scholarly journals Spatial scales of interannual recruitment variations of marine, anadromous, and freshwater fish

1997 ◽  
Vol 54 (6) ◽  
pp. 1400-1407 ◽  
Author(s):  
R A Myers ◽  
G Mertz ◽  
J Bridson
Keyword(s):  
Spatial Scale ◽  
Freshwater Fish ◽  
Marine Environment ◽  
Environmental Variables ◽  
Large Scale ◽  
Spatial Scales ◽  
Marine Species ◽  
Freshwater Species ◽  
Environmental Agents

We examine the spatial scale of variability in recruitment for 11 marine, three anadromous, and five freshwater species. Generally the spatial scale of recruitment correlations for marine species is approximately 500 km, compared with less than 50 km for freshwater; anadromous species fall between these two scales. The scale for marine species is comparable with (but less than) that of the largest-scale environmental variables (and is compatible with the idea that large-scale environmental agents influence recruitment). Our results are consistent with the hypothesis that predation is a more important factor in determining recruitment in freshwater than it is in the marine environment.

Endocrine disruption in marine fish

2003 ◽  
Vol 75 (11-12) ◽  
pp. 2249-2261 ◽  
Author(s):  
P. Matthiessen
Keyword(s):  
Freshwater Fish ◽  
Endocrine Disruption ◽  
Marine Fish ◽  
Marine Environment ◽  
Population Level ◽  
Marine Species ◽  
Future Research ◽  
Freshwater Species ◽  
Top Predators

This topic reviews the whole field of endocrine disruption (ED) in marine fish and compares this with our knowledge of the situation in freshwater species. In broad terms, similar types of ED have been observed in the two groups, although effects in the marine environment tend to be less marked, presumably due to dispersion and dilution. There are, however, some data which suggest that marine fish that are top-predators can experience ED due to biomagnification of organochlorines. Processes such as smoltification, metamorphosis, and hermaphroditism, which are common in some marine species, may be particularly susceptible to ED, but have as yet been scarcely studied. As with freshwater fish, firm links to population-level effects have not yet been demonstrated, although it is not unreasonable to suppose that they are occurring in some locations. The topic concludes with some recommendations for future research.

scholarly journals Scaling impacts on environmental controls and spatial heterogeneity of soil organic carbon stocks

2015 ◽  
Vol 12 (13) ◽  
pp. 3993-4004 ◽  
Author(s):  
U. Mishra ◽  
W. J. Riley
Keyword(s):  
Environmental Factors ◽  
Organic Carbon ◽  
Spatial Structure ◽  
Spatial Heterogeneity ◽  
Spatial Scale ◽  
Environmental Variables ◽  
Spatial Scales ◽  
Statistical Properties ◽  
Environmental Controls ◽  
Soc Stocks

Abstract. The spatial heterogeneity of land surfaces affects energy, moisture, and greenhouse gas exchanges with the atmosphere. However, representing the heterogeneity of terrestrial hydrological and biogeochemical processes in Earth system models (ESMs) remains a critical scientific challenge. We report the impact of spatial scaling on environmental controls, spatial structure, and statistical properties of soil organic carbon (SOC) stocks across the US state of Alaska. We used soil profile observations and environmental factors such as topography, climate, land cover types, and surficial geology to predict the SOC stocks at a 50 m spatial scale. These spatially heterogeneous estimates provide a data set with reasonable fidelity to the observations at a sufficiently high resolution to examine the environmental controls on the spatial structure of SOC stocks. We upscaled both the predicted SOC stocks and environmental variables from finer to coarser spatial scales (s = 100, 200, and 500 m and 1, 2, 5, and 10 km) and generated various statistical properties of SOC stock estimates. We found different environmental factors to be statistically significant predictors at different spatial scales. Only elevation, temperature, potential evapotranspiration, and scrub land cover types were significant predictors at all scales. The strengths of control (the median value of geographically weighted regression coefficients) of these four environmental variables on SOC stocks decreased with increasing scale and were accurately represented using mathematical functions (R2 = 0.83–0.97). The spatial structure of SOC stocks across Alaska changed with spatial scale. Although the variance (sill) and unstructured variability (nugget) of the calculated variograms of SOC stocks decreased exponentially with scale, the correlation length (range) remained relatively constant across scale. The variance of predicted SOC stocks decreased with spatial scale over the range of 50 m to ~ 500 m, and remained constant beyond this scale. The fitted exponential function accounted for 98 % of variability in the variance of SOC stocks. We found moderately accurate linear relationships between mean and higher-order moments of predicted SOC stocks (R2 ∼ 0.55–0.63). Current ESMs operate at coarse spatial scales (50–100 km), and are therefore unable to represent environmental controllers and spatial heterogeneity of high-latitude SOC stocks consistent with observations. We conclude that improved understanding of the scaling behavior of environmental controls and statistical properties of SOC stocks could improve ESM land model benchmarking and perhaps allow representation of spatial heterogeneity of biogeochemistry at scales finer than those currently resolved by ESMs.

