Patterns of variability in early life traits of a Mediterranean coastal fish

Spawning dates and pelagic larval duration (PLD) are early life traits (ELT) crucial for understanding life cycles, properly assessing patterns of connectivity and gathering indications about patchiness or homogeneity of larval pools. Considering that little attention has been paid to spatial variability in these traits, we investigated variability of ELT from the analysis of otolith microstructure in the common two-banded sea bream Diplodus vulgaris. In the southwestern Adriatic Sea, along ~200 km of coast (∼1° in latitude, 41.2° to 40.2°N), variability of ELT was assessed at multiple spatial scales. Overall, PLD (ranging from 25 to 61 d) and spawning dates (October 2009 to February 2010) showed significant variability at small scales (i.e. <6 km), but not at larger scales. These outcomes suggest patchiness of the larval pool at small spatial scales. Multiple causal processes underlying the observed variability are discussed, along with the need to properly consider spatial variability in ELT, for example when delineating patterns of connectivity. Copyright © 2013 Inter-Research

Pink landscapes: 1/f spectra of spatial environmental variability and bird community composition

Temporal and spatial environmental variability are predicted to have reddened spectra that reveal increases in variance with the period or length sampled. However, spectral analyses have seldom been performed on ecological data to determine whether these predictions hold true in the case of spatial environmental variability. For a 50 km long continuous transect of 128 point samples across a heterogeneous cultural landscape in the Czech Republic, both habitat composition and bird species composition decomposed by standard ordination techniques did indeed exhibit reddened spectra. The values of main ordination axes have relationships between log spectral density and log frequency with slopes close to -1, indicating 1/f, or 'pink' noise type of variability that is characterized by scale invariance. However, when habitat composition was controlled for and only residuals for bird species composition were analysed, the spectra revealed a peak at intermediate frequencies, indicating that population processes that structure bird communities but are not directly related to the structure of the environment might have some typical correlation length. Spatial variability of abundances of individual species was mostly reddened as well, but the degree was positively correlated to their total abundance and niche position (strength of species-habitat association). If 'pink' noise type of variability is as generally typical for spatial environmental variability as for temporal variability, the consequences may be profound for patterns of species diversity on different spatial scales, the form of species-area relationships and the distribution of abundances within species ranges

Spatial variability of precipitation regimes over Turkey

Turkish annual precipitation regimes are analysed to provide large-scale perspective and redefine precipitation regions. Monthly total precipitation data are employed for 107 stations (1963–2002). Precipitation regime shape (seasonality) and magnitude (size) are classified using a novel multivariate methodology. Six shape and five magnitude classes are identified, which exhibit clear spatial structure. A composite (shape and magnitude) regime classification reveals dominant controls on spatial variability of precipitation. Intra-annual timing and magnitude of precipitation is highly variable due to seasonal shifts in Polar and Subtropical zones and physiographic factors. Nonetheless, the classification methodology is shown to be a powerful tool that identifies physically-interpretable precipitation regions: (1) coastal regimes for Marmara, coastal Aegean, Mediterranean and Black Sea; (2) transitional regimes in continental Aegean and Southeast Anatolia; and (3) inland regimes across central and Eastern Anatolia. This research has practical implications for understanding water resources, which are under ever growing pressure in Turkey

ASSESSING SPATIAL BREAK-EVEN VARIABILITY IN FIELDS WITH TWO OR MORE MANAGEMENT ZONES

Farmers are interested in knowing whether applying inputs at variable rates across a field is economically viable. The answer depends on the crop, the input, their prices, the cost of variable rate technology (VRT) versus uninform rate technology (URT), and the spatial and yield response variability within each field. Methods were investigated for determining the range of spatial variability over which the return to VRT covers its additional cost compared with URT in fields with multiple management zones. Models developed in this article, or variants thereof, could be used to help farmers make the VRT adoption decision.management zones, nitrogen, precision farming, site-specific management, spatial break-even variability proportions, spatial variability, variable rate technology, yield response variability, Farm Management,

Spatial variability of groundwater recharge - I. Is it really variable?

