Long‐term Effects of Land‐Use Change on Bird Communities Depend on Spatial Scale and Land‐Use Type

Land‐use transformation is one of the most important and pervasive ecological changes occurring across the Earth, but its long‐term effects are poorly understood. Here, we analyze the effects of urban and agriculture development on bird biodiversity and community structure over a 16‐yr study period. We found that long‐term effects of land‐use change are dependent on spatial scale and land‐use type. At the regional scale, we found that gamma diversity (total number of species observed) declined by ~10% over time. At the landscape spatial scale, we found that beta diversity (uniqueness of bird communities) increased by ~16% over time. Additionally, the average contributions of urban riparian bird communities to beta diversity were generally the highest but declined by ~26% over the study period. Contributions of urban communities to beta diversity were generally the lowest but increased by ~10% over time. At the local scale, we observed different responses for different measures of alpha diversity. For bird species richness, temporal changes varied by land use. Species richness declined 16% at sites in desert riparian areas but increased by 21% and 12% at sites in urban and agricultural areas, respectively. Species evenness declined across all land uses, with some land uses experiencing more rapid declines than others. Our analysis of species groups that shared certain traits suggests that these community‐level changes were driven by species that are small, breed onsite, and feed on insects, grains, and nectar. Collectively, our results suggest that biodiversity declines associated with land‐use change predominate at the regional and local spatial scale, and that these effects can strengthen or weaken over time. However, these changes counterintuitively led to increases in biodiversity at the landscape scale, as bird communities became more unique. This has implications for conservation and management as it shows that the effects of land‐use modification on biodiversity may be positive or negative depending on the spatial scale considered.Funding was provided by the National Science Foundation Long‐Term Ecological Research Program (DEB‐9714833, DEB‐0423704, DEB‐1026865, DEB‐1637590, and DEB‐1832016). Open Access fees paid for in whole or in part by the University of Oklahoma Libraries.Ye

Bistability, multistability and nonreciprocal light propagation in Thue-Morse multilayered structures

The nonlinear properties of quasiperiodic photonic crystals based on the Thue-Morse sequence are investigated. The intrinsic spatial asymmetry of these one-dimensional structures for odd generation numbers results in bistability thresholds which are sensitive to the propagation direction. Along with resonances of perfect transmission, this feature allows to achieve strongly non-reciprocal propagation and to create an all-optical diode. The salient qualitative features of such optical diode action is readily explained through a simple coupled resonator model. The efficiency of a passive scheme, which does not necessitate of an additional short pump signal, is compared to an active scheme, where such a signal is required.Comment: 18 pages, 8 figure

Evaluation of Bacteria and Fungi DNA Abundance in Human Tissues

Whereas targeted and shotgun sequencing approaches are both powerful in allowing the study of tissue-associated microbiota, the human: microorganism abundance ratios in tissues of interest will ultimately determine the most suitable sequencing approach. In addition, it is possible that the knowledge of the relative abundance of bacteria and fungi during a treatment course or in pathological conditions can be relevant in many medical conditions. Here, we present a qPCR-targeted approach to determine the absolute and relative amounts of bacteria and fungi and demonstrate their relative DNA abundance in nine different human tissue types for a total of 87 samples. In these tissues, fungi genomes are more abundant in stool and skin samples but have much lower levels in other tissues. Bacteria genomes prevail in stool, skin, oral swabs, saliva, and gastric fluids. These findings were confirmed by shotgun sequencing for stool and gastric fluids. This approach may contribute to a more comprehensive view of the human microbiota in targeted studies for assessing the abundance levels of microorganisms during disease treatment/progression and to indicate the most informative methods for studying microbial composition (shotgun versus targeted sequencing) for various samples types

Classification of schizophrenic traits in transcriptions of audio spectra from patient literature: artificial intelligence models enhanced by geometric properties

Schizophrenia is a severe mental illness that affects approximately 1% of the global population and presents significant challenges for patients, families, and healthcare professionals. Characterized by symptoms such as delusions, hallucinations, disorganized speech or behavior, and cognitive impairment, this condition has an early onset and chronic trajectory, making it a debilitating challenge. Schizophrenia also imposes a substantial burden on society, exacerbated by the stigma associated with mental disorders. Technological advancements, such as computerized semantic, linguistic, and acoustic analyses, are revolutionizing the understanding and assessment of communication alterations, a significant aspect in various severe mental illnesses. Early and accurate diagnosis is crucial for improving prognosis and implementing appropriate treatments. In this context, the advancement of Artificial Intelligence (AI) has provided new perspectives for the treatment of schizophrenia, with machine learning techniques and natural language processing allowing a more detailed analysis of clinical, neurological, and behavioral data sets. The present article aims to present a proposal for computational models for the identification of schizophrenic traits in texts.  The database used in this article was created with 139 excerpts of patients' speeches reported in the book “Memories of My Nervous Disease” by German judge Daniel Paul Schreber, classifying them into three categories: 1 - schizophrenic, 2 - with schizophrenic traits and 3 - without any relation to the disorder. Of these speeches, 104 were used for training the models and the others 35 for validation.Three classification models were implemented using features based on geometric properties of graphs (number of vertices, number of cycles, girth, vertex of maximum degree, maximum clique size) and text entropy. Promising results were observed in the classification, with the Decision Tree-based model [1] achieving 100% accuracy, the KNN- k-Nearest Neighbor model observed with 62.8% accuracy, and the 'centrality-based' model with 59% precision. The high precision rates, observed when geometric properties are incorporated into Artificial Intelligence Models, suggest that the models can be improved to the point of capturing the language deviation traits that are indicative of schizophrenic disorders. In summary, this study paves the way for significant advances in the use of geometric properties in the field of psychiatry, offering a new data-based approach to the understanding and therapy of schizophrenia

Coefficient shifts in geographical ecology: an empirical evaluation of spatial and non-spatial regression

Copyright © 2009 The Authors. Copyright © ECOGRAPHY 2009.A major focus of geographical ecology and macro ecology is to understand the causes of spatially structured ecological patterns. However, achieving this understanding can be complicated when using multiple regressions, because the relative importance of explanatory variables, as measured by regression coefficients, can shift depending on whether spatially explicit or non-spatial modelling is used. However, the extent to which coefficients may shift and why shifts occur are unclear. Here, we analyze the relationship between environmental predictors and the geographical distribution of species richness, body size, range size and abundance in 97 multi-factorial data sets. Our goal was to compare standardized partial regression coefficients of non-spatial ordinary least squares regressions (i.e. models fitted using ordinary least squares without taking autocorrelation into account; “OLS models” hereafter) and eight spatial methods to evaluate the frequency of coefficient shifts and identify characteristics of data that might predict when shifts are likely. We generated three metrics of coefficient shifts and eight characteristics of the data sets as predictors of shifts. Typical of ecological data, spatial autocorrelation in the residuals of OLS models was found in most data sets. The spatial models varied in the extent to which they minimized residual spatial autocorrelation. Patterns of coefficient shifts also varied among methods and datasets, although the magnitudes of shifts tended to be small in all cases. We were unable to identify strong predictors of shifts, including the levels of autocorrelation in either explanatory variables or model residuals. Thus, changes in coefficients between spatial and non-spatial methods depend on the method used and are largely idiosyncratic, making it difficult to predict when or why shifts occur. We conclude that the ecological importance of regression coefficients cannot be evaluated with confidence irrespective of whether spatially explicit modelling is used or not. Researchers may have little choice but to be more explicit about the uncertainty of models and more cautious in their interpretation

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