Monthly Archives: March 2017

Based on their overlap in Sun centered

Based on their overlap in Sun-centered ecliptic coordinates, the following showers are likely duplicates. The later addition should be removed from the Working List: The ζ-Draconids (#73) are the κ-Cygnids (#12), the ν-Draconids (#220) are the August Draconids (#197), the January Comae Berenicids (#90) are the Comae Berenicids (#20), and the Southern σ-Sagittariids (#168) are the Southern μ-Sagittariids (#69). The ξ-Aurigids (#205) are part of the Perseids (#7). The ζ-Taurids (#226) are part of the Orionids (#8). The October ι-Cassiopeiids (#230) are the Leonis Minorids (#22), also pointed out by Andreic et al. (2014a). The November μ-Arietids (#249) are the Andromedids (#18), and the before mentioned ν-Cygnids (#409) are the ζ-Cygnids (#40).
Because the showers up to #318 were originally reported based mostly on small numbers of photographed meteoroid orbits, a non-detection in this CAMS data can be justified reason to dismiss the shower from the Working List. We therefore recommend removal because they neurokinin receptor are not detected while they should have been: showers ##34, 43, 46, 92, 104, 125, 126, 127, 131, 133, 136, 139, 142, 147, 148, 150, 157, 167, 169, 193, 194, 199, 207, 210, 217, 218, 224, 228, 229, 231, 232, 234, 235, 236, 237, 241, 244, 245, 258, and 260. Based on this list, a few proposed complexes should also be removed from the Working List, namely the δ-Leonids Complex (#29) and the March Virginids Complex (#93).
For now, we recommend that the remaining showers be kept in the Working List, still in need of confirmation, mostly based on the possibility of weak (S/B < 2) activity in CAMS data. We also recommend that periodic showers known from past visual observations be kept in the list, because they may not have returned during the period of our CAMS observations: the γ-Delphinids (#65), the February Canis Majorids (#111), the α-Bootids (#138), the June Lyrids (#166) and the ε-Eridanids (#209). Similarly, we cannot provide evidence to dismiss southern hemisphere or daytime showers. Finally, validation of the remaining asteroidal streams (##263–289) requires further study, because most are based on (random?) pairs of slow-moving meteors and do not stand out as a cluster in the CAMS data, in part due to their low angular velocity. The CMOR is particularly sensitive to 20–40 km/s meteors, but they need to be faint enough to create specular trails (>+5 magnitude). In this velocity range, CMOR observes mostly +6 to +8 magnitude meteors. Slower meteors generate too few electrons, while fast meteors ablate higher in the atmosphere where their generated electrons spread quickly (echo height ceiling effect). Because CAMS sees predominantly +4 to ?2 magnitude meteors, some showers rich in faint meteors are not detected by CAMS, while many fast and very slow showers detected by CAMS are not seen by CMOR.
The second CMOR batch (Brown et al., 2010) contains some duplicates: the Microscopiids (#370) are the Piscis Austrinids (#183). The λ-Draconids (#383) may be the ξ-Draconids (#242). The ν-Geminids (#397) appear to be part of the November Orionids (#250). Also, the β-Camelopardalids (#365) appear to be the June μ-Cassiopeiids (#362), while the o-Pegasids (#367) may be the July β-Pegasids (#366).

