scholarly journals The map of biodiversity: From local to global scales

2018 ◽  
Author(s):  
Maria Anton Pardo
Keyword(s):  
Environmental Variables ◽  
Environmental Heterogeneity ◽  
Spatial Scales ◽  
Global Scale ◽  
Local Scale ◽  
Habitat Size ◽  
Diversity Patterns ◽  
Regional Species Pool ◽  
Species Area ◽  
Species Area Relationship

Species richness is not homogeneous in space and it normally presents differences when comparing among different sites. These differences often respond to gradients in one or several factors which create biodiversity patterns in space and are scale-dependent. At a local scale, diversity patterns depend on the habitat size (species-area relationship), the productivity, the environmental harshness, the frequency and intensity of disturbance, or the regional species pool. Regional diversity may be influenced by environmental heterogeneity (increasing dissimilarity), although it could act also at smaller or larger spatial scales, and the connectivity among habitats. Finally, at a global scale, diversity patterns are found with the latitude, the altitude or the depth, although these factors are surrogates or one or several environmental variables (productivity, area, isolation, or harshness).

scholarly journals Global gradients in intertidal species richness and functional groups

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Jakob Thyrring ◽  
Lloyd S Peck
Keyword(s):  
Functional Groups ◽  
Global Scale ◽  
Local Scale ◽  
Rocky Intertidal ◽  
Marine Biodiversity ◽  
Diversity Patterns ◽  
The North ◽  
Discernible Effect ◽  
Diversity Gradients ◽  
Α Diversity

Whether global latitudinal diversity gradients exist in rocky intertidal a-diversity and across functional groups remains unknown. Using literature data from 433 intertidal sites, we investigated α-diversity patterns across 155° of latitude, and whether local-scale or global-scale structuring processes control α-diversity. We, furthermore, investigated how the relative composition of functional groups changes with latitude. α-diversity differed among hemispheres with a mid-latitudinal peak in the north, and a non-significant unimodal pattern in the south, but there was no support for a tropical-to-polar decrease in α-diversity. Although global-scale drivers had no discernible effect, the local-scale drivers significantly affected α-diversity, and our results reveal that latitudinal diversity gradients are outweighed by local-processes. In contrast to α-diversity patterns, three functional groups: predators, grazers and suspension-feeders diversity declined with latitude, coinciding with an inverse gradient in algae. Polar and tropical intertidal data were sparse, and more sampling is required to improve knowledge of marine biodiversity.

scholarly journals Estimation of Global and Local Complexities of Brain Networks: A Random Walks Approach

2019 ◽  
Author(s):  
Roberto C. Sotero ◽  
Lazaro M. Sanchez-Rodriguez ◽  
Narges Moradi
Keyword(s):  
Time Series ◽  
Functional Connectivity ◽  
Brain Activity ◽  
Spatial Scales ◽  
Mental States ◽  
Global Scale ◽  
Sample Entropy ◽  
Local Scale ◽  
Functional Connections ◽  
Local Complexity

AbstractThe complexity of brain activity has been observed at many spatial scales and there exists increasing evidence supporting its use in differentiating between mental states and disorders. Here we proposed a new measure of network (global) complexity that is constructed as the sum of the complexities of its nodes (i.e, local complexity). The local complexity of each node is regarded as an index that compares the sample entropy of the time series generated by the movement of a random walker on the network resulting from removing the node and its connections, with the sample entropy of the time series obtained from a regular lattice (the ordered state) and an Erdös-Renyi network (disordered state). We studied the complexity of fMRI-based resting-state functional networks. We found that positively correlated, or “pos”, network comprising only the positive functional connections has higher complexity than the anticorrelation (“neg”) network (comprising the negative functional connections) and the network consisting of the absolute value of all connections (“abs”). We also found a significant correlation between complexity and the strength of functional connectivity. For the pos network this correlation is significantly weaker at the local scale compared to the global scale, whereas for the neg network the link is stronger at the local scale than at the global scale, but still weaker than for the pos network. Our results suggest that the pos network is related to the information processing in the brain and should be used for functional connectivity analysis instead of the abs network as is usually done.

Dissimilar biodiversity data sets yield congruent patterns and inference in lichens

Botany ◽  
2020 ◽  
Author(s):  
James C. Lendemer ◽  
Jessica Coyle
Keyword(s):  
Environmental Variables ◽  
Large Scale ◽  
Spatial Scales ◽  
Geographic Region ◽  
Individual Species ◽  
Data Sets ◽  
Biodiversity Data ◽  
Regional Species Pool ◽  
Secondary Analyses ◽  
Multiple Species