scholarly journals Scaling impacts on environmental controls and spatial heterogeneity of soil organic carbon stocks

2015 ◽  
Vol 12 (2) ◽  
pp. 1721-1751 ◽  
Author(s):  
U. Mishra ◽  
W. J. Riley
Keyword(s):  
Environmental Factors ◽  
Organic Carbon ◽  
Spatial Structure ◽  
Spatial Heterogeneity ◽  
Spatial Scale ◽  
Environmental Variables ◽  
Spatial Scales ◽  
Statistical Properties ◽  
Environmental Controls ◽  
Soc Stocks

Abstract. The spatial heterogeneity of land surfaces affects energy, moisture, and greenhouse gas exchanges with the atmosphere. However, representing heterogeneity of terrestrial hydrological and biogeochemical processes in earth system models (ESMs) remains a critical scientific challenge. We report the impact of spatial scaling on environmental controls, spatial structure, and statistical properties of soil organic carbon (SOC) stocks across the US state of Alaska. We used soil profile observations and environmental factors such as topography, climate, land cover types, and surficial geology to predict the SOC stocks at a 50 m spatial scale. These spatially heterogeneous estimates provide a dataset with reasonable fidelity to the observations at a sufficiently high resolution to examine the environmental controls on the spatial structure of SOC stocks. We upscaled both the predicted SOC stocks and environmental variables from finer to coarser spatial scales (s = 100, 200, 500 m, 1, 2, 5, 10 km) and generated various statistical properties of SOC stock estimates. We found different environmental factors to be statistically significant predictors at different spatial scales. Only elevation, temperature, potential evapotranspiration, and scrub land cover types were significant predictors at all scales. The strengths of control (the median value of geographically weighted regression coefficients) of these four environmental variables on SOC stocks decreased with increasing scale and were accurately represented using mathematical functions (R2 = 0.83–0.97). The spatial structure of SOC stocks across Alaska changed with spatial scale. Although the variance (sill) and unstructured variability (nugget) of the calculated variograms of SOC stocks decreased exponentially with scale, the correlation length (range) remained relatively constant across scale. The variance of predicted SOC stocks decreased with spatial scale over the range of 50 to ~ 500 m, and remained constant beyond this scale. The fitted exponential function accounted for 98% of variability in the variance of SOC stocks. We found moderately-accurate linear relationships between mean and higher-order moments of predicted SOC stocks (R2 ~ 0.55–0.63). Current ESMs operate at coarse spatial scales (50–100 km), and are therefore unable to represent environmental controllers and spatial heterogeneity of high-latitude SOC stocks consistent with observations. We conclude that improved understanding of the scaling behavior of environmental controls and statistical properties of SOC stocks can improve ESM land model benchmarking and perhaps allow representation of spatial heterogeneity of biogeochemistry at scales finer than those currently resolved by ESMs.

scholarly journals Population genetics and connectivity in Paphies subtriangulata and Paphies australis (Bivalvia: Mesodesmatidae)

2021 ◽  
Author(s):  
◽  
Danielle Amelia Hannan
Keyword(s):  
Population Structure ◽  
New Zealand ◽  
Marine Environment ◽  
Environmental Variables ◽  
Marine Species ◽  
Genetic Population Structure ◽  
Small Scale ◽  
Genetic Population ◽  
Geographic Patterns ◽  
Coastal Marine