Surface evaporation We calculate surface

2.2.2. Surface evaporation
We calculate surface methane evaporation, EvCH4 (in kg m?2), using the bulk-aerodynamic formula of Deardorff (1972), as in Tokano et al. (2001). Evaporation is given by:
equation(2)
EvCH4=min(SurfCH4,max[Cdrag(Qsat-QCH4)Mfactδt,0])EvCH4=min(SurfCH4,max[Cdrag(Qsat-QCH4)Mfactδt,0])
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where SurfCH4 is the surface methane (in kg m?2), Cdrag is the surface exchange coefficient calculated from the GCM’s surface layer scheme (see Section 2.1.2), Qsat and QCH4 are respectively the saturation mass mixing ratio (mmr) and mmr of methane vapor in the lowest atmospheric layer, Mfact is the surface moisture availability factor, and δt is the model timestep (~400 s). Qsat is found from Psat by:
Qsat=RhumPsatMrat/PQsat=RhumPsatMrat/P
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where Rhum is the relative humidity chosen for condensation to occur (here 100%), Mrat is the ratio of mean molecular masses of methane and Titan’s background atmosphere, ~16/27, and P is the pressure at the middle of the lowest atmospheric layer. SurfCH4 is tracked in according to past precipitation (PrCH4) and VX765 manufacturer at each grid point, i.e.:
View the MathML sourceSurfCH4=SurfCH4init+sum of(PrCH4-EvCH4)over all previous timesteps.
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Note that Mfact does not refer to the amount of methane at the surface but rather the ease with which surface methane may be evaporated, which will depend on the type of surface on/in which the methane resides (e.g., adsorbed into a porous regolith versus a rocky regolith, or held in a lake). Mfact = 0 corresponds to a surface in which methane is permanently trapped, whereas Mfact = 1 corresponds to methane on top of the surface with no barrier to evaporation. Here we do not track surface type, so we assume that Mfact = 0.5 everywhere, equivalent to assuming that surface methane is partially adsorbed into the top regolith layer, as opposed to e.g. forming a liquid lake or diffusing deeper into the sub-surface. Given the many other approximations and uncertainties involved in these simulations, this is a reasonable starting point, but we will explore the sensitivity to Mfact in future work as a prelude to including a Land Surface Model (LSM) into TitanWRF (for details see Section 6.4).
2.2.3. Latent heating/cooling in the atmosphere and at the surface
In this work we ignore the radiative impact of varying the distribution of gaseous or condensed methane. Instead, TitanWRF’s radiative transfer scheme sees a fixed, prescribed methane distribution based on Voyager observations (Lellouch et al., 1989). However, we do consider the latent heating/cooling associated with condensation/evaporation of methane. We calculate the heat gained/lost when atmospheric methane condenses/evaporates using either the latent heat of methane evaporation, LCH4liq, for liquid and binary clouds, or the latent heat of methane sublimation, LCH4sub, for ice clouds. As in Barth and Toon (2006):
View the MathML sourceLCH4liq=(437.54809-3197.7024/T+463701.06/T2)Rdln(10)/MCH4

Artificial diet incorporation assay Standard authentic

Artificial diet incorporation assay
Standard authentic phenolic standard compounds were used for diet incorporation assay (Narayanamma et al., 2007). The compounds were weighed and mixed with the freshly prepared standard chick-pea flour based diet (mg mL? 1) at three different concentrations (100 ppm, 500 ppm and 1000 ppm). Artificial control diet contains chick pea flour (84.0 g), yeast extract powder(5.8 g), casein (5.0 g), ascorbic squalene epoxidase (3.0 g), sorbic acid (1.0 g), parabean (2.0 g), streptomycin sulfate (0.2 g), cholesterol (0.2 g), formaldehyde (1.0 mL), multi-vitamin (500 mg), vitamin-E (400 mg), double distilled H2O (300 mL) and agar (11.5 g). Treatment diet (3 g) was spread uniformly at the bottom of each plastic jar (7 cm × 5 cm). Three replications were maintained for each treatment with 10 neonate larvae of H. armigera and S. litura, separately in each replication. Larvae feeding on non-treated standard diets served as controls and maintained in triplicate with 10 in each replication. Diet assays were repeated thrice. After 10 days post treatment (dpt), larval weights and mortality was recorded and expressed in milligram and percentage, respectively.
Insect biochemical and physiological examination
Another set of experiment was conducted to examine the impact of two most effective compounds (with lowest LC50 value) on the enzymatic and oxidative status of the insect larvae feeding on treated diets (100, 500 and 1000 ppm concentration) at 5th and 10th day post treatment (dpt), however larvae fed on gallic acid (GA), cinnamic acid (CA), salicylic acid (SA) and p-coumaric acid (PA) were examined for biochemical status in terms of LDH activity ( King, 1965), total glucose and protein level (Bradford, 1976). Following exposure, the larval samples (combined weight of 100 mg) were collected on 5th and 10th dpt and weighted. Control and treated larvae whole body (100 mg) was homogenized in liquid nitrogen followed by dilution with 0.01 M (pH 7.0) potassium phosphate buffer (1:1, w/v). It was further centrifuged at 14,000 rpm for 10 min at 4 °C. The supernatant was collected in fresh tubes and stored at ? 80 °C for analysis. Larval homogenate was used to evaluate the lipid peroxidation by quantifying thiobarbituric acid-reactive substances (TBARS) (Halliwell and Gutteridge, 1990) and the degree of protein oxidation determined by quantifying total disulfide content and carbonyl content using previously published methods ( Anderson and Wetlaufer, 1975 ; Levine, 1990). Glutathione S-transferase (GST) enzyme activity was studied according to Habig et al. (1974). All biochemical assays were performed using Lambda 35 UV/vis spectrometer (Perkin Elmer, USA).
Statistical analysis
The significance in phenolics level between control and pest infestations were determined by Student t-test (P < 0.05) and represented by (*). The biochemical and GST enzyme activity data was subjected to analysis of variance (ANOVA) and treatment means were separated using Duncan\'s multiple range test (DMRT). Regression analysis was done through XY scatter-plot (linear and logarithmic curve) and 2D line chart using MS-Excel (2007). Probit analysis was used to estimate mean lethal concentrations (LC50) and associated parameters such as 95% fiducial limits of LC50\'s and chi-square (χ2) values. Statistical analysis was performed with SPSS software v 16.0 (SPSS, Inc., Chicago, IL, USA).

