Small spatial scale effects of wave action exposure on morphological traits of the limpet Lottia subrugosa

2019 ◽  
Vol 99 (06) ◽  
pp. 1309-1315
Author(s):  
Edson A. Vieira ◽  
Marília Bueno
Keyword(s):  
Spatial Scale ◽  
Scale Effects ◽  
Spatial Scales ◽  
Wave Exposure ◽  
Wave Action ◽  
Shell Shape ◽  
Small Scale ◽  
Small Spatial Scale ◽  
Large Spatial Scale ◽  
Exposed Shore

AbstractMany studies have already assessed how wave action may affect morphology of intertidal species among sites that vary in wave exposure, but few attempted to look to this issue in smaller scales. Using the most common limpet of the Brazilian coast, Lottia subrugosa, and assuming position on rocky boulders as a proxy for wave action at small scale, we tested the hypothesis that waves may also influence limpet morphology at a smaller spatial scale by investigating how individual size, foot area and shell shape vary between sheltered and exposed boulder sides on three shores in the coast of Ubatuba, Brazil. Limpets consistently showed a proportionally larger foot on exposed boulder sides for all shores, indicating that stronger attachment is an important mechanism to deal with wave action dislodgement at a smaller scale. Shell shape also varied in the scale investigated here, with more conical (dissipative) shells occurring in exposed boulder sides in one exposed shore across time and in the other exposed shore in one year. Shell shape did not vary regarding boulder sides across time in the most sheltered shore. Although we did not assess large spatial scale effects of wave action in this study, variations of the effect of waves at small spatial scale observed for shell shape suggest that it may be modulated by the local wave exposure regime. Our work highlights the importance of wave action at small spatial scales, and may help to understand the ecological variability of limpets inhabiting rocky shores.

scholarly journals Relationship between small-scale catch-per-unit-effort and abundance in New Zealand abalone (pāua, Haliotis iris) fisheries

2015 ◽  
Author(s):  
Edward R Abraham ◽  
Philipp Neubauer
Keyword(s):  
Spatial Scale ◽  
Spatial Information ◽  
Spatial Scales ◽  
Small Scale ◽  
Gps Data ◽  
Catch Per Unit Effort ◽  
Small Spatial Scale ◽  
Unit Effort ◽  
Data Loggers ◽  
Gps Data Loggers

Catch-per-unit-effort (CPUE) is commonly used as an index of abundance in fishery stock assessments, but CPUE may be misleading, as a number of global fishery collapses have been attributed to a hyper-stable CPUE. In abalone (Halitidae family) fisheries, CPUE at large spatial scales may be hyper-stable because of aggregating behaviour and serial-depletion, whereby fishers sequentially fish areas with no corresponding decline in CPUE. Obtaining detailed spatial information in abalone fisheries might mitigate this problem, allowing CPUE to be used more confidently in these fisheries. Here, we report on the use of newly-developed high-resolution Global Positioning System (GPS) data loggers in New Zealand's blacklip abalone (pāua, Haliotis iris) fisheries. Using these data loggers, we tested, via a fish-down experiment, if CPUE is a reliable indicator of abundance at a small spatial scale and over a period of months. In the experiment, hyper-stability at small spatial scales occurred at high abundance, but CPUE reflected the estimated depletion level at the end of experimental fishing. This experiment suggests that the GPS data loggers provide a promising avenue to track CPUE at a small spatial scale, and to assess spatial resource use in New Zealand's pāua fisheries.

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 Relationship between small-scale catch-per-unit-effort and abundance in New Zealand abalone (pāua, Haliotis iris) fisheries

2015 ◽  
Author(s):  
Edward R Abraham ◽  
Philipp Neubauer
Keyword(s):  
Spatial Scale ◽  
Spatial Information ◽  
Spatial Scales ◽  
Small Scale ◽  
Gps Data ◽  
Catch Per Unit Effort ◽  
Small Spatial Scale ◽  
Unit Effort ◽  
Data Loggers ◽  
Gps Data Loggers

