Other Techniques Related to Habitat Analysis

2021 ◽  
pp. 193-208
Author(s):  
Joseph A. Veech
Keyword(s):  
Habitat Suitability ◽  
Environmental Variables ◽  
Resource Selection ◽  
Compositional Data ◽  
Initial Step ◽  
Compositional Data Analysis ◽  
Habitat Suitability Modeling ◽  
Species Response ◽  
Habitat Analysis ◽  
Active Choice

Habitat analysis is strictly defined as a statistical examination to identify the environmental variables that a species associates with, wherein association is taken as some form of correspondence between a species response variable (e.g., presence–absence or abundance) and the environmental variables. There are other statistical techniques and empirical goals that extend this basic framework. These techniques often rely on a habitat analysis having been conducted as an initial step. Resource selection functions quantify an individual’s and a species’ use of a resource based upon the properties of the resource. Resource is broadly defined and can include particular types of habitat. Selectivity and preference indices are used to assess an individual’s preference and active choice of different resource types. Compositional data analysis is a statistical method for examining the composition of an individual’s territory or home range with regard to different habitat types that may be represented. Habitat suitability modeling and species distribution modeling are closely related techniques designed to map the spatial distribution of a species’ habitat and sometimes the species itself based upon its habitat requirements and other factors.

Habitat suitability modeling based on remote sensing to realize time synchronization of species and environmental variables

2020 ◽  
Author(s):  
Da-Ju Wang ◽  
Hai-Yan Wei ◽  
Xu-Hui Zhang ◽  
Ya-Qin Fang ◽  
Wei Gu
Keyword(s):  
Remote Sensing ◽  
Habitat Suitability ◽  
Land Surface ◽  
Environmental Variables ◽  
Large Scale ◽  
Time Synchronization ◽  
Ambrosia Artemisiifolia ◽  
Real World Data ◽  
Habitat Suitability Modeling ◽  
Temperature And Precipitation

Abstract Aims Remote sensing (RS) is a technical method for effectively capturing real-world data on a large scale. We aimed to (i) realize the time synchronization of species and environmental variables, and extract variables related to the actual growth of species based on RS in habitat suitability modeling, and (ii) provide a reference for species management. Methods Taking invasive species Ambrosia artemisiifolia in China as an example for habitat suitability modeling. Temperature and precipitation variables were calculated from the land surface temperature (LST) provided by the moderate-resolution imaging spectroradiometer (MODIS), and climate station data, respectively. Besides, other variables that directly affect the growth or reproduction of A. artemisiifolia were also included, such as the relative humidity of the previous year's flowering period (RHPFP), and the effective UV irradiance reaching the Earth's surface (UVI). The Random Forest (RF) method was selected to model the habitat suitability. The environmental variables and samples were divided into four-time periods (i.e. 1990-2000, 2001-2005, 2006-2010, and 2011-2016) based on sampling time. Variables from the long-time series of RS (1990-2016) and WorldClim (1960-1990) were also modeled. Important Findings It was feasible to extract environmental variables from RS for habitat suitability modeling, and was more accurate than that based on the variables from WorldClim. The potential distribution of A. artemisiifolia in 1990-2000 and 2006-2010 was smaller than that in 2001-2005 and 2011-2016. The precipitation of driest months (bio14), precipitation coefficient of variation (bio15), RHPFP, and UVI were the important environmental variables that affect the growth and reproduction of A. artemisiifolia. The results indicated that the time synchronization of species and environmental variables improved the prediction accuracy of A. artemisiifolia, which should be considered in habitat suitability modeling (especially for annual species). This study can provide an important reference for the management and prevention of the spread of A. artemisiifolia.

scholarly journals Mapping current and potential future distributions of the oak tree (Quercus aegilops) in the Kurdistan Region, Iraq

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Nabaz R. Khwarahm
Keyword(s):  
Climate Change ◽  
Habitat Suitability ◽  
Environmental Variables ◽  
Climate Changes ◽  
Future Climate ◽  
Annual Precipitation ◽  
Future Climate Change ◽  
Climate Change Scenarios ◽  
Kurdistan Region ◽  
Oak Tree