<p>Understanding the different types of genetic population structure that characterise marine species, and the processes driving such patterns, is crucial for establishing links between the ecology and evolution of a species. This knowledge is vital for management and conservation of marine species. Genetic approaches are a powerful tool for revealing ecologically relevant insights to marine population dynamics. Geographic patterns of genetic population structure are largely determined by the rate at which individuals are exchanged among populations (termed ‘population connectivity’), which in turn is influenced by conditions in the physical environment. The complexity of the New Zealand marine environment makes it difficult to predict how physical oceanographic and environmental processes will influence connectivity in coastal marine organisms and hence the type of genetic structure that will form. This complexity presents a challenge for management of marine resources but also makes the New Zealand region an interesting model system to investigate how and why population structure develops and evolves over time. Paphies subtriangulata (tuatua) and P. australis (pipi) are endemic bivalve ‘surf clams’ commonly found on New Zealand surf beaches and harbour/estuary environments, respectively. They form important recreational, customary and commercial fisheries, yet little is known about the stock structure of these species. This study aimed to use genetic techniques to determine population structure, levels of connectivity and ‘seascape’ genetic patterns in P. subtriangulata and P. australis, and to gain further knowledge of common population genetic processes operating in the New Zealand coastal marine environment. Eleven and 14 novel microsatellite markers were developed for P. subtriangulata and P. australis, respectively. Samples were collected from 10 locations for P. subtriangulata and 13 locations for P. australis (35-57 samples per location; total sample size of 517 for P. subtriangulata and 674 for P. australis). Geographic patterns of genetic variation were measured and rates of migration among locations were estimated on recent and historic time scales. Both species were characterised by genetic population structure that was consistent with their habitat. For P. subtriangulata, the Chatham Island population was strongly differentiated from the rest of the sampled locations. The majority of mainland locations were undifferentiated and estimated rates of migration among locations were high on both time scales investigated, although differentiation among some populations was observed. For P. australis, an overall isolation by distance (IBD) pattern was likely to be driven by distance between discrete estuary habitats. However, it was difficult to distinguish IBD from hierarchical structure as populations could be further subdivided into three significantly differentiated groups (Northern, South Eastern and South Western), providing evidence for barriers to dispersal. Further small scale patterns of genetic differentiation were observed in some locations, suggesting that complex current patterns and high self-recruitment drive small scale genetic population structure in both P. subtriangulata and P. australis. These patterns of genetic variation were used in seascape genetic analyses to test for associations with environmental variables, with the purpose of understanding the processes that might shape genetic population structure in these two species. Although genetic population structure varied between the two species, common physical and environmental variables (geographic distance, sea surface temperature, bed slope, tidal currents) are likely to be involved in the structuring of populations. Results suggest that local adaptation, in combination with restricted dispersal, could play a role in driving the small scale patterns of genetic differentiation seen among some localities. Overall, the outcomes of this research fill a gap in our knowledge about the rates and routes by which populations are connected and the environmental factors influencing such patterns in the New Zealand marine environment. Other studies have highlighted the importance of using multi-faceted approaches to understand complex processes operating in the marine environment. The present study is an important first step in this direction as these methods are yet to be widely applied to New Zealand marine species. Importantly, this study used a comparative approach, applying standardised methodology to compare genetic population structure and migration across species. Such an approach is necessary if we wish to build a robust understanding of the spatial and temporal complexities of population dynamics in the New Zealand coastal marine environment, and to develop effective management strategies for our unique marine species.</p>

Characteristic Spatial Scales of Particle Clusters Formed in Gas-Solid Flow

2007 ◽  
Author(s):  
T. Tsuji ◽  
A. Ito ◽  
T. Tanaka
Keyword(s):  
Spatial Scale ◽  
Large Scale ◽  
Deterministic Model ◽  
Spatial Scales ◽  
Direct Simulation Monte Carlo ◽  
Domain Size ◽  
Three Dimensions ◽  
Wave Number ◽  
Particle Collision ◽  
Study Results

Spatial scale characteristics of particle clusters are investigated by directly performing Fourier transform of spatial particle concentration distributions. Flow field data are obtained by large-scale Eulerian / Lagrangian simulations. All calculations are performed in three-dimensions and more than sixteen million particles are tracked in the maximum case. The inter-particle collision plays an important role for the development of particle clusters. In this study, results obtained by using the stochastic model based on direct simulation Monte Carlo (DSMC) are compared with that by the deterministic model. The results obtained by DSMC method agree quantitatively with deterministic model. Particle clusters consist of multiple-spatial scale components and the low wave-number, hence large-scale structure, is dominant. Dependency on the domain size and resolution is also investigated in detail.