Catch-per-unit-effort (CPUE) is commonly used as an index of abundance in fishery stock assessments, but CPUE may be misleading, as a number of global fishery collapses have been attributed to a hyper-stable CPUE. In abalone (Halitidae family) fisheries, CPUE at large spatial scales may be hyper-stable because of aggregating behaviour and serial-depletion, whereby fishers sequentially fish areas with no corresponding decline in CPUE. Obtaining detailed spatial information in abalone fisheries might mitigate this problem, allowing CPUE to be used more confidently in these fisheries. Here, we report on the use of newly-developed high-resolution Global Positioning System (GPS) data loggers in New Zealand's blacklip abalone (pāua, Haliotis iris) fisheries. Using these data loggers, we tested, via a fish-down experiment, if CPUE is a reliable indicator of abundance at a small spatial scale and over a period of months. In the experiment, hyper-stability at small spatial scales occurred at high abundance, but CPUE reflected the estimated depletion level at the end of experimental fishing. This experiment suggests that the GPS data loggers provide a promising avenue to track CPUE at a small spatial scale, and to assess spatial resource use in New Zealand's pāua fisheries.

scholarly journals Relationship between small-scale catch-per-unit-effort and abundance in New Zealand abalone (pāua, Haliotis iris) fisheries

2015 ◽  
Author(s):  
Edward R Abraham ◽  
Philipp Neubauer
Keyword(s):  
Spatial Scale ◽  
Spatial Information ◽  
Spatial Scales ◽  
Small Scale ◽  
Gps Data ◽  
Catch Per Unit Effort ◽  
Small Spatial Scale ◽  
Unit Effort ◽  
Data Loggers ◽  
Gps Data Loggers

Catch-per-unit-effort (CPUE) is commonly used as an index of abundance in fishery stock assessments, but CPUE may be misleading, as a number of global fishery collapses have been attributed to a hyper-stable CPUE. In abalone (Halitidae family) fisheries, CPUE at large spatial scales may be hyper-stable because of aggregating behaviour and serial-depletion, whereby fishers sequentially fish areas with no corresponding decline in CPUE. Obtaining detailed spatial information in abalone fisheries might mitigate this problem, allowing CPUE to be used more confidently in these fisheries. Here, we report on the use of newly-developed high-resolution Global Positioning System (GPS) data loggers in New Zealand's blacklip abalone (pāua, Haliotis iris) fisheries. Using these data loggers, we tested, via a fish-down experiment, if CPUE is a reliable indicator of abundance at a small spatial scale and over a period of months. In the experiment, hyper-stability at small spatial scales occurred at high abundance, but CPUE reflected the estimated depletion level at the end of experimental fishing. This experiment suggests that the GPS data loggers provide a promising avenue to track CPUE at a small spatial scale, and to assess spatial resource use in New Zealand's pāua fisheries.

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

2013 ◽  
Vol 10 (8) ◽  
pp. 13855-13895 ◽  
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 ◽  
Order Estimate ◽  
Small Spatial Scale ◽  
Spatio Temporal

Abstract. The considerable uncertainties in the carbon budget of the Southern Ocean are largely attributed to unresolved variability, in particular at seasonal time scale and small spatial scale (~ 100 km). In this study, the variability of surface pCO2 and DIC at seasonal and small-spatial scales is examined using a dataset of surface drifters including ~ 80 000 measurements at high spatio-temporal resolution. On spatial scales of 100 km, we find gradients ranging from 5 to 50 μ atm for pCO2 and 2 to 30 μ mol kg−1 for DIC, with highest values in energetic and frontal regions. This result is supported by a second estimate obtained with SST satellite images and local DIC/SST relationships derived from drifters observations. We find that dynamical processes drives the variability of DIC at small spatial scale in most regions of the Southern Ocean, the cascade of large-scale gradients down to small spatial scales leading to gradients up to 15 μ mol kg−1 over 100 km. Although the role of biological activity is more localized, it enhances the variability up to 30 μ mol 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 drifters observations and proves to be a reasonable first-order estimate of the seasonality in the Southern Ocean, which could be used to validate models simulations. We find that small spatial scales 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, inter-annual variability, decadal trends) of the carbon budget from low resolution observations and models.

Meiofauna constitute a considerable portion of invertebrate drift among moss-rich patches within a karst hydrosystem

Biologia ◽  
2014 ◽  
Vol 69 (3) ◽  
Author(s):  
Mirela Perić ◽  
Tvrtko Dražina ◽  
Maria Špoljar ◽  
Ines Radanović ◽  
Biserka Primc ◽  
...  
Keyword(s):  
Flow Velocity ◽  
Spatial Scale ◽  
Early Summer ◽  
Small Scale ◽  
Invertebrate Drift ◽  
Flow Conditions ◽  
Small Spatial Scale ◽  
Minor Influence ◽  
Drift Density ◽  
A Minor