Depar Deperp DTI FA AD

Depar 0.09 0.43 ? 0.34
Deperp 0.08 0.84 ? 0.76
DTI
FA 0.31 0.67 ? 0.36
AD 0.62 0.33 0.29
RD 0.07 0.92 ? 0.85
MD 0.01 0.97 ? 0.96
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Fig. 5
Fig. 5.
Simulated effects of undulation (λ) and axonal volume fraction (VF) on the NODDI and WMTI metrics.
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Fig. 6
Fig. 6.
Simulated effects of undulation (λ) and axonal volume fraction (VF) on the DTI metrics.
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4. Discussion
This study demonstrated that the increased orientational coherence of the CST in iNPH tends to normalize after CSF shunt surgery, whereas the decreased axon density remains unchanged. The observations of orientational coherence are consistent with the presumed axon stretching and its recovery. These findings were consistent in the two different models (NODDI and WMTI), although the estimated values varied between the two, as in a previous report (Jelescu et al., 2015). The ability of these methods to disentangle the effects of stretching and axon density was confirmed by the simulation experiment. The estimates of orientational coherence and axon density thus appear to serve as markers of reversible and irreversible changes in the CST of iNPH patients. The cholinesterase inhibitor simulation experiment was useful for interpreting the clinical results. Recognition of how much the estimates of microstructural properties are affected by factors not explicitly considered by the model increased our confidence in the interpretation. In addition, although NODDI and WMTI appeared able to estimate reversible and irreversible changes separately, MD derived from conventional DTI also seemed to reflect irreversible changes in the patients (Table 1) and was highly sensitive to axon density in the simulation (Table 2). Previous attempts have been made to predict treatment outcome using DTI (Jurcoane et al., 2014). Although FA and AD were not specific solely to stretching in the simulation, a multi-parametric classification scheme combined with MD may improve the predictive performance.
The present results may also facilitate the interpretation of previously reported findings. For example, using q-space diffusion displacement profiles, Hori et al. reported a postoperative increase in the root mean square displacement in the extra-axonal space (Hori et al., 2016). They focused on the diffusion perpendicular to the CST, and the present simulation result is compatible with their observation, in that recovery of physiological undulation (increase in λ) leads to an increase in Deperp (Fig. 5). We speculate that the postoperative change in iNPH is a recovery of undulation, and not relief from dense packing by compaction, because Fig. 5 shows that decreasing VF cannot cause such postoperative decrease in τ1 as observed in the present patient group.
It should be noted, though consistency between NODDI and WMTI as well as between the clinical data and simulation suggested axon undulation is a good candidate mechanism for the observed changes, we do not yet have conclusive evidence. Microscopically, non-straight, twisted or tortuous axon trajectories have been observed within the CST (Axer and Keyserlingk, 2000), and the tortuosity decreases with mechanical stretching (Hao and Shreiber, 2007), supporting the undulation/stretching hypothesis. However, undulation is very closely related to dispersion, and the effects from undulation and dispersion are considered indistinguishable by the method we used. Dispersion is almost ubiquitous within the brain even in the relatively coherent structures like internal capsule and corpus callosum (Axer et al., 2011 ; Budde and Annese, 2013). In the present study, comparison between the results from the human subjects (Fig. 4) and the simulation (Fig. 5) demonstrates that τ1 in the normal subjects is lower than even the highest undulation in the simulation (λ = 1.2), indicating that undulation in combination with dispersion may be a more complete and realistic scenario. Regarding other candidate sources of diffusion changes, the pathological findings in iNPH are nonspecific ischemia and gliosis ( Akai et al., 1987 ; Del Bigio, 1993) that are associated with decreased FA. This implies increased axon density or myelination is unlikely as a source of the FA increase. Because iNPH is a disorder of CSF circulation, observation of the post-mortem brain may not be enough to detect the pathognomonic changes of the disease. Recently, increased FA and AD within the CST was reported also in Parkinson\’s disease and was interpreted as selective neurodegeneration or a compensatory increase in axon density (Mole et al., 2016). Our results indicate that the FA/AD increase in iNPH is based on a different mechanism, because the axon density was lower in iNPH than in the controls and did not show any changes that can be related to the partial normalization of FA/AD.

