scholarly journals Tracking Changes of Hidden Food: Spatial Pattern Learning in Two Macaw Species

Birds ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 285-301
Author(s):  
Pizza Ka Yee Chow ◽  
James R. Davies ◽  
Awani Bapat ◽  
Auguste M. P. von Bayern
Keyword(s):  
Spatial Pattern ◽  
Food Availability ◽  
Spatial Information ◽  
Distribution Patterns ◽  
Learning Performance ◽  
Food Sources ◽  
Pattern Learning ◽  
Generalist Species ◽  
Habitat Specialisation ◽  
Alternative Food Sources

Food availability may vary spatially and temporally within an environment. Efficiency in locating alternative food sources using spatial information (e.g., distribution patterns) may vary according to a species’ diet and habitat specialisation. Hypothetically, more generalist species would learn faster than more specialist species due to being more explorative when changes occur. We tested this hypothesis in two closely related macaw species, differing in their degree of diet and habitat specialisation; the more generalist Great Green Macaw and the more specialist Blue-throated Macaw. We examined their spatial pattern learning performance under predictable temporal and spatial change, using a ‘poke box’ that contained hidden food placed within wells. Each week, the rewarded wells formed two patterns (A and B), which were changed on a mid-week schedule. We found that the two patterns varied in their difficulty. We also found that the more generalist Great Green Macaws took fewer trials to learn the easier pattern and made more mean correct responses in the difficult pattern than the more specialist Blue-throated Macaws, thus supporting our hypothesis. The better learning performance of the Great Green Macaws may be explained by more exploration and trading-off accuracy for speed. These results suggest how variation in diet and habitat specialisation may relate to a species’ ability to adapt to spatial variation in food availability.

Visual cues and spatial pattern learning

2001 ◽  
Author(s):  
Michael F. Brown ◽  
Sue Yang ◽  
Kelly Digian
Keyword(s):  
Spatial Pattern ◽  
Visual Cues ◽  
Pattern Learning

scholarly journals Multi-Temporal Built-Up Grids of Brazilian Cities: How Trends and Dynamic Modelling Could Help on Resilience Challenges?

2021 ◽  
Vol 13 (2) ◽  
pp. 748
Author(s):  
Iana Rufino ◽  
Slobodan Djordjević ◽  
Higor Costa de Brito ◽  
Priscila Barros Ramalho Alves
Keyword(s):  
Land Use ◽  
Urban Planning ◽  
Cellular Automata ◽  
Land Use Change ◽  
Spatial Pattern ◽  
Urban Growth ◽  
Spatial Information ◽  
Dynamic Modelling ◽  
Urban Models ◽  
Extreme Hazards

The northeastern Brazilian region has been vulnerable to hydrometeorological extremes, especially droughts, for centuries. A combination of natural climate variability (most of the area is semi-arid) and water governance problems increases extreme events’ impacts, especially in urban areas. Spatial analysis and visualisation of possible land-use change (LUC) zones and trends (urban growth vectors) can be useful for planning actions or decision-making policies for sustainable development. The Global Human Settlement Layer (GHSL) produces global spatial information, evidence-based analytics, and knowledge describing Earth’s human presence. In this work, the GHSL built-up grids for selected Brazilian cities were used to generate urban models using GIS (geographic information system) technologies and cellular automata for spatial pattern simulations of urban growth. In this work, six Brazilian cities were selected to generate urban models using GIS technologies and cellular automata for spatial pattern simulations of urban sprawl. The main goal was to provide predictive scenarios for water management (including simulations) and urban planning in a region highly susceptible to extreme hazards, such as floods and droughts. The northeastern Brazilian cities’ analysis raises more significant challenges because of the lack of land-use change field data. Findings and conclusions show the potential of dynamic modelling to predict scenarios and support water sensitive urban planning, increasing cities’ coping capacity for extreme hazards.

scholarly journals Spatial Pattern and Hotspots of Urban Rail Public Transport to Public Access Using Geospatial Techniques in Selangor, Malaysia

2021 ◽  
Vol 6 (1) ◽  
pp. 234-244
Author(s):  
Mohd Sahrul Syukri BIN Yahya ◽  
Edie Ezwan Mohd Safian ◽  
Burhaida Burhan
Keyword(s):  
Open Access ◽  
Spatial Pattern ◽  
Public Transport ◽  
Public Transportation ◽  
Spatial Information ◽  
Hot Spot ◽  
Developed Countries ◽  
Transportation Systems ◽  
Gis Mapping ◽  
Urban Public Transport

Currently, the trends in urban public transport have been changing over the years in developing countries for mobilization and accessibility development. Urban public transportation systems are the most popular in Selangor State, including big cities such as the Klang Valley Region. Objective measures of spatial pattern and hotspots have been used to understand how urban public transport development relate to open access. This method relies on specific spatial information and available web-based tool that shows the pattern primarily based on given vicinity and statistics connectivity. To date, several studies have finished tested in developed countries. In this study, we use Geographic Information Systems to analyse and consider hotspots identification precisely and efficaciously. Therefore, in this paper, we focus on two types of point sample evaluations – Gi* hot spot and point density analysis evaluation as statistical operations. Public rail transport was evaluated as a validation to describe the percentage of distribution of open access. The final result, GIS mapping capabilities to show that GIS's technology offers to the variation of urban public transport relate to public services, is to create maps and spatial interpretations.

scholarly journals 3DViewGraph: Learning Global Features for 3D Shapes from A Graph of Unordered Views with Attention

