Extrapolation methods for climate time series revisited – Spatial correlations in climatic fluctuations influence simulated tree species abundance and migration

2014 ◽  
Vol 20 ◽  
pp. 315-324 ◽  
Author(s):  
Julia E.M.S. Nabel ◽  
James W. Kirchner ◽  
Natalie Zurbriggen ◽  
Felix Kienast ◽  
Heike Lischke
Keyword(s):  
Time Series ◽  
Tree Species ◽  
Species Abundance ◽  
Extrapolation Methods ◽  
Spatial Correlations ◽  
Climatic Fluctuations ◽  
And Migration ◽  
Climate Time Series

scholarly journals Spatial and space-time correlations in systems of subpopulations with genetic drift and migration.

Genetics ◽  
1993 ◽  
Vol 133 (3) ◽  
pp. 711-727
Author(s):  
B K Epperson
Keyword(s):  
Population Genetics ◽  
Time Correlation ◽  
Space Time ◽  
Spatial Correlations ◽  
Gene Frequencies ◽  
Migration Patterns ◽  
Migration Rates ◽  
Space And Time ◽  
Time Correlations ◽  
And Migration

Abstract The geographic distribution of genetic variation is an important theoretical and experimental component of population genetics. Previous characterizations of genetic structure of populations have used measures of spatial variance and spatial correlations. Yet a full understanding of the causes and consequences of spatial structure requires complete characterization of the underlying space-time system. This paper examines important interactions between processes and spatial structure in systems of subpopulations with migration and drift, by analyzing correlations of gene frequencies over space and time. We develop methods for studying important features of the complete set of space-time correlations of gene frequencies for the first time in population genetics. These methods also provide a new alternative for studying the purely spatial correlations and the variance, for models with general spatial dimensionalities and migration patterns. These results are obtained by employing theorems, previously unused in population genetics, for space-time autoregressive (STAR) stochastic spatial time series. We include results on systems with subpopulation interactions that have time delay lags (temporal orders) greater than one. We use the space-time correlation structure to develop novel estimators for migration rates that are based on space-time data (samples collected over space and time) rather than on purely spatial data, for real systems. We examine the space-time and spatial correlations for some specific stepping stone migration models. One focus is on the effects of anisotropic migration rates. Partial space-time correlation coefficients can be used for identifying migration patterns. Using STAR models, the spatial, space-time, and partial space-time correlations together provide a framework with an unprecedented level of detail for characterizing, predicting and contrasting space-time theoretical distributions of gene frequencies, and for identifying features such as the pattern of migration and estimating migration rates in experimental studies of genetic variation over space and time.

Econometrics for Modelling Climate Change

2021 ◽  
Author(s):  
Jennifer L. Castle ◽  
David F. Hendry
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Econometric Modeling ◽  
Complex Data ◽  
Explanatory Variables ◽  
Stochastic Trends ◽  
Trend Breaks ◽  
Abrupt Shifts ◽  
Climate Time Series ◽  
Do So

Shared features of economic and climate time series imply that tools for empirically modeling nonstationary economic outcomes are also appropriate for studying many aspects of observational climate-change data. Greenhouse gas emissions, such as carbon dioxide, nitrous oxide, and methane, are a major cause of climate change as they cumulate in the atmosphere and reradiate the sun’s energy. As these emissions are currently mainly due to economic activity, economic and climate time series have commonalities, including considerable inertia, stochastic trends, and distributional shifts, and hence the same econometric modeling approaches can be applied to analyze both phenomena. Moreover, both disciplines lack complete knowledge of their respective data-generating processes (DGPs), so model search retaining viable theory but allowing for shifting distributions is important. Reliable modeling of both climate and economic-related time series requires finding an unknown DGP (or close approximation thereto) to represent multivariate evolving processes subject to abrupt shifts. Consequently, to ensure that DGP is nested within a much larger set of candidate determinants, model formulations to search over should comprise all potentially relevant variables, their dynamics, indicators for perturbing outliers, shifts, trend breaks, and nonlinear functions, while retaining well-established theoretical insights. Econometric modeling of climate-change data requires a sufficiently general model selection approach to handle all these aspects. Machine learning with multipath block searches commencing from very general specifications, usually with more candidate explanatory variables than observations, to discover well-specified and undominated models of the nonstationary processes under analysis, offers a rigorous route to analyzing such complex data. To do so requires applying appropriate indicator saturation estimators (ISEs), a class that includes impulse indicators for outliers, step indicators for location shifts, multiplicative indicators for parameter changes, and trend indicators for trend breaks. All ISEs entail more candidate variables than observations, often by a large margin when implementing combinations, yet can detect the impacts of shifts and policy interventions to avoid nonconstant parameters in models, as well as improve forecasts. To characterize nonstationary observational data, one must handle all substantively relevant features jointly: A failure to do so leads to nonconstant and mis-specified models and hence incorrect theory evaluation and policy analyses.