fructose 1 6 bisphosphatase We calculated the area of north facing and south

We calculated the area of north-facing and south-facing slopes in each grid cell based on G-DEM. These slopes were defined by azimuth angles of 315–45° and 135–225°, respectively. We also calculated alternatives along the NNE–SSW axis and NE–SW axis following the assumption that the highest temperature of the day is usually reached in early afternoon on SSW or SW slopes (Yoshino, 1975 and Geiger et al., 2003). However, models based on the latter two variants explained less variability in the EFS Index than the variant with south- and north-facing slopes (data not shown). Therefore, we decided to use only slopes related to the N–S axis.
For the EFS Index calculation we used only slopes steeper than 10° because more gentle slopes have a low difference in solar radiation between northern and southern aspects, especially in the spring and summer growing season when the sun is high above the horizon. Moreover, flat areas or gentle slopes are more often occupied by villages or used as arable land. We used all the 2100-m grid fructose 1 6 bisphosphatase that contained more than 50% of their area with slopes steeper than 10°, but we excluded the cells containing more than 50% of their area above the timberline, where the establishment of forest, thus also of forest-steppe, is limited by low temperature. We obtained the timberline altitudes from isolines published by Malyshev and Nimis (1997), which we interpolated into a 30 m grid to cover the entire study area. However, because the published timberline isolines were relatively coarse, we checked the robustness of the results by calculating an additional model for the EFS Index which also included the areas above the timberline.
2.4. Regression trees
Regression trees (Breiman et al., 1984) were used to analyze how the climatic predictors explain variation in the EFS Index values. We used this method because of the high complexity of the data including possible non-linear relationships and interactions among predictors (Fig. 3 and Fig. 4). The method splits the cases (grid cells) into subgroups based on the threshold values of predictors to maximize within-group homogeneity and between-group heterogeneity in the EFS Index values (De\’ath and Fabricius, 2000). To select the optimal tree size (optimal number of splits, i.e. nodes) in both datasets, 10-fold cross-validation was used. We followed the 1-SE rule based on the cross-validation results to select the optimal tree size (Breiman et al., 1984). This rule prunes the tree so that the mean error estimate is within one standard error of the mean error estimate of the full tree. For each node of the tree, surrogates were identified, i.e. the variables that were able to allocate at least 65% of the cases similarly to the splitting based on the primary splitter. To assess the importance of each predictor variable in the final tree, we calculated the importance values of these variables on each node (Breiman et al., 1984) and rescaled them to express the percentage of explained variation. Shared effects were then calculated for each node by subtracting the variation explained by a weaker predictor from that explained by a stronger predictor. Thus, this approach possibly overestimates the amount of variation shared by all three predictors at the expense of the variation shared between pairs of predictors. The regression trees were calculated using R 3.1.2 (www.r-project.org) with the “rpart” package (Therneau et al., 2012).

