scholarly journals Positive Interactions Drive Bat Distribution in a Remote Oceanic Archipelago (Azores, Portugal)

Diversity ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 17
Author(s):  
Ana Rainho
Keyword(s):  
Species Distribution ◽  
Essential Role ◽  
Linear Models ◽  
Positive Interactions ◽  
Positive Interaction ◽  
Generalised Linear Models ◽  
Protection Measures ◽  
Azores Archipelago ◽  
Occurrence Pattern ◽  
Occurrence Patterns

One of the fundamental interests in ecology is understanding which factors drive species’ distribution. We aimed to understand the drivers of bat distribution and co-occurrence patterns in a remote, insular system. The two bat species known to occur in the Azores archipelago were used as a model. Echolocation calls were recorded at 414 point-locations haphazardly distributed across the archipelago. Calls were analysed and assigned to each species. Binominal generalised linear models were adjusted using different descriptors at two scales: archipelago and island. The presence of the co-occurring species was included at both scales. The results show that island isolation, habitat and climate play an essential role on the archipelago and island scales, respectively. However, the positive interaction between bat species was the most critical driver of species’ distribution at the island scale. This high co-occurrence pattern at the island scale may result from both species’ maximising foraging profit in a region where prey abundance may be highly variable. However, further research is necessary to clarify the mechanisms behind this positive interaction. Both species are threatened and lack specific management and protection measures. Maintaining this positive interaction between the two species may prove to be fundamental for their conservation.

Nonnull asymptotic distributions of three classic criteria in generalised linear models

Biometrika ◽  
1994 ◽  
Vol 81 (4) ◽  
pp. 709-720 ◽  
Author(s):  
GAUSS M. CORDEIRO ◽  
DENISE A. BOTTER ◽  
SILVIA L. DE PAULA FERRARI
Keyword(s):  
Linear Models ◽  
Asymptotic Distributions ◽  
Generalised Linear Models

scholarly journals A Social Recommendation Based on Metric Learning and Users’ Co-Occurrence Pattern

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2158
Author(s):  
Xin Zhang ◽  
Jiwei Qin ◽  
Jiong Zheng
Keyword(s):  
Matrix Factorization ◽  
Triangle Inequality ◽  
Metric Learning ◽  
Social Recommendation ◽  
Rating Data ◽  
Fine Grained ◽  
Information Metric ◽  
Occurrence Pattern ◽  
Public Datasets ◽  
Occurrence Patterns

For personalized recommender systems, matrix factorization and its variants have become mainstream in collaborative filtering. However, the dot product in matrix factorization does not satisfy the triangle inequality and therefore fails to capture fine-grained information. Metric learning-based models have been shown to be better at capturing fine-grained information than matrix factorization. Nevertheless, most of these models only focus on rating data and social information, which are not sufficient for dealing with the challenges of data sparsity. In this paper, we propose a metric learning-based social recommendation model called SRMC. SRMC exploits users’ co-occurrence patterns to discover their potentially similar or dissimilar users with symmetric relationships and change their relative positions to achieve better recommendations. Experiments on three public datasets show that our model is more effective than the compared models.

scholarly journals Inferring viral occurrence patterns through a synthetic data simulation

2021 ◽  
Author(s):  
Ville N Pimenoff ◽  
Ramon Cleries
Keyword(s):  
Linear Models ◽  
Population Sample ◽  
Synthetic Data ◽  
Interaction Patterns ◽  
Viral Strain ◽  
Data Simulation ◽  
Synthetic Datasets ◽  
Pathogen Occurrence ◽  
Log Linear ◽  
Occurrence Patterns

Viruses infecting humans are manifold and several of them provoke significant morbidity and mortality. Simulations creating large synthetic datasets from observed multiple viral strain infections in a limited population sample can be a powerful tool to infer significant pathogen occurrence and interaction patterns, particularly if limited number of observed data units is available. Here, to demonstrate diverse human papillomavirus (HPV) strain occurrence patterns, we used log-linear models combined with Bayesian framework for graphical independence network (GIN) analysis. That is, to simulate datasets based on modeling the probabilistic associations between observed viral data points, i.e different viral strain infections in a set of population samples. Our GIN analysis outperformed in precision all oversampling methods tested for simulating large synthetic viral strain-level prevalence dataset from observed set of HPVs data. Altogether, we demonstrate that network modeling is a potent tool for creating synthetic viral datasets for comprehensive pathogen occurrence and interaction pattern estimations.

scholarly journals The geographic scaling of biotic interactions

2013 ◽  
Author(s):  
Miguel B Araújo ◽  
Alejandro Rozenfeld
Keyword(s):  
Biotic Interactions ◽  
Species Distributions ◽  
Scale Dependence ◽  
Positive Interactions ◽  
Ample Evidence ◽  
Species Ranges ◽  
Central Tenet ◽  
Resource Interactions ◽  
Occurrence Patterns ◽  
Consumer Resource

A central tenet of ecology and biogeography is that the broad outlines of species ranges are determined by climate, whereas the effects of biotic interactions are manifested at local scales. While the first proposition is supported by ample evidence, the second is still a matter of controversy. To address this question, we develop a mathematical model that predicts the spatial overlap, i.e., co-occurrence, between pairs of species subject to all possible types of interactions. We then identify the scale in which predicted range overlaps are lost. We found that co-occurrence arising from positive interactions, such as mutualism (+/+) and commensalism (+/0), are manifested across scales of resolution. Negative interactions, such as competition (-/-) and amensalism (-/0), generate checkerboard-type co-occurrence patterns that are discernible at finer resolutions. Scale dependence in consumer-resource interactions (+/-) depends on the strength of positive dependencies between species. Our results challenge the widely held view that climate alone is sufficient to characterize species distributions at broad scales, but also demonstrate that the spatial signature of competition is unlikely to be discernible beyond local and regional scales.