Abstract Background The oak tree (Quercus aegilops) comprises ~ 70% of the oak forests in the Kurdistan Region of Iraq (KRI). Besides its ecological importance as the residence for various endemic and migratory species, Q. aegilops forest also has socio-economic values—for example, as fodder for livestock, building material, medicine, charcoal, and firewood. In the KRI, Q. aegilops has been degrading due to anthropogenic threats (e.g., shifting cultivation, land use/land cover changes, civil war, and inadequate forest management policy) and these threats could increase as climate changes. In the KRI and Iraq as a whole, information on current and potential future geographical distributions of Q. aegilops is minimal or not existent. The objectives of this study were to (i) predict the current and future habitat suitability distributions of the species in relation to environmental variables and future climate change scenarios (Representative Concentration Pathway (RCP) 2.6 2070 and RCP8.5 2070); and (ii) determine the most important environmental variables controlling the distribution of the species in the KRI. The objectives were achieved by using the MaxEnt (maximum entropy) algorithm, available records of Q. aegilops, and environmental variables. Results The model demonstrated that, under the RCP2.6 2070 and RCP8.5 2070 climate change scenarios, the distribution ranges of Q. aegilops would be reduced by 3.6% (1849.7 km2) and 3.16% (1627.1 km2), respectively. By contrast, the species ranges would expand by 1.5% (777.0 km2) and 1.7% (848.0 km2), respectively. The distribution of the species was mainly controlled by annual precipitation. Under future climate change scenarios, the centroid of the distribution would shift toward higher altitudes. Conclusions The results suggest (i) a significant suitable habitat range of the species will be lost in the KRI due to climate change by 2070 and (ii) the preference of the species for cooler areas (high altitude) with high annual precipitation. Conservation actions should focus on the mountainous areas (e.g., by establishment of national parks and protected areas) of the KRI as climate changes. These findings provide useful benchmarking guidance for the future investigation of the ecology of the oak forest, and the categorical current and potential habitat suitability maps can effectively be used to improve biodiversity conservation plans and management actions in the KRI and Iraq as a whole.

scholarly journals Effects of Two Randomized and Controlled Multi-Component Interventions Focusing On 24-Hour Movement Behavior among Office Workers: A Compositional Data Analysis

2021 ◽  
Vol 18 (8) ◽  
pp. 4191
Author(s):  
Lisa-Marie Larisch ◽  
Emil Bojsen-Møller ◽  
Carla F. J. Nooijen ◽  
Victoria Blom ◽  
Maria Ekblom ◽  
...  
Keyword(s):  
Physical Activity ◽  
Data Analysis ◽  
Cardiorespiratory Fitness ◽  
Leisure Time ◽  
Compositional Data ◽  
Office Workers ◽  
Movement Behavior ◽  
Compositional Data Analysis ◽  
Intervention Effects ◽  
Time In Bed

Intervention studies aiming at changing movement behavior have usually not accounted for the compositional nature of time-use data. Compositional data analysis (CoDA) has been suggested as a useful strategy for analyzing such data. The aim of this study was to examine the effects of two multi-component interventions on 24-h movement behavior (using CoDA) and on cardiorespiratory fitness among office workers; one focusing on reducing sedentariness and the other on increasing physical activity. Office workers (n = 263) were cluster randomized into one of two 6-month intervention groups, or a control group. Time spent in sedentary behavior, light-intensity, moderate and vigorous physical activity, and time in bed were assessed using accelerometers and diaries, both for 24 h in total, and for work and leisure time separately. Cardiorespiratory fitness was estimated using a sub-maximal cycle ergometer test. Intervention effects were analyzed using linear mixed models. No intervention effects were found, either for 24-h behaviors in total, or for work and leisure time behaviors separately. Cardiorespiratory fitness did not change significantly. Despite a thorough analysis of 24-h behaviors using CoDA, no intervention effects were found, neither for behaviors in total, nor for work and leisure time behaviors separately. Cardiorespiratory fitness did not change significantly. Although the design of the multi-component interventions was based on theoretical frameworks, and included cognitive behavioral therapy counselling, which has been proven effective in other populations, issues related to implementation of and compliance with some intervention components may have led to the observed lack of intervention effect.