equation in which qx qy qz are

equation(4)
in which qx, qy, qz are the specific discharge components in the principal directions, Kfx, Kfy, Kfz are the freshwater hydraulic conductivity components in the same directions, and ρf ap4 the freshwater density.
The relation between water density and solute concentration is described by the following equation:
equation(5)
in which ρo is the freshwater density, α is the density difference ratio and Co, Cs are the reference and the maximum concentration, respectively. In the current study we consider Co = 0.
The density difference ratio is expressed as:
equation(6)
View the MathML sourceα=ρs?ρoρo
in which ρs is the maximum seawater density.
The coupled partial differential equation system described above is solved numerically, using a finite difference scheme and specifically SEAWAT code. SEAWAT is a popular numerical code, which utilises MODFLOW and MT3DMS codes, to solve flow and transport equations respectively in an iterative time-step approach.
The indicator adopted in the current study is based on one of the vulnerability indicators proposed by Werner et al. (2012) and Morgan and Werner (2014) for the steady-state, sharp interface analytical solutions of SWI in an unconfined aquifer. The proposed sharp interface indicator is modified to become applicable to the variable density solution of SWI. The 50% isohaline is selected to represent the interface between freshwater and seawater (Pool and Carrera, 2011). The freshwater volume, expressed per length of coastline, is calculated numerically as the volume between the 50% isohaline and the water table, multiplied by porosity n. The calculations are performed at each time step of the SWI simulation, in order to determine the interface variations on a temporal scale.
The difference of the values of freshwater volume between two consecutive years (FwV) is used as the indicator to investigate the relation between climate variability and groundwater flow dynamics. This is achieved through the correlation of the obtained values of RDI for each reference period (3, 6, 9 and 12 months) with the value of FwV of the corresponding year. A significant correlation between the FwV and short reference periods of RDI, may also provide an early estimation of groundwater conditions based on the drought severity level.
3. Results and discussion
The simulation results from the SWI numerical model for the two examined aquifers appear in Fig. 4, in which the average annual values of freshwater volume per coastal aquifer width are presented for the three datasets. The FwV indicator values, along with the RDIst values for each reference period (3, 6, 9 and 12 months), indicatively for the second dataset, are presented in Fig. 5.
Average annual values of freshwater volume per coastal aquifer width for the two aquifer types (a) and (b), respectively, and the three datasets.
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FwV values and RDIst values for 3-, 6-, 9- and 12-month reference periods, indicatively for the second dataset.