scholarly journals Observed small spatial scale and seasonal variability of the CO<sub>2</sub> system in the Southern Ocean

2014 ◽  
Vol 11 (1) ◽  
pp. 75-90 ◽  
Author(s):  
L. Resplandy ◽  
J. Boutin ◽  
L. Merlivat
Keyword(s):  
Southern Ocean ◽  
Spatial Scale ◽  
Large Scale ◽  
Carbon Budget ◽  
Spatial Scales ◽  
Mixed Layer Depth ◽  
Small Scale ◽  
Spatiotemporal Resolution ◽  
Small Spatial Scale ◽  
Data Set

Abstract. The considerable uncertainties in the carbon budget of the Southern Ocean are largely attributed to unresolved variability, in particular at a seasonal timescale and small spatial scale (~ 100 km). In this study, the variability of surface pCO2 and dissolved inorganic carbon (DIC) at seasonal and small spatial scales is examined using a data set of surface drifters including ~ 80 000 measurements at high spatiotemporal resolution. On spatial scales of 100 km, we find gradients ranging from 5 to 50 μatm for pCO2 and 2 to 30 μmol kg−1 for DIC, with highest values in energetic and frontal regions. This result is supported by a second estimate obtained with sea surface temperature (SST) satellite images and local DIC–SST relationships derived from drifter observations. We find that dynamical processes drive the variability of DIC at small spatial scale in most regions of the Southern Ocean and the cascade of large-scale gradients down to small spatial scales, leading to gradients up to 15 μmol kg−1 over 100 km. Although the role of biological activity is more localized, it enhances the variability up to 30 μmol kg−1 over 100 km. The seasonal cycle of surface DIC is reconstructed following Mahadevan et al. (2011), using an annual climatology of DIC and a monthly climatology of mixed layer depth. This method is evaluated using drifter observations and proves to be a reasonable first-order estimate of the seasonality in the Southern Ocean that could be used to validate model simulations. We find that small spatial-scale structures are a non-negligible source of variability for DIC, with amplitudes of about a third of the variations associated with the seasonality and up to 10 times the magnitude of large-scale gradients. The amplitude of small-scale variability reported here should be kept in mind when inferring temporal changes (seasonality, interannual variability, decadal trends) of the carbon budget from low-resolution observations and models.

scholarly journals Population genetics and connectivity in Paphies subtriangulata and Paphies australis (Bivalvia: Mesodesmatidae)

2021 ◽  
Author(s):  
◽  
Danielle Amelia Hannan
Keyword(s):  
Population Structure ◽  
New Zealand ◽  
Marine Environment ◽  
Environmental Variables ◽  
Marine Species ◽  
Genetic Population Structure ◽  
Small Scale ◽  
Genetic Population ◽  
Geographic Patterns ◽  
Coastal Marine