Large-scale efforts to aggregate and promote the re-use of biodiversity data are leading to novel insights into biogeography and macroecology. However, secondary analyses must account for the tradeoffs and limitations of the original studies. Studies of speciose and taxonomically complex groups often utilize morphospecies or functional subsets as proxies, potentially complicating data re-use. We evaluated whether lichen biodiversity patterns are robust to differences in sampling methodology, utilizing parallel analyses to compare species richness, regional species pool variation, species probabilities of occurrence, and correlation of those three with environmental variables in data sets that cover the same geographic region. Analyses revealed that, although individual species distributions sometimes differed in idiosyncratic ways, inference based on the aggregated response of multiple species was generally robust across the two datasets, despite differences in observer expertise and functional and taxonomic scope. This suggests that biodiversity data assembled from disparate sources could be used to evaluate biogeographical and macroecological hypotheses in understudied groups such as lichens, particularly at larger spatial scales.

scholarly journals A framework for disentangling ecological mechanisms underlying the island species-area relationship

2018 ◽  
Author(s):  
Jonathan M. Chase ◽  
Leana Gooriah ◽  
Felix May ◽  
Wade A. Ryberg ◽  
Matthew S. Schuler ◽  
...  
Keyword(s):  
Random Sampling ◽  
Spatial Scales ◽  
Biodiversity Loss ◽  
Oceanic Islands ◽  
Island Area ◽  
Rarefaction Curves ◽  
Island Species ◽  
Species Area ◽  
Ecological Mechanisms ◽  
Species Area Relationship

AbstractThe relationship between an island’s size and the number of species on that island—the island species-area relationship (ISAR)—is one of the most well-known patterns in biogeography, and forms the basis for understanding biodiversity loss in response to habitat loss and fragmentation. Nevertheless, there is contention about exactly how to estimate the ISAR, and the influence of the three primary ecological mechanisms—random sampling, disproportionate effects, and heterogeneity— that drive it. Key to this contention is that estimates of the ISAR are often confounded by sampling and estimates of measures (i.e., island-level species richness) that are not diagnostic of potential mechanisms. Here, we advocate a sampling-explicit approach for disentangling the possible ecological mechanisms underlying the ISAR using parameters derived from individual-based rarefaction curves estimated across spatial scales. If the parameters derived from rarefaction curves at each spatial scale show no relationship with island area, we cannot reject the hypothesis that ISARs result only from random sampling. However, if the derived metrics change with island area, we can reject random sampling as the only operating mechanism, and infer that effects beyond sampling (i.e., disproportionate effects and/or heterogeneity) are also operating. Finally, if parameters indicative of within-island spatial variation in species composition (i.e., β-diversity) increase with island area, we can conclude that intra-island compositional heterogeneity plays a role in driving the ISAR. We illustrate this approach using representative case studies, including oceanic islands, natural island-like patches, and habitat fragments from formerly continuous habitat, illustrating several combinations of underlying mechanisms. This approach will offer insight into the role of sampling and other processes that underpin the ISAR, providing a more complete understanding of how, and some indication of why, patterns of biodiversity respond to gradients in island area.

Patterns of biological diversity

2019 ◽  
pp. 11-37
Author(s):  
Gary G. Mittelbach ◽  
Brian J. McGill
Keyword(s):  
Species Richness ◽  
Species Interactions ◽  
Biological Diversity ◽  
Spatial Scales ◽  
Species Numbers ◽  
Diversity Gradient ◽  
Species Abundances ◽  
Species Area ◽  
Species Area Relationship ◽  
The Relationship

This chapter examines how biodiversity, the variety of life, is distributed across the globe and within local communities. It begins by considering some of the challenges associated with assessing biological diversity at different spatial scales. Then, three of the best-studied patterns in species richness are examined in detail—the species–area relationship, the distribution of species abundances, and the relationship between productivity and species richness. The chapter concludes with a detailed exploration of the most dramatic of Earth’s biodiversity patterns—the latitudinal diversity gradient. The above patterns constitute much of what community ecology seeks to explain about nature. Their study provides a foundation from which to explore mechanisms of species interactions, and to understand the processes that drive variation in species numbers and their distribution.

scholarly journals Impacto do aumento de CO2 nas Precipitações do estado de Pernambuco

2021 ◽  
Vol 14 (3) ◽  
pp. 1828-1839
Author(s):  
Josiclêda Domiciano Galvincio
Keyword(s):  
Climate Change ◽  
Water Balance ◽  
Hydrological Cycle ◽  
Spatial Scales ◽  
Global Scale ◽  
Local Scale ◽  
The State ◽  
Atmospheric Co2 ◽  
Hydrological Response ◽  
Climatic Characteristics

Seeking to mitigate the effects of climate change requires knowing the current situation and the possible consequences of the increase in CO2 in the atmosphere in different areas of knowledge and spatial scales. On a global scale, studies are already widespread. At the regional and local scale, these studies still need to be advanced. Given the above, this study aims to assess the impacts of atmospheric CO2 on rainfall in the state of Pernambuco. Five municipalities in the state were chosen, Recife, Garanhuns, Serra Talhada, Belém do São Francisco and Petrolina. These municipalities represent the mesoregions of the state, which are related to their climatic characteristics. Several statistical methods were applied to identify the relationships between variables and how strong these relationships are, correlation and multivariate. The SUPER-System of Hydrological Response Units for Pernambuco was used to discuss the results with the water balance of Pernambuco basins. The results show that Pernambuco will have two impacting conditions with an increase in atmospheric CO2, Agreste and Litoral, an increase in precipitation and Sertão, a decrease in precipitation. The strongest impact being the Agreste. Considering the SUPER, it was possible to see that around 60% of Pernambuco's precipitation is transformed into evapotranspiration. It is known that the increase in CO2 impacts plant evapotranspiration. Thus, it is possible to state that not only changes in precipitation will impact water resources, but also evapotranspiration, which are important variables in the hydrological cycle of Pernambuco basins.