Natural fluctuations and assimilation of

Natural fluctuations and assimilation of N in vegetation and soils make it difficult to assess anthropogenic N inputs; however, the analysis of stable isotope ratios offers a method to trace pollution to its source. Because different sources of N often have distinctive isotopic ratios (i.e. relative abundance δ15N), N pools in the atmosphere, plant tissues and soils can often be traced back to their sources (Kendall et al., 2007). For example, the δ15N of NOx derived from fuel combustion in vehicles with catalytic converters range from +3.7‰ to +5.7‰ (Moore, 1977, Ammann et al., 1999, Pearson et al., 2000 and Middlecamp and Elliott, 2009), while vehicles without catalytic converters range from ?13‰ to ?2‰ (Heaton, 1990). Natural NOx sources, including lightning (?0.5–1.4‰; Hoering, 1957) and biogenic N2O emissions from fertilized soils have lower δ15N values (?49‰ to ?20‰; Hoering, 1957 and Li and Wang, 2008). The δ15N signatures of plants along roadsides are often elevated compared to the surrounding environment (Pearson et al., 2000). In this way, δ15N analysis can be used to track the origin of NOx in the purchase RO4929097 as well as the sources of N in soils and plants.
Spatial variability in the distribution of atmospheric N deposition is not well known in the Grand Canyon Region, and may be of increasing importance to guide park management decisions. High levels of tourism, known vehicular patterns, and the sensitivity of GCNP make it an ideal location to investigate indicators of N-deposition from vehicular emissions in a primarily water-limited environment. In this study, we combined measurements of localized atmospheric NOx concentrations, foliar and soil δ15N abundance and soil N concentrations to evaluate N enrichment along roadsides in GCNP. In this study, we combined measurements of localized atmospheric NOx concentrations, foliar and soil δ15N abundance and soil N concentrations to evaluate N enrichment along roadsides in GCNP. Areas of GCNP closest to the road at the South Entrance with were predicted to receive the highest N inputs, particularly during the peak tourist season.
2. Materials and methods
The study was conducted from May 2011 to January 2012 at ten sites along the South Rim of Grand Canyon National Park in northern Arizona (Fig. 1a). There are traffic density gradients in GCNP. Nearly 85% of approximately 2.5 million vehicles enter the park through the South Entrance and the remaining vehicles enter at the Desert View entrance on the east side of the park (Fig. 1b). Hermit’s Rest, on the west side of the park, is closed to private vehicles except December through February. The South Entrance highway was constructed in 1928, and Highway 64, which runs through the Desert View entrance, was constructed in 1932. The South Rim averages approximately 394 mm of precipitation annually with 145 mm falling as rain during the summer months (July–September; GCNP 2013). Soils are calcareous, ranging from silty loam alfisols at the South Entrance to sandy loam inceptisols (less weathered) at the Desert View Entrance (Fig. 1c, National Resource Conservation Service: http://ortho.ftw.nrcs.usda.gov/cgi-bin/osd/osdname.cgi). Soil moisture across the study sites varies from relatively arid at the South Entrance, to more arid at the Desert purchase RO4929097 View Entrance. Vegetation at the South Entrance is dominated by ponderosa pine (Pinus ponderosa Engelm.) and Pi?on pine (Pinus edulis Engelm.), while the east entrance (Desert View) and Hermit’s Rest vegetation is characterized as Pi?on – juniper woodlands (Juniperus monosperma; LANDFIRE, 2010). Complex-terrain features affect local wind-flow patterns in the Grand Canyon area. Local channeling, decoupled canyon winds, and slope and valley flows dominate the region when synoptic weather systems are weak.

For M grahamii it is

For M. grahamii, it is noticeable the decrease in the density of up to 95% in the area invaded by buffelgrass, compared with the undisturbed site. This decline is probably associated with changes in microclimatic conditions due to the loss of tree and shrub cover associated with fires occurring after the invasion. Changes in vegetation structure have produced a savanna type grassland of buffelgrass in the study area with only a few dispersed trees and shrubs. Structural change modify the chemicals exchange fluxes and water fluxes to the grassland layer closer to the soil, with a consequent microclimate alteration that may influence native plants germination and establishment ( Morales-Romero and Molina-Freaner, 2016). A comparison of microclimate parameters between a buffelgrass pasture and natural thornscrub found soil temperature 6 °C higher and air temperature 2 °C higher in the pasture compared to thornscrub (Morales-Romero and Molina-Freaner, 2016). In the same study, soil water availability showed significantly greater values in the thornscrub than in the pasture. If these differences in microclimate happen in our study system, these altered conditions can limit the germination and establishment of native species as M. grahammii and can also cause the death of seedlings, as has been observed in Pachycereus pecten aboriginum in buffelgrass pastures ( Morales-Romero and Molina-Freaner, 2008). Allelopathic interaction between native species and buffelgrass can also influence negatively the germination and survival of M. grahamii, in the invaded site, as has been found for some cacti species using root and leaf leachates ( Silva, 2013). Another factor that can induce a direct drastic change in the density of the species is fire. In the studied area, 24 fires have been reported in different points, from June 2010 to February 2016 (records from the CEES). These fires occurred during the dry season and in areas invaded by buffel grass. Nine fires have occurred in the invaded site in which this study took place and burned plants of M. grahamii chemicals have been observed with the appearance of being dead. However we do not know if some regrowth may occur overtime. In addition seeds of M. grahamii did not germinate after being exposed to a thermal shock of 1 min at temperatures of 100 or 120 °C which according to our experience are easily reached on the ground during the burning of buffelgrass ( Tinoco-Ojanguren et al., 2016). It has been shown that high temperatures produced during buffel grass fires can affect surface soil temperature and 1 cm below surface; however, there is no effect at 2 cm below soil surface ( Patten and Cave, 1984 and Tinoco-Ojanguren et al., 2016). The same pattern was observed for fires in the Mojave Desert produced by invasive annual grasses where temperatures at ?2 cm from soil surface were below 100 °C (Brooks, 2002). However McDonald and McPherson (2013) reported an average temperature from the soil surface to 1.2 m above the soil of 690 °C for prescribed fires, but this study did not recorded below ground temperatures.