<p>Understanding the different types of genetic population structure that characterise marine species, and the processes driving such patterns, is crucial for establishing links between the ecology and evolution of a species. This knowledge is vital for management and conservation of marine species. Genetic approaches are a powerful tool for revealing ecologically relevant insights to marine population dynamics. Geographic patterns of genetic population structure are largely determined by the rate at which individuals are exchanged among populations (termed ‘population connectivity’), which in turn is influenced by conditions in the physical environment. The complexity of the New Zealand marine environment makes it difficult to predict how physical oceanographic and environmental processes will influence connectivity in coastal marine organisms and hence the type of genetic structure that will form. This complexity presents a challenge for management of marine resources but also makes the New Zealand region an interesting model system to investigate how and why population structure develops and evolves over time. Paphies subtriangulata (tuatua) and P. australis (pipi) are endemic bivalve ‘surf clams’ commonly found on New Zealand surf beaches and harbour/estuary environments, respectively. They form important recreational, customary and commercial fisheries, yet little is known about the stock structure of these species. This study aimed to use genetic techniques to determine population structure, levels of connectivity and ‘seascape’ genetic patterns in P. subtriangulata and P. australis, and to gain further knowledge of common population genetic processes operating in the New Zealand coastal marine environment. Eleven and 14 novel microsatellite markers were developed for P. subtriangulata and P. australis, respectively. Samples were collected from 10 locations for P. subtriangulata and 13 locations for P. australis (35-57 samples per location; total sample size of 517 for P. subtriangulata and 674 for P. australis). Geographic patterns of genetic variation were measured and rates of migration among locations were estimated on recent and historic time scales. Both species were characterised by genetic population structure that was consistent with their habitat. For P. subtriangulata, the Chatham Island population was strongly differentiated from the rest of the sampled locations. The majority of mainland locations were undifferentiated and estimated rates of migration among locations were high on both time scales investigated, although differentiation among some populations was observed. For P. australis, an overall isolation by distance (IBD) pattern was likely to be driven by distance between discrete estuary habitats. However, it was difficult to distinguish IBD from hierarchical structure as populations could be further subdivided into three significantly differentiated groups (Northern, South Eastern and South Western), providing evidence for barriers to dispersal. Further small scale patterns of genetic differentiation were observed in some locations, suggesting that complex current patterns and high self-recruitment drive small scale genetic population structure in both P. subtriangulata and P. australis. These patterns of genetic variation were used in seascape genetic analyses to test for associations with environmental variables, with the purpose of understanding the processes that might shape genetic population structure in these two species. Although genetic population structure varied between the two species, common physical and environmental variables (geographic distance, sea surface temperature, bed slope, tidal currents) are likely to be involved in the structuring of populations. Results suggest that local adaptation, in combination with restricted dispersal, could play a role in driving the small scale patterns of genetic differentiation seen among some localities. Overall, the outcomes of this research fill a gap in our knowledge about the rates and routes by which populations are connected and the environmental factors influencing such patterns in the New Zealand marine environment. Other studies have highlighted the importance of using multi-faceted approaches to understand complex processes operating in the marine environment. The present study is an important first step in this direction as these methods are yet to be widely applied to New Zealand marine species. Importantly, this study used a comparative approach, applying standardised methodology to compare genetic population structure and migration across species. Such an approach is necessary if we wish to build a robust understanding of the spatial and temporal complexities of population dynamics in the New Zealand coastal marine environment, and to develop effective management strategies for our unique marine species.</p>

scholarly journals Evoked pain intensity representation is distributed across brain systems: A multistudy mega-analysis

2020 ◽  
Author(s):  
Bogdan Petre ◽  
Philip Kragel ◽  
Lauren Y. Atlas ◽  
Stephan Geuter ◽  
Marieke Jepma ◽  
...  
Keyword(s):  
Pain Intensity ◽  
Spatial Scale ◽  
Data Storage ◽  
Large Scale ◽  
Model Comparison ◽  
Spatial Scales ◽  
Meta Analysis ◽  
Individual Subject ◽  
System Models ◽  
Elementary Region

ABSTRACTInformation is coded in the brain at different scales for different phenomena: locally, distributed across regions and networks, and globally. For pain, the scale of representation is controversial. Although generally believed to be an integrated cognitive and sensory phenomenon implicating diverse brain systems, quantitative characterizations of which regions and networks are sufficient to represent pain are lacking. In this meta-analysis (or mega-analysis) using data from 289 participants across 10 studies, we use model comparison combined with multivariate predictive models to investigate the spatial scale and location of acute pain representation. We compare models based on (a) a single most pain-predictive module, either previously identified elementary regions or a single best large-scale cortical resting-state network module; (b) selected cortical-subcortical systems related to evoked pain in prior literature (‘multi-system models’); and (c) a model spanning the full brain. We estimate the accuracy of pain intensity predictions using cross validation (7 studies) and subsequently validate in three independent holdout studies. All spatial scales convey information about pain intensity, but distributed, multi-system models better characterize pain representations than any individual region or network (e.g. multisystem models explain >20% more of individual subject pain ratings than the best elementary region). Full brain models showed no predictive advantage over multi-system models. These findings quantify the extent that representation of evoked pain experience is distributed across multiple cortical and subcortical systems, show that pain representation is not circumscribed by any elementary region or conical network, and provide a blueprint for identifying the spatial scale of information in other domains.Significance StatementWe define modular, multisystem and global views of brain function, use multivariate fMRI decoding to characterize pain representations at each level, and provide evidence for a multisystem representation of evoked pain. We further show that local views necessarily exclude important components of pain representation, while a global full brain representation is superfluous, even though both are viable frameworks for representing pain. These findings quantitatively juxtapose and reconcile divergent conclusions from evoked pain studies within a generalized neuroscientific framework, and provide a blueprint for investigating representational architecture for diverse brain processes.Author NoteData storage supported by the University of Colorado Boulder “PetaLibrary”. Research funded by NIMH R01 MH076136, NIDA R01 DA046064 and NIDA R01 DA035484. Lauren Atlas is supported in part by funding from the Intramural Research Program of the National Center for Complementary and Integrative Health, National Institutes of Health (ZIA-AT000030). Marina Lopez-Sola is supported by a Serra Hunter fellow lecturer program. We would like to thank Dr. Christian Buchel for contributing data to this project, and Dr. Marta Čeko for comments and feedback on the manuscript.

