity and non-route fidelity movement trajectories, for all three species, describe overlapping in-phase or antiphase sinusoids when individual movements are normalized to the gravitational acceleration present at migratory departure sites. Although these empirical results provide an inductive basis for the development of testable hypotheses and future research questions, they cannot be taken as evidence for causal relations between marine megafauna movement decisions and geophysical cues. Experiments on model organisms with known sensitivities to gravity and magnetism, complemented by further empirical observation of free-ranging animals, are required to fully explore how animals use discrete, or potentially integrated, geophysical cues for orientation and navigation purposes