Coupling compositional data analysis (CoDA) with hierarchical cluster analysis (HCA) for preliminary understanding of the dynamics of a complex water distribution system: the Naples (south Italy) case study

2021 ◽  
Author(s):  
Pooria Ebrahimi ◽  
Stefano Albanese ◽  
Leopoldo Esposito ◽  
Daniela Zuzolo ◽  
Domenico Cicchella
Keyword(s):  
Distribution System ◽  
Water Distribution ◽  
Compositional Data ◽  
Tap Water ◽  
Hierarchical Cluster ◽  
Compositional Data Analysis ◽  
Anthropogenic Pressure ◽  
South Italy ◽  
Global Concern

Providing safe tap water has been a global concern. Water scarcity, the ever-increasing water demand, temporal variation of water consumption, aging urban water infrastructure and anthropogenic pressure on the water...

scholarly journals The Gut Microbiota of Healthy Aged Chinese Is Similar to That of the Healthy Young

mSphere ◽  
2017 ◽  
Vol 2 (5) ◽  
Author(s):  
Gaorui Bian ◽  
Gregory B. Gloor ◽  
Aihua Gong ◽  
Changsheng Jia ◽  
Wei Zhang ◽  
...  
Keyword(s):  
Data Analysis ◽  
Gut Microbiota ◽  
Large Scale ◽  
Compositional Data ◽  
Healthy Lifestyle ◽  
Compositional Data Analysis ◽  
Surprising Result ◽  
Microbiota Composition ◽  
Cross Sectional ◽  
Age Cohorts

ABSTRACT We report the large-scale use of compositional data analysis to establish a baseline microbiota composition in an extremely healthy cohort of the Chinese population. This baseline will serve for comparison for future cohorts with chronic or acute disease. In addition to the expected difference in the microbiota of children and adults, we found that the microbiota of the elderly in this population was similar in almost all respects to that of healthy people in the same population who are scores of years younger. We speculate that this similarity is a consequence of an active healthy lifestyle and diet, although cause and effect cannot be ascribed in this (or any other) cross-sectional design. One surprising result was that the gut microbiota of persons in their 20s was distinct from those of other age cohorts, and this result was replicated, suggesting that it is a reproducible finding and distinct from those of other populations. The microbiota of the aged is variously described as being more or less diverse than that of younger cohorts, but the comparison groups used and the definitions of the aged population differ between experiments. The differences are often described by null hypothesis statistical tests, which are notoriously irreproducible when dealing with large multivariate samples. We collected and examined the gut microbiota of a cross-sectional cohort of more than 1,000 very healthy Chinese individuals who spanned ages from 3 to over 100 years. The analysis of 16S rRNA gene sequencing results used a compositional data analysis paradigm coupled with measures of effect size, where ordination, differential abundance, and correlation can be explored and analyzed in a unified and reproducible framework. Our analysis showed several surprising results compared to other cohorts. First, the overall microbiota composition of the healthy aged group was similar to that of people decades younger. Second, the major differences between groups in the gut microbiota profiles were found before age 20. Third, the gut microbiota differed little between individuals from the ages of 30 to >100. Fourth, the gut microbiota of males appeared to be more variable than that of females. Taken together, the present findings suggest that the microbiota of the healthy aged in this cross-sectional study differ little from that of the healthy young in the same population, although the minor variations that do exist depend upon the comparison cohort. IMPORTANCE We report the large-scale use of compositional data analysis to establish a baseline microbiota composition in an extremely healthy cohort of the Chinese population. This baseline will serve for comparison for future cohorts with chronic or acute disease. In addition to the expected difference in the microbiota of children and adults, we found that the microbiota of the elderly in this population was similar in almost all respects to that of healthy people in the same population who are scores of years younger. We speculate that this similarity is a consequence of an active healthy lifestyle and diet, although cause and effect cannot be ascribed in this (or any other) cross-sectional design. One surprising result was that the gut microbiota of persons in their 20s was distinct from those of other age cohorts, and this result was replicated, suggesting that it is a reproducible finding and distinct from those of other populations.

Compositional Data Analysis (CoDA) as a tool to study the (paleo)ecology of coccolithophores from coastal-neritic settings off central Portugal

2015 ◽  
Vol 319 ◽  
pp. 134-146 ◽  
Author(s):  
Catarina Guerreiro ◽  
Mário Cachão ◽  
Vera Pawlowsky-Glahn ◽  
Anabela Oliveira ◽  
Aurora Rodrigues
Keyword(s):  
Data Analysis ◽  
Compositional Data ◽  
Compositional Data Analysis ◽  
Central Portugal
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