scholarly journals Effect of disturbance on plant species abundance and density distribution in tropical forest of Sunsari district, Eastern Nepal

2016 ◽  
Vol 6 (1) ◽  
pp. 1-12
Author(s):  
Tilak Prasad Gautam ◽  
Tej Narayan Mandal
Keyword(s):  
Carbon Sequestration ◽  
Species Composition ◽  
Tropical Forest ◽  
Tropical Forests ◽  
Tree Species ◽  
Forest Disturbance ◽  
Species Abundance ◽  
Forest Degradation ◽  
Tree Density ◽  
Shrub Species

The disappearance of global tropical forests due to deforestation and forest degradation has reduced the biodiversity and carbon sequestration capacity. In these contexts, present study was carried out to understand the species composition and density in the undisturbed and disturbed stands of moist tropical forest located in Sunsari district of eastern Nepal. Study revealed that the forest disturbance has reduced the number of tree species by 33% and tree density by 50%. In contrary, both number and density of herb and shrub species have increased with forest disturbance.

scholarly journals Causal Discovery for Climate Time Series in the Presence of Unobserved Variables

2020 ◽  
Author(s):  
Andreas Gerhardus ◽  
Jakob Runge
Keyword(s):  
Time Series ◽  
Causal Discovery ◽  
Time Lags ◽  
Cause And Effect ◽  
Scientific Curiosity ◽  
Relevant Variables ◽  
Data Driven Approach ◽  
Dynamical Regimes ◽  
Unobserved Variables ◽  
Climate Time Series

<p>Scientific inquiry seeks to understand natural phenomena by understanding their underlying processes, i.e., by identifying cause and effect. In addition to mere scientific curiosity, an understanding of cause and effect relationships is necessary to predict the effect of changing dynamical regimes and for the attribution of extreme events to potential causes. It is thus an important question to ask how, in cases where controlled experiments are not feasible, causation can still be inferred from the statistical dependencies in observed time series.</p><p>A central obstacle for such an inference is the potential existence of unobserved causally relevant variables. Arguably, this is more likely to be the case than not, for example unmeasured deep oceanic variables in atmospheric processes. Unobserved variables can act as confounders (meaning they are a common cause of two or more observed variables) and thus introduce spurious, i.e., non-causal dependencies. Despite these complications, the last three decades have seen the development of so-called causal discovery algorithms (an example being FCI by Spirtes et al., 1999) that are often able to identify spurious associations and to distinguish them from genuine causation. This opens the possibility for a data-driven approach to infer cause and effect relationships among climate variables, thereby contributing to a better understanding of Earth's complex climate system.</p><p>These methods are, however, not yet well adapted to some specific challenges that climate time series often come with, e.g. strong autocorrelation, time lags and nonlinearities. To close this methodological gap, we generalize the ideas of the recent PCMCI causal discovery algorithm (Runge et al., 2019) to time series where unobserved causally relevant variables may exist (in contrast, PCMCI made the assumption of no confounding). Further, we present preliminary applications to modes of climate variability.</p>

scholarly journals Diversity of Tree Species in Rivers State University, Port Harcourt, Nigeria

2021 ◽  
pp. 67-72
Author(s):  
P. C. Nnadi ◽  
B. B. Otene ◽  
Nwiisator David-Sarogoro
Keyword(s):  
Plant Species ◽  
Tree Species ◽  
Dominant Species ◽  
Species Abundance ◽  
State University ◽  
University Campus ◽  
Management Interventions ◽  
Port Harcourt ◽  
Pinus Spp ◽  
Rivers State

This study was carried out to examine the distribution of plant species in Rivers State University campus at different locations at the Rivers State University Nkpolu-Oroworukwo Port Harcourt Nigeria. The general objective of this study was to examine the Ecological distribution of plant species in Rivers State University campus at different locations. The specific objective was to determine the species abundance of various plants in the study area and also to examine the ecological diversity of tree species in the various groups. The study area was divided into three groups (stations) with the various plants species identified and recorded. Data gotten from the field was analyzed using descriptive statistic and some ecological indices such as Margalef, Mehinick, Shannon diversity, Shannon Wiener, Evenness/Equitability and Simpson dominance.  A total of one thousand Sixty-nine (1069) individual plant were identified with 16, 17 and 12 species in stations 1-3 respectively. The highest individual plants (561) were observed in station 1 while the least (87) were observed in station 3. The mean values of stations 1 and 2 were significantly and statistically different from site 3 at p<0.05. The results obtained showed some dominant species to include Elaeis guincensis, Polyaithia longifolia, Pinus spp, Gmelina arborea, Wodyetia bifareata, Citrus sinensis, Cocos nuciferia while others were the least dominant species found in the study area. The Margalef, Mehinicks and Shannon Wiener values were consistently highest in station 2 but lowest in station 3.  Human disturbances had negative impact on tree species abundance especially in site 3. It is therefore recommended that management interventions are necessary in other to stop indiscriminate felling of the various trees species that made up the different groups.

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