The spatial variability of recharge is an important consideration in estimating recharge especially as all methods of estimating it are 'point' estimates and in most places recharge varies in space. This paper along with the accompanying paper attempts to find a suitable answer to the question of taking this variability into account in estimating groundwater recharge. This paper attempts to determine if recharge is actually varying in space and that this is 'true' variability and that it is not an artefact of the method used for estimating recharge. It also pulls together information on spatial variability of recharge reported by various workers in the literature, in order to determine if recharge is truly variable in space

Clustering and classifying images with local and global variability

A procedure for clustering and classifying images determined by three classification variables is presented. A measure of global variability based on the singular value decomposition of the image matrices, and two average measures of local variability based on spatial correlation and spatial changes. The performance of the procedure is compared using three different databases

Climatic and oceanic associations with daily rainfall extremes over southern Africa

Changes in climate variability and, in particular, changes in extreme climate events are likely to be of far more significance for environmentally vulnerable regions than changes in the mean state. It is generally accepted that sea-surface temperatures (SSTs) play an important role in modulating rainfall variability. Consequently, SSTs can be prescribed in global and regional climate modelling in order to study the physical mechanisms behind rainfall and its extremes. Using a satellite-based daily rainfall historical data set, this paper describes the main patterns of rainfall variability over southern Africa, identifies the dates when extreme rainfall occurs within these patterns, and shows the effect of resolution in trying to identify the location and intensity of SST anomalies associated with these extremes in the Atlantic and southwest Indian Ocean. Derived from a Principal Component Analysis (PCA), the results also suggest that, for the spatial pattern accounting for the highest amount of variability, extremes extracted at a higher spatial resolution do give a clearer indication regarding the location and intensity of anomalous SST regions. As the amount of variability explained by each spatial pattern defined by the PCA decreases, it would appear that extremes extracted at a lower resolution give a clearer indication of anomalous SST regions

Temporal and spatial variability in speakers with Parkinson's Disease and Friedreich's Ataxia

Speech variability in groups of speakers with Parkinson's disease (PD) and with Friedreich's ataxia was compared with healthy controls. Speakers repeated the same phrase 20 times at one of two rates (fast or habitual). A non-linear analysis of variability was performed which used some of the principles behind the spatio-temporal index (STI). The STI usually employs variation in lip displacement over repetitions of the same utterance and a linear analysis of such signals is conducted to represent the combined variation in spatial and temporal control. When working with patients, audio measures (here we used speech energy) are preferred over kinematics ones as they are minimally disruptive to speech. Non-linear methods allow spatial variability to be estimated separately from temporal variability. The results are tentatively interpreted as showing that PD speakers were distinguished from healthy control speakers in spatial variability and ataxic speakers were distinguished from controls in temporal variability. These findings are consistent with the speech symptoms reported for these disorders. We conclude that the non-linear analysis using the speech energy measure is worth investigating further as it is potentially revealing of the differences underlying these two pathologies

Clustering and classifying images with local and global variability

A procedure for clustering and classifying images determined by three classification variables is presented. A measure of global variability based on the singular value decomposition of the image matrices, and two average measures of local variability based on spatial correlation and spatial changes. The performance of the procedure is compared using three different databases.Images, Cluster, Classification

Extracting Additional Information From Biotic Index Samples

Macroinvertebrates were collected from a small midwestern stream over a 3-year period as part of a non-point source pollution study. Temporal and spatial variability in standard biotic index values (BIs) were computed and compared with variability expressed by a series of additional community measurements, including the mean tolerance value of all taxa present in a sample, irrespective of the numerical abundance of individual taxa. The mean tolerance value exhibited lower spatial and temporal variability than the standard BI; therefore, mean tolerance values may be useful in estimating a stream\u27s long-term ambient water quality and its recovery potential. Computations of additional BI metrics are easily accomplished with no additional lab work required, and comparisons of mean tolerance values with standard BIs should aid investigators in interpreting changes in water quality