Second order asymptotics for score tests in generalised linear models

Biometrika ◽  
1995 ◽  
Vol 82 (2) ◽  
pp. 426-432 ◽  
Author(s):  
FRANCISCO CRIBARI-NETO ◽  
SILVIA L. P. FERRARI
Keyword(s):  
Linear Models ◽  
Second Order ◽  
Generalised Linear Models ◽  
Score Tests ◽  
Second Order Asymptotics

Intensification of future low-flow events in relation to projected changes in large-scale climate drivers due to climate change

2021 ◽  
Author(s):  
Pallavi Goswami ◽  
Arpita Mondal ◽  
Christoph Rüdiger ◽  
Tim J. Peterson
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Linear Models ◽  
Southern Oscillation ◽  
Value Theory ◽  
Extreme Value ◽  
Low Flow ◽  
Generalised Linear Models ◽  
Southeast Australia ◽  
The Future

<p>Large-scale climate processes such as the El Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Southern Annular Mode (SAM) influence the hydro-climatology of Southeast Australia (SEA). In the present study, we show that low-flow events in many catchments in SEA are significantly influenced by variability in these climate drivers. Extreme value distributions and Generalised Linear Models (GLMs) are used here to model low-flow characteristics such as intensity, duration and frequency with respect to these climate drivers. Further, we study how the future projections of ENSO, IOD and SAM are likely to evolve under climate change by examining the projected values of their representative indices and how they will impact low-flow events in the region. It is found that the future dry phases of these climate drivers are likely to be more dry than those in the historic period. This in turn is expected to lead to intensification of low-flow events in the future, resulting in lower availability of fresh water during occurrences of the dry phases of these climate drivers. Thus, climate change in the future is expected to significantly influence future low-flow events in the region thereby making it even more crucial for water managers to adequately manage and ensure water availability.</p><p><br>Keywords: low-flows, ENSO, IOD, SAM, Extreme Value Theory, Generalised Linear Models, Southeast Australia, CMIP5, RCP8.5.</p>

Integral projection models

2021 ◽  
pp. 181-196
Author(s):  
Edgar J. González ◽  
Dylan Z. Childs ◽  
Pedro F. Quintana-Ascencio ◽  
Roberto Salguero-Gómez
Keyword(s):  
Population Dynamics ◽  
Environmental Variables ◽  
Linear Models ◽  
Vital Rates ◽  
Matrix Population Models ◽  
Generalised Linear Models ◽  
Integral Projection Models ◽  
Integral Projection ◽  
Over Time

Integral projection models (IPMs) allow projecting the behaviour of a population over time using information on the vital processes of individuals, their state, and that of the environment they inhabit. As with matrix population models (MPMs), time is treated as a discrete variable, but in IPMs, state and environmental variables are continuous and are related to the vital rates via generalised linear models. Vital rates in turn integrate into the population dynamics in a mechanistic way. This chapter provides a brief description of the logic behind IPMs and their construction, and, because they share many of the analyses developed for MPMs, it only emphasises how perturbation analyses can be performed with respect to different model elements. The chapter exemplifies the construction of a simple and a more complex IPM structure with an animal and a plant case study, respectively. Finally, inverse modelling in IPMs is presented, a method that allows population projection when some vital rates are not observed.

scholarly journals Lifetime, 5-year and past-year prevalence of homelessness in Europe: a cross-national survey in eight European nations

BMJ Open ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. e033237 ◽  
Author(s):  
Owen Taylor ◽  
Sandrine Loubiere ◽  
Aurelie Tinland ◽  
Maria Vargas-Moniz ◽  
Freek Spinnewijn ◽  
...  
Keyword(s):  
Service Use ◽  
Linear Models ◽  
Pairwise Comparison ◽  
Response Rates ◽  
Computer Assisted ◽  
Generalised Linear Models ◽  
The Past ◽  
History Of ◽  
Nationally Representative ◽  
Difference Test

ObjectivesTo examine the lifetime, 5-year and past-year prevalence of homelessness among European citizens in eight European nations.DesignA nationally representative telephone survey using trained bilingual interviewers and computer-assisted telephone interview software.SettingThe study was conducted in France, Ireland, Italy, the Netherlands, Poland, Portugal, Spain and Sweden.ParticipantsEuropean adult citizens, selected from opt-in panels from March to December 2017. Total desired sample size was 5600, with 700 per country. Expected response rates of approximately 30% led to initial sample sizes of 2500 per country.Main outcome measuresHistory of homelessness was assessed for lifetime, past 5 years and past year. Sociodemographic data were collected to assess correlates of homelessness prevalence using generalised linear models for clustered and weighted samples.ResultsResponse rates ranged from 30.4% to 33.5% (n=5631). Homelessness prevalence was 4.96% for lifetime (95% CI 4.39% to 5.59%), 1.92% in the past 5 years (95% CI 1.57% to 2.33%) and 0.71% for the past year (95% CI 0.51% to 0.98%) and varied significantly between countries (pairwise comparison difference test, p<0.0001). Time spent homeless ranged between less than a week (21%) and more than a year (18%), with high contrasts between countries (p<0.0001). Male gender, age 45–54, lower secondary education, single status, unemployment and an urban environment were all independently strongly associated with lifetime homelessness (all OR >1.5).ConclusionsThe prevalence of homelessness among the surveyed nations is significantly higher than might be expected from point-in-time and homeless service use statistics. There was substantial variation in estimated prevalence across the eight nations. Coupled with the well-established health impacts of homelessness, medical professionals need to be aware of the increased health risks of those with experience of homelessness. These findings support policies aiming to improve health services for people exposed to homelessness.

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