Mara could be defined as an

Mara could be defined as an edge species (sensu Imbeau et al., 2003) given that its habitat includes open and shrubby areas. In this sense, and as indicated by the relation of warrens presence to fences, this species could actually benefit from shrubland fragmentation due to human infrastructure. Another implication of mara\’s requirement for heterogeneity is that croplands are not suitable habitat for this species, which could explain the range reductions in Córdoba and Buenos Aires provinces where agriculture area has been expanded during the last decades. In contrast, cattle or sheep ranching could be compatible with mara conservation. It is even possible that ranching benefits maras to some degree (Kufner and Chambouleyron, 1991) given that ranching produces areas of low vegetation cover ( Bisigato and Bertiller, 1997 ; Cheli, 2009). This effect was also reported for other medium size rodents and the brown hare in Monte and Chaco regions (Tabeni and Ojeda, 2003) of South America. However over grazing could lead to shrub recruitment and eif2a of grasses ( Beeskow et al., 1995 ; Bucher, 1987), changes that would negatively impact mara.
The different relationships between mara presence and shrubby steppe in grassland environments highlight the need for further study of mara habitat use, distribution and response to human activities in multiple regions of Argentina. This is advisable because the Pampa and Patagonia Steppe regions have a more open vegetation structure than the Monte where a dense shrub matrix is predominant. Most of the previous studies on mara were performed in Monte (Kufner, 1995; Kufner and Chambouleyron, 1991; Rodríguez, 2009; Rodríguez and Dacar, 2008 ; Sombra and Mangione, 2005). This concentration of research in the Monte may explain only part of the mara-habitat interaction and could potentially lead to an overemphasis on the importance of open areas. Moreover, it is likely that habitat modification resulting from human activities varies both with the environment of a region as well as the extent and intensity of the impact. Specifically, activities that lead to small and medium size open areas in a shrub matrix like Monte vegetation could benefit mara but activities that create large open areas should be avoided because of the lack of shelter. On the other hand, because the different characteristics of vegetation activities with the same impact on shrubs that in Monte lead to small open areas could lead to the lack of shelter in Patagonia.
There are similar cases across the world where mammals respond in different way to habitat modification by human activities in arid and semi-arid biomes, especially in response to different intensities of cattle grazing (i.e. Table 1 in Tabeni and Ojeda, 2003). Although there seems to be agreement about negatives impacts of shrub encroachment by overgrazing, medium to low levels of grazing can have positive eif2a or negative effects depending on the particular habitat requirements of a species (Blaum et al., 2007; Hoffmann and Zeller, 2005 ; Tabeni and Ojeda, 2003). More drastic land-use changes such as urbanization or the conversion of rangelands to cropland have a stronger impact on biodiversity (MA, 2005) but they could be compatible with biodiversity conservation in a landscape context if species responses are considered across spatial scales (Tews et al., 2004) with land use diversification. The complexity and scale-dependence of these relationships make it difficult to generalize across species, activities, or environments. In addition, it underscores the need for land managers and biologists to understand the local response of a species. Conservation strategies that are responsive to these local species-habitat relationships within the broader landscape context may support economic development while protecting biodiversity and associated ecosystem services.