AbstractAiming to establish the most frequent invertebrate taxa in drift at the small spatial scale within a moss-rich karst tufa-precipitating hydrosystem, we sampled drift among microhabitats differing in substratum type and flow conditions along a tufa barrier-cascading lotic reach. Additionally, we addressed the question of the contribution and the potential significance of meiofauna within the overall invertebrate drift at the small spatial scale. During the study period, a total of 60 invertebrate taxa were recorded in the drift. Six of these taxa belonged to the annelid/arthropod meiofauna and they represented 35% of total drift density. Macroinvertebrates found in drift were represented mainly by larval insects. The composition of the most abundant taxa in total drift was as follows: Alona spp. (Cladocera 26.7%), Riolus spp. (Coleoptera: Elmidae 13.2%), Simulium spp. (Diptera: Simuliidae 12.2%), Enchytraeidae (Oligochaeta 10.4%), Hydrachnidia (6.3%), Orthocladinae (Diptera: Chironomidae 3.9%) and Naididae (Oligochaeta 3.6%). Faunal drift densities and amounts of transported particulate matter (PM) were highest at the fast-flowing sites located at the barriers and lowest at the slow-flowing sites within pools. Similarly to the seasonal amounts of transported PM, faunal drift was lowest in winter, and peaked in autumn and in late spring/early summer. Correlation between flow velocity and PM-faunal drift densities suggested a significant effect of the dislodged PM, though a minor influence of discharge and flow velocity on faunal drift. We suggest that the small-scale habitat heterogeneity and the respective feeding and refugial strategies of the fauna, as well as faunal passive dislodgement initiated by the shear forces of the flow were the most important drivers of observed drift patterns.

scholarly journals Improving the Resolution of GRACE Data for Spatio-Temporal Groundwater Storage Assessment

2021 ◽  
Vol 13 (17) ◽  
pp. 3513
Author(s):  
Shoaib Ali ◽  
Dong Liu ◽  
Qiang Fu ◽  
Muhammad Jehanzeb Masud Cheema ◽  
Quoc Bao Pham ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Regional Scale ◽  
Water Storage ◽  
Slope Aspect ◽  
Small Scale ◽  
Ann Model ◽  
Groundwater Storage ◽  
Large Spatial Scale ◽  
Grace Data ◽  
Hydrological Variables

Groundwater has a significant contribution to water storage and is considered to be one of the sources for agricultural irrigation; industrial; and domestic water use. The Gravity Recovery and Climate Experiment (GRACE) satellite provides a unique opportunity to evaluate terrestrial water storage (TWS) and groundwater storage (GWS) at a large spatial scale. However; the coarse resolution of GRACE limits its ability to investigate the water storage change at a small scale. It is; therefore; needed to improve the resolution of GRACE data at a spatial scale applicable for regional-level studies. In this study; a machine-learning-based downscaling random forest model (RFM) and artificial neural network (ANN) model were developed to downscale GRACE data (TWS and GWS) from 1° to a higher resolution (0.25°). The spatial maps of downscaled TWS and GWS were generated over the Indus basin irrigation system (IBIS). Variations in TWS of GRACE in combination with geospatial variables; including digital elevation model (DEM), slope; aspect; and hydrological variables; including soil moisture; evapotranspiration; rainfall; surface runoff; canopy water; and temperature; were used. The geospatial and hydrological variables could potentially contribute to; or correlate with; GRACE TWS. The RFM outperformed the ANN model and results show Pearson correlation coefficient (R) (0.97), root mean square error (RMSE) (11.83 mm), mean absolute error (MAE) (7.71 mm), and Nash–Sutcliffe efficiency (NSE) (0.94) while comparing with the training dataset from 2003 to 2016. These results indicate the suitability of RFM to downscale GRACE data at a regional scale. The downscaled GWS data were analyzed; and we observed that the region has lost GWS of about −9.54 ± 1.27 km3 at the rate of −0.68 ± 0.09 km3/year from 2003 to 2016. The validation results showed that R between downscaled GWS and observational wells GWS are 0.67 and 0.77 at seasonal and annual scales with a confidence level of 95%, respectively. It can; therefore; be concluded that the RFM has the potential to downscale GRACE data at a spatial scale suitable to predict GWS at regional scales.

scholarly journals seNorge_2018, daily precipitation and temperature datasets over Norway

2019 ◽  
Author(s):  
Cristian Lussana ◽  
Ole Einar Tveito ◽  
Andreas Dobler ◽  
Ketil Tunheim
Keyword(s):  
Spatial Scale ◽  
Climate Model ◽  
Spatial Scales ◽  
Small Scale ◽  
Total Precipitation ◽  
Climate Data ◽  
Wide Range ◽  
Analysis Quality ◽  
Station Density ◽  
Daily Total