2019 ◽  
Author(s):  
Zhizhong Han ◽  
Xiyang Wang ◽  
Chi Man Vong ◽  
Yu-Shen Liu ◽  
Matthias Zwicker ◽  
...  
Keyword(s):  
Spatial Pattern ◽  
Large Scale ◽  
Spatial Information ◽  
Dimensional Space ◽  
Spatial Relationship ◽  
Global Features ◽  
Pattern Correlation ◽  
3D Shape Analysis ◽  
3D Shapes ◽  
Learned Features

Learning global features by aggregating information over multiple views has been shown to be effective for 3D shape analysis. For view aggregation in deep learning models, pooling has been applied extensively. However, pooling leads to a loss of the content within views, and the spatial relationship among views, which limits the discriminability of learned features. We propose 3DViewGraph to resolve this issue, which learns 3D global features by more effectively aggregating unordered views with attention. Specifically, unordered views taken around a shape are regarded as view nodes on a view graph. 3DViewGraph first learns a novel latent semantic mapping to project low-level view features into meaningful latent semantic embeddings in a lower dimensional space, which is spanned by latent semantic patterns. Then, the content and spatial information of each pair of view nodes are encoded by a novel spatial pattern correlation, where the correlation is computed among latent semantic patterns. Finally, all spatial pattern correlations are integrated with attention weights learned by a novel attention mechanism. This further increases the discriminability of learned features by highlighting the unordered view nodes with distinctive characteristics and depressing the ones with appearance ambiguity. We show that 3DViewGraph outperforms state-of-the-art methods under three large-scale benchmarks.

scholarly journals Replacing Red Meat with Alternative Food Sources of Protein on Risk of Type 2 Diabetes - Modeling Dietary Changes in a Causal Framework

2020 ◽  
Vol 4 (Supplement_2) ◽  
pp. 1418-1418
Author(s):  
Daniel Ibsen ◽  
Marianne Jakobsen ◽  
Jytte Halkjær ◽  
Erik Parner ◽  
Kim Overvad
Keyword(s):  
Type 2 Diabetes ◽  
Whole Grains ◽  
Red Meat ◽  
Risk Difference ◽  
Alternative Food ◽  
Food Sources ◽  
Incident Type ◽  
Exposure Effect ◽  
Alternative Food Sources

Abstract Objectives We investigated whether decreasing the intake of red meat and simultaneously increasing the intake of alternative food sources of protein affects the risk of type 2 diabetes compared with no changes in the substituted foods. We also examined interaction with the age at which participants changed their diet. Methods We used the Danish Diet, Cancer and Health cohort including men and women, two measures of diet taken roughly 5 years apart using food frequency questionnaires and information on incident type 2 diabetes derived from the Danish National Diabetes Register (n = 39,349; aged 55 to 72 years at the second diet measure; n cases = 3759). The pseudo-observation method was used to estimate the average exposure effect of decreasing the intake of red meat (processed and unprocessed) while increasing the intake of either poultry, fish, cheese, eggs or whole grains compared with no changes in the substituted foods on the subsequent 10-year risk of development type 2 diabetes. Results In multivariable adjusted models, we found that replacing 1 serving/day (100 g) of red meat with 1 serving/day of eggs (50 g) (risk difference −2.4%, 95% confidence interval −3.7 to −1.1%) or whole grains (30 g) (−1.4%, −2.2 to −0.6%) was associated with a reduced risk of type 2 diabetes. No effects were observed for other replacements. In general, the lowest risk was observed for replacements at age 55 years compared with older ages (up to 70 years) for all replacements. Conclusions Replacing red meat with eggs or whole grains may reduce the risk of type 2 diabetes compared with no changes in the substituted foods. Changing red meat intake in midlife may be more beneficial than at older ages. Funding Sources Aarhus University.

scholarly journals Scales of Food Availability for an Endangered Insectivore, The Hawaii Akepa

The Auk ◽  
2002 ◽  
Vol 119 (1) ◽  
pp. 166-174 ◽  
Author(s):  
J. Scott Fretz
Keyword(s):  
Food Availability ◽  
Prey Availability ◽  
Spatial Scales ◽  
Canopy Cover ◽  
Distribution Patterns ◽  
Prey Population ◽  
Suitable Habitat ◽  
Population Declines ◽  
Loxops Coccineus ◽  
A Site

Abstract The Hawaii Akepa (Loxops coccineus coccineus) is an endangered bird that has declined dramatically in the last 100 years, and is now rare or absent from many areas that appear to support suitable habitat. Food availability may play a role in these distribution patterns, but differences in food between sites may arise from different sources. I compared prey availability between a site supporting a large, stable Hawaii Akepa population, and a site from which Hawaii Akepa have declined in the last 100 years for unknown reasons. I used three spatial scales to compare food between sites to explore the basis of differences in food between sites. At a scale appropriate for comparing prey population dynamics (scale 1), I found that prey population densities are similar between sites, suggesting that introduced (or native) predators or parasitoids have not affected prey populations differently between sites. At two larger scales incorporating habitat structure, I found that food availability is much lower at the site of Hawaii Akepa declines. Differences in canopy density per square meter (scale 2), and in canopy cover per square kilometer (scale 3), result in lower food availability that may have effects on individual foraging birds as well as on potential Hawaii Akepa population density. These findings illustrate the importance of explicitly incorporating spatial scale into inquiries about food for Hawaii Akepa, and suggest that the site of population declines may not be suitable habitat with respect to food for this species.

scholarly journals Spatial pattern learning in the radial arm maze

2006 ◽  
Vol 34 (1) ◽  
pp. 102-108 ◽  
Author(s):  
Michael F. Brown ◽  
Gary W. Giumetti
Keyword(s):  
Spatial Pattern ◽  
Radial Arm Maze ◽  
Pattern Learning
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