scholarly journals The strength of the biodiversity–ecosystem function relationship depends on spatial scale

2018 ◽  
Vol 285 (1880) ◽  
pp. 20180038 ◽  
Author(s):  
Patrick L. Thompson ◽  
Forest Isbell ◽  
Michel Loreau ◽  
Mary I. O'Connor ◽  
Andrew Gonzalez
Keyword(s):  
Spatial Variation ◽  
Spatial Scale ◽  
Spatial Scales ◽  
Scale Dependence ◽  
Α Diversity ◽  
Species Area ◽  
Species Area Relationship ◽  
Function Relationship ◽  
New Research ◽  
The Relationship

Our understanding of the relationship between biodiversity and ecosystem functioning (BEF) applies mainly to fine spatial scales. New research is required if we are to extend this knowledge to broader spatial scales that are relevant for conservation decisions. Here, we use simulations to examine conditions that generate scale dependence of the BEF relationship. We study scale by assessing how the BEF relationship (slope and R 2 ) changes when habitat patches are spatially aggregated. We find three ways for the BEF relationship to be scale-dependent: (i) variation among local patches in local (α) diversity, (ii) spatial variation in the local BEF relationship and (iii) incomplete compositional turnover in species composition among patches. The first two cause the slope of the BEF relationship to increase moderately with spatial scale, reflecting nonlinear averaging of spatial variation in diversity or the BEF relationship. The third mechanism results in much stronger scale dependence, with the BEF relationship increasing in the rising portion of the species area relationship, but then decreasing as it saturates. An analysis of data from the Cedar Creek grassland BEF experiment revealed a positive but saturating slope of the relationship with scale. Overall, our findings suggest that the BEF relationship is likely to be scale dependent.

scholarly journals 8-Day and Daily Maximum and Minimum Air Temperature Estimation via Machine Learning Method on a Climate Zone to Global Scale

2021 ◽  
Vol 13 (12) ◽  
pp. 2355
Author(s):  
Linglin Zeng ◽  
Yuchao Hu ◽  
Rui Wang ◽  
Xiang Zhang ◽  
Guozhang Peng ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Air Temperature ◽  
Spatial Scales ◽  
Model Performance ◽  
Global Scale ◽  
Scale Model ◽  
Validation Dataset ◽  
Daily Maximum ◽  
Climate Zone ◽  
Temporal And Spatial

Air temperature (Ta) is a required input in a wide range of applications, e.g., agriculture. Land Surface Temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) are widely used to estimate Ta. Previous studies of these products in Ta estimation, however, were generally applied in small areas and with a small number of meteorological stations. This study designed both temporal and spatial experiments to estimate 8-day and daily maximum and minimum Ta (Tmax and Tmin) on three spatial scales: climate zone, continental and global scales from 2009 to 2018, using the Random Forest (RF) method based on MODIS LST products and other auxiliary data. Factors contributing to the relation between LST and Ta were determined based on physical models and equations. Temporal and spatial experiments were defined by the rules of dividing the training and validation datasets for the RF method, in which the stations selected in the training dataset were all included or not in the validation dataset. The RF model was first trained and validated on each spatial scale, respectively. On a global scale, model accuracy with a determination coefficient (R2) > 0.96 and root mean square error (RMSE) < 1.96 °C and R2 > 0.95 and RMSE < 2.55 °C was achieved for 8-day and daily Ta estimations, respectively, in both temporal and spatial experiments. Then the model was trained and cross-validated on each spatial scale. The results showed that the data size and station distribution of the study area were the main factors influencing the model performance at different spatial scales. Finally, the spatial patterns of the model performance and variable importance were analyzed. Both daytime and nighttime LST had a significant contribution in the 8-day Tmax estimation on all the three spatial scales; while their contribution in daily Tmax estimation varied over different continents or climate zones. This study was expected to improve our understanding of Ta estimation in terms of accuracy variations and influencing variables on different spatial and temporal scales. The future work mainly includes identifying underlying mechanisms of estimation errors and the uncertainty sources of Ta estimation from a local to a global scale.

scholarly journals The species–area relationship and evolution

2006 ◽  
Vol 241 (3) ◽  
pp. 590-600 ◽  
Author(s):  
Daniel Lawson ◽  
Henrik Jeldtoft Jensen
Keyword(s):  
Species Area ◽  
Species Area Relationship
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