S V T S V

S34-V1569853207 2007-273T13:44:55-13:46:20 36 18 120 23.1 47.8 9.4 35.9 ??10.4 183882.
S34-V1569853808 2007-273T13:54:56-13:56:21 36 18 120 23.0 48.7 9.3 36.8 ??10.4 187928.
S34-V1569854409 2007-273T14:04:57-14:06:22 36 18 120 22.9 49.5 9.2 37.7 ??10.4 191945.
S34-V1569854506 2007-273T14:06:34-14:07:59 36 18 120 22.9 49.7 9.2 37.9 ??10.4 192744.
S34-V1569854700 2007-273T14:09:48-14:11:13 36 18 120 22.8 50.0 9.2 38.3 ??10.4 194339.
S70-V1696219767 2011-275T03:19:40-03:20:56 34 17 120 28.8 102.7 0.0 129.4 11.4 199816.
S70-V1696219855 2011-275T03:21:08-03:22:24 34 17 120 28.8 102.8 0.0 129.5 11.4 199916.
S70-V1696219943 2011-275T03:22:36-03:23:52 34 17 120 28.7 103.0 0.0 129.6 11.4 200017.
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Table 5.
Rhea disk-integrated observations processed in this ephrin receptor work. Columns S and L indicate the cube dimensions along sample and lines axis, respectively.
SEQUENCE START-END S L EXP PHASE SUBSPC SUBSPC SSOL SSOL SPC ALT
OBS ID TIME (UT) (s) (°) LON (°) LAT (°) LON (°) LAT (°) (km)
S17-V1516202693 2006-017T14:55:22-14:56:28 42 36 40 34.7 309.1 0.3 338.7 ??18.5 226163.
S17-V1516202765 2006-017T14:56:34-14:57:40 42 36 40 34.8 309.0 0.3 338.8 ??18.5 225845.
S43-V1597403216 2008-227T10:28:30-10:30:00 32 16 160 27.7 348.9 ??4.1 321.2 ??5.3 1638460.
S43-V1597403307 2008-227T10:30:01-10:31:30 32 16 160 27.7 348.9 ??4.1 321.3 ??5.3 1638800.
S43-V1597404177 2008-227T10:44:31-10:46:01 32 16 160 27.4 349.4 ??3.9 322.1 ??5.3 1642060.
osteoporosis S43-V1597405905 2008-227T11:13:19-11:14:49 32 16 160 26.9 350.4 ??3.7 323.7 ??5.3 1648300.
S43-V1597405996 2008-227T11:14:50-11:16:19 32 16 160 26.9 350.5 ??3.7 323.8 ??5.3 1648620.
S43-V1597406099 2008-227T11:16:33-11:18:03 32 16 160 26.8 350.5 ??3.7 323.9 ??5.3 1648990.
S43-V1597407060 2008-227T11:32:34-11:34:04 32 16 160 26.5 351.1 ??3.6 324.8 ??5.3 1652310.
S43-V1597407151 2008-227T11:34:05-11:35:34 32 16 160 26.5 351.2 ??3.6 324.8 ??5.3 1652610.
S43-V1597407254 2008-227T11:35:48-11:37:18 32 16 160 26.5 351.2 ??3.6 324.9 ??5.3 1652970.
S43-V1597407345 2008-227T11:37:19-11:38:48 32 16 160 26.4 351.3 ??3.6 325.0 ??5.3 1653270.
S43-V1597407448 2008-227T11:39:02-11:40:32 32 16 160 26.4 351.3 ??3.6 325.1 ??5.3 1653620.
S43-V1597407539 2008-227T11:40:33-11:42:02 32 16 160 26.4 351.4 ??3.6 325.2 ??5.3 1653920.
S43-V1597407642 2008-227T11:42:16-11:43:01 32 16 80 26.3 351.4 ??3.6 325.3 ??5.3 1654200.
S43-V1597407688 2008-227T11:43:02-11:43:46 32 16 80 26.3 351.5 ??3.5 325.3 ??5.3 1654350.
S43-V1597407740 2008-227T11:43:54-11:44:39 32 16 80 26.3 351.5 ??3.5 325.4 ??5.3 1654530.
S43-V1597407786 2008-227T11:44:40-11:45:24 32 16 80 26.3 351.5 ??3.5 325.4 ??5.3 1654680.
S43-V1597407838 2008-227T11:45:32-11:46:17 32 16 80 26.3 351.6 ??3.5 325.5 ??5.3 1654850.
S43-V1597407884 2008-227T11:46:18-11:47:02 32 16 80 26.3 351.6 ??3.5 325.5 ??5.3 1655000.
S43-V1597407936 2008-227T11:47:10-11:47:55 32 16 80 26.3 351.6 ??3.5 325.5 ??5.3 1655180.