Abstract. seNorge_2018 is a collection of observational gridded datasets over Norway for: daily total precipitation; daily mean, maximum and minimum temperatures. The time period covers 1957 to 2017, and the data are presented over a high-resolution terrain-following grid with 1 km spacing in both meridional and zonal directions. The seNorge family of observational gridded datasets developed at the Norwegian Meteorological Institute (MET Norway) has a twenty-year long history and seNorge_2018 is its newest member, the first providing daily minimum and maximum temperatures. seNorge datasets are used for a wide range of applications in climatology, hydrology and meteorology. The observational dataset is based on MET Norway's climate data, which has been integrated by the European Climate Assessment and Dataset database. Two distinct statistical interpolation methods have been developed, one for temperature and the other for precipitation. They are both based on a spatial scale-separation approach where, at first, the analysis (i.e., predictions) at larger spatial scales are estimated. Subsequently they are used to infer the small-scale details down to a spatial scale comparable to the local observation density. Mean, maximum and minimum temperatures are interpolated separately, then physical consistency among them is enforced. For precipitation, in addition to observational data, the spatial interpolation makes use of information provided by a climate model. The analysis evaluation is based on cross-validation statistics and comparison with a previous seNorge version. The analysis quality is presented as a function of the local station density. We show that the occurrence of large errors in the analyses decays at an exponential rate with the increase in the station density. Temperature analyses over most of the domain are generally not affected by significant biases. However, during wintertime in data-sparse regions the analyzed minimum temperatures do have a bias between 2 °C and 3 °C. Minimum temperatures are more challenging to represent and large errors are more frequent than for maximum and mean temperatures. The precipitation analysis quality depends crucially on station density: the frequency of occurrence of large errors for intense precipitation is less than 5 % in data-dense regions, while it is approximately 30 % in data-sparse regions. he open-access datasets are available20for public download at: daily total precipitation (DOI: https://doi.org/10.5281/zenodo.2082320, Lussana, 2018b); daily mean (DOI: https://doi.org/10.5281/zenodo.2023997, Lussana, 2018c) , maximum (DOI: https://doi.org/10.5281/zenodo.2559372, Lussana, 2018e) and minimum (DOI: https://doi.org/10.5281/zenodo.2559354, Lussana, 2018d) temperatures.

Variation In Coping With EI Niño Droughts In Northern Costa Rica

2000 ◽  
Vol 22 (4) ◽  
pp. 15-19 ◽  
Author(s):  
Sarah Otterstrom
Keyword(s):  
Costa Rica ◽  
Spatial Scale ◽  
El Niño ◽  
El Nino ◽  
Small Scale ◽  
Small Country ◽  
Small Spatial Scale ◽  
Climate Event ◽  
The Caribbean ◽  
Small Scale Farmers

In a small country like Costa Rica, the same climate event can affect neighboring communities in very distinct ways. In the summer of 1998, following an intense El Niño-related drought, I set out to examine how this event had affected small-scale farmers across northern Costa Rica. Surprisingly, there were large differences in coping abilities between farmers of the Caribbean and Pacific regions despite the overall small spatial scale at which my research was conducted.

scholarly journals Towards a Wave Theory Interpretation of Time-Distance Helioseismology Data

2001 ◽  
Vol 203 ◽  
pp. 180-182
Author(s):  
A. C. Birch ◽  
A. G. Kosovichev
Keyword(s):  
Spatial Scale ◽  
Born Approximation ◽  
Acoustic Waves ◽  
Sound Speed ◽  
Large Scale ◽  
Small Scale ◽  
Ray Theory ◽  
Travel Times ◽  
Large Spatial Scale ◽  
Time Distance

Time-distance helioseismology, which measures the time for acoustic waves to travel between points on the solar surface, has been used to study small-scale three-dimensional features in the sun, for example active regions, as well as large-scale features, such as meridional flow, that are not accessible by standard global helioseismology. Traditionally, travel times have been interpreted using geometrical ray theory, which is not always a good approximation. In order to develop a wave interpretation of time-distance data we employ the first Born approximation, which takes into account finite-wavelength effects and is expected to provide more accurate inversion results. In the Born approximation, in contrast with ray theory, travel times are sensitive to perturbations to sound speed which are located off the ray path. In an example calculation of travel time perturbations due to sound speed perturbations that are functions only of depth, we see that that the Born and ray approximations agree when applied to perturbations with large spatial scale and that the ray approximation fails when applied to perturbations with small spatial scale.

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