YbPd2In : A promising candidate for strong entropy accumulation at very low temperature

We report on synthesis, crystal structure, magnetic, thermodynamic, and transport properties of the compound YbPd2In, crystallizing as a Heusler structure type. A trivalent state of the rare earth was determined by fitting the magnetic susceptibility with a Curie-Weiss law. This compound is characterized by showing very weak magnetic interactions and a negligible Kondo effect. A specific-heat jump was observed at T 48250mK, followed at higher temperature by a power-law decrease of CP(T)/T. The resulting large electronic entropy increase at very low temperature is rapidly shifted to higher temperature by the application of magnetic field. This magnetocaloric effect places YbPd2In as a very good candidate for adiabatic demagnetization cooling processes

Ground state and stability of the fractional plateau phase in metallic Shastry Sutherland system TmB4

We present a study of the ground state and stability of the fractional plateau phase FPP with M Msat 1 8 in the metallic Shastry Sutherland system TmB4. Magnetization M measurements show that the FPP states are thermodynamically stable when the sample is cooled in constant magnetic field from the paramagnetic phase to the ordered one at 2 K. On the other hand, after zero field cooling and subsequent magnetization these states appear to be of dynamic origin. In this case the FPP states are closely associated with the half plateau phase HPP, M Msat , mediate the HPP to the low field antiferromagnetic AF phase and depend on the thermodynamic history. Thus, in the same place of the phase diagram both, the stable and the metastable dynamic fractional plateau FP states, can be observed, depending on the way they are reached. In case of metastable FP states thermodynamic paths are identified that lead to very flat fractional plateaus in the FPP. Moreover, with a further decrease of magnetic field also the low field AF phase becomes influenced and exhibits a plateau of the order of 1 1000 Msa

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

Amazonia Camtrap: a data set of mammal, bird, and reptile species recorded with camera traps in the Amazon forest.

Abstract : The Amazon forest has the highest biodiversity on Earth. However, information on Amazonian vertebrate diversity is still deficient and scatteredacross the published, peer-reviewed, and gray literature and in unpublishedraw data. Camera traps are an effective non-invasive method of surveying vertebrates, applicable to different scales of time and space. In this study, we organized and standardized camera trap records from different Amazonregions to compile the most extensive data set of inventories of mammal,bird, and reptile species ever assembled for the area. The complete data setcomprises 154,123 records of 317 species (185 birds, 119 mammals, and13 reptiles) gathered from surveys from the Amazonian portion of eightcountries (Brazil, Bolivia, Colombia, Ecuador, French Guiana, Peru,Suriname, and Venezuela). The most frequently recorded species per taxawere: mammals:Cuniculus paca (11,907 records); birds: Pauxi tuberosa (3713 records); and reptiles:Tupinambis teguixin(716 records). The infor-mation detailed in this data paper opens up opportunities for new ecological studies at different spatial and temporal scales, allowing for a moreaccurate evaluation of the effects of habitat loss, fragmentation, climatechange, and other human-mediated defaunation processes in one of themost important and threatened tropical environments in the world. The data set is not copyright restricted; please cite this data paper when usingits data in publications and we also request that researchers and educator sinform us of how they are using these data

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications 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, 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

Rotating magnetocaloric effect in TmB 4 A comparison between estimations based on heat capacity and magnetization measurements

We present a comparison of the rotating magnetocaloric effect R MCE carried out on TmB4, a strongly anisotropic magnetic system, which is based on the determination of the entropy S and entropy change amp; 916;S. Both quantities are determined using independent measurements of the heat capacity and the magnetization as a function of temperature and magnetic field. The comparison of these two approaches shows that estimates of the R MCE, in particular the estimate of the temperature difference amp; 916;T that occurs during sample rotation in magnetic field H, based on magnetization measurements which usually present a simpler and faster way to obtain the necessary data provide similar results to those obtained from detailed temperature dependencies of heat capacity. However, to take the advantage of magnetisation measurements it is necessary to make an approximation concerning heat capacity data. There one has to be careful and use the right approximation for the amp; 916;T estimate. In case of materials with a complex temperature dependence of the heat capacity common approach can lead to significant errors. Our results here suggest for the entropy calculation from magnetic data sets the following procedure first determine the zero field H amp; 8239; amp; 8239;0 contribution to S from heat capacity data, then add to this the magnetic entropy change contribution determined from magnetization measurement