scholarly journals Spatial Scale of Geomagnetic Pc5/pi3 Pulsations as a Factor of Their Efficiency in Generation of Geomagnetically Induced Currents

2020 ◽  
Author(s):  
Nadezda V. Yagova ◽  
Vyacheslav Pilipenko ◽  
Yaroslav Sakharov ◽  
Vasily Selivanov
Keyword(s):  
Electric Power ◽  
Spatial Scale ◽  
Large Scale ◽  
Power Transmission ◽  
Spatial Scales ◽  
Power Line ◽  
Small Scale ◽  
Geomagnetically Induced Currents ◽  
Electric Power Line ◽  
Induced Currents

Abstract Geomagnetically induced currents (GICs) in a meridional power transmission line at the Kola Peninsula are analyzed during the intervals of Pc5/Pi3 (frequency range from 1.5 to 5 mHz) pulsation activity observed at the IMAGE magnetometer network. We have analyzed GIC in a transformer at the terminal station Vykhodnoj (68◦N, 33◦E) during the entire year of 2015, near the maximum of 24-th Solar cycle. To quantify the efficiency of GIC generation by a geomagnetic pulsation, a ratio between power spectral densities of GIC and magnetic field variations is introduced. Upon examination of the efficiency of geomagnetic pulsations in GIC generation, the emphasis is given to its dependence on frequency and spatial scale. To estimate pulsation spatial scales in latitudinal and longitudinal directions, the triangle of stations KEV-SOD-KIL has been used. Large-scale pulsations along the electric power line (with a high spectral coherence, low phase difference, and similar amplitudes) are found to be more effective in GIC generation than small-scale pulsations. The accuracy of GIC prediction also depends on the pulsation scale transversal to the electric power line.

scholarly journals Spatial scale changes the relationship between beta diversity, species richness and latitude

2018 ◽  
Vol 5 (9) ◽  
pp. 181168 ◽  
Author(s):  
Rachakonda Sreekar ◽  
Masatoshi Katabuchi ◽  
Akihiro Nakamura ◽  
Richard T. Corlett ◽  
J. W. Ferry Slik ◽  
...  
Keyword(s):  
Species Richness ◽  
Spatial Scale ◽  
Large Scale ◽  
Spatial Scales ◽  
Asia Pacific ◽  
Β Diversity ◽  
Sample Representativeness ◽  
Tree Communities ◽  
Tree Data ◽  
The Relationship

The relationship between β-diversity and latitude still remains to be a core question in ecology because of the lack of consensus between studies. One hypothesis for the lack of consensus between studies is that spatial scale changes the relationship between latitude and β-diversity. Here, we test this hypothesis using tree data from 15 large-scale forest plots (greater than or equal to 15 ha, diameter at breast height ≥ 1 cm) across a latitudinal gradient (3–30 o ) in the Asia-Pacific region. We found that the observed β-diversity decreased with increasing latitude when sampling local tree communities at small spatial scale (grain size ≤0.1 ha), but the observed β-diversity did not change with latitude when sampling at large spatial scales (greater than or equal to 0.25 ha). Differences in latitudinal β-diversity gradients across spatial scales were caused by pooled species richness (γ-diversity), which influenced observed β-diversity values at small spatial scales, but not at large spatial scales. Therefore, spatial scale changes the relationship between β-diversity, γ-diversity and latitude, and improving sample representativeness avoids the γ-dependence of β-diversity.

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