FE SEM SE images of extreme

FE-SEM/SE images of extreme F-rich particles D08-034 (a) and D08-035 (b).
Fig. 16.
FE-SEM/SE images of extreme F-rich particles D08-034 (a) and D08-035 (b).
Figure options
(a) OCaF compositions of F-rich particles D08-034 (open squares) and D08-035 …
Fig. 17.
(a) Osingle bondCasingle bondF compositions of F-rich particles D08-034 (open squares) and D08-035 (solid squares) with Ca/(Ca + F) ratios of 0.3 and 0.6, resp., and (b) the Csingle bondOsingle bondCa compositions on a mixing line between CaO particles and a partially assimilated oxcocarbon layer.
Figure options
5. Morphing F-bearing CaCO3 particles
Six carbonate particles that were first analyzed during October 2008 were revisited for additional imaging and re-analyses in April 2010, March 2011, and again in January 2012. During selective estrogen receptor modulators this time these particles had undergone significant changes in their appearances. Initially well-defined outlines and surfaces became smooth and featureless and developed a bubbly crust with occasional pores. Their equivalent SQRT grain sizes range from 0.8 μm to 2.5 μm which is smaller than the CaCO3 particles describe above that are 4.6 μm, 6.4 μm and 6.9 μm in size. These changes were probably controlled by the surface-to-volume ratio of thermodynamically unstable particles. They were not due to exposure in the incident FE-SEM selective estrogen receptor modulators beam, or a FIB artifact. It is noted that FTIR spectra of several of these Casingle bondOsingle bondC particles showed an Osingle bondH (3200–3500 cm?1) feature (Ciucci, 2011), which provided a hint of this post-collection secondary process. The DUSTER storage protocol did not foresee chemical interactions between the collected particles and the humid ambient air in the laboratory in Naples (Italy). There are several pathways for this interaction of Ca-carbonate with atmospheric water, viz.
equation(1)
CaCO3+H2O=Ca(OH)2+CO2CaCO3+H2O=Ca(OH)2+CO2
Turn MathJax on
and
equation(2a)
View the MathML sourceH2O+CO2→H2CO3
Turn MathJax on
followed by
equation(2b)
CaCO3+H2CO3=Ca(HCO3)2.CaCO3+H2CO3=Ca(HCO3)2.
Turn MathJax on
The product of the first reaction, Ca(OH)2, cannot be detected by the EDS system that cannot detect hydrogen; Primary transcript will reduce the input CaO2 (Ca-peroxide) (Rietmeijer et al., 2008).
This morphing process is most spectacularly displayed by particle D08-012. The main particle is rather flat with a few angular platy grains on top (Fig. 18a). The main flat part shows a straight gash of unknown origin and many small pores (Fig. 18b). Initially the particle contours are rather sharp; after the morphing process the particle dissolved into a massive blob that retained vestiges of the original shape (Fig. 18c) and ultimately only a trace of the original gash survived (Fig. 18d). The morphing process preserved the compositions expected for Ca-carbonate with a carbon surface layer (Fig. 19).
FE-SEM/SE images showing the morphing sequence of D08-012 beginning with the …
Fig. 18.
FE-SEM/SE images showing the morphing sequence of D08-012 beginning with the original particle (a) with its distinct gash (b), and (c) after complete morphing with a vestige of the original gash (d).
Figure options
COCa compositions of morphed particle D08-012 (a) and stoichiometric …
Fig. 19.
Csingle bondOsingle bondCa compositions of morphed particle D08-012 (a) and stoichiometric Ca-carbonate (star), and the low-F Osingle bondCasingle bondF compositions (b).
Figure options
The same morphing process also affected other particles that ultimately dissolved into amorphous blobs (Fig. 20). Morphing of the smallest particles may have started well before April 2010 when it was first noticed that some of the collected particles had developed surface blisters, e.g. particle D08-028 ( Fig. 20). In a rare case, particle D08-015, the blister had an open pore at the top (Fig. 21). We speculated that these blisters mark the onset of the built-up of a gas phase. Particle D08-023 is actually two chemically homogeneous components, (1) a platy part decorated with smaller (sub-)spherical particles and (2) a compact cluster of rectangular prismatic and (sub-)spherical droplets.

Phylogenetic analyses Both molecular markers were analysed separately in

Phylogenetic analyses
Both molecular markers were analysed separately in a series of preliminary maximum likelihood phylogenetic analyses that produced trees virtually without conflict, mostly because of lack of support for most nodes. Therefore, the aligned cox1 and wnt data were concatenated in a single alignment that was used for subsequent phylogenetic analyses upon adding the homologous sequences from two outgroups in the subfamily Chrysomelinae: Calligrapha polyspila (Germar) [AM160984 (cox1) and LM644939 (wnt)] and Stilodes sp. [LM644645 (cox1) and LM644959 (wnt)]. The phylogenetic analysis on the combined matrix was under a maximum likelihood framework as implemented in RAxML 7.2.8 ( Stamatakis, 2006). We ran seven independent analyses under a general time-reversible substitution model with different combinations of data partitioning (each g protein coupled receptors position of each gene as a different partition, or first and second codon positions of each gene in the same partition), and inclusion of heterogeneity parameters in the probability of nucleotide changes (a CAT approximation, proportion of invariable sites [I] and/or the parameter gamma [Γ]). Specifically, the evolutionary models tested were: (1) GTR + Γ with two codon partitions per gene (12_3); (2) GTR + Γ + I with two codon partitions per gene; (3) GTR + Γ + I with three codon partitions per gene (1_2_3); (4) GTRCAT with two codon partitions; (5) CAT with three codon partitions; (6) GTRCAT + I with two codon partitions; and (7) GTRCAT + I with three codon partitions. Tree searches consisted in the search of the best-scoring maximum likelihood tree from the optimisation of 100 random starting trees. Moreover, bootstrap support was added to each optimal tree based on 100 data pseudo-replicates.
Molecular identification of host-plants
We investigated potential host-plant associations of galerucine beetles of interest following the strategy of Jurado-Rivera et al. (2009) and the protocols outlined in Papadopoulou et al. (2015) and De la Cadena et al. (2017). Specifically, we amplified short flanking regions and intergenic spacer of the cpDNA psbA-trnH locus. Primers and conditions used were the same as in De la Cadena et al. (2017). The psbA-trnH sequences obtained were submitted to the bioinformatic pipeline BAGpipe to assist taxonomic assignments based on joint analyses with publicly available and taxonomically labelled homologue sequences from public sequence databases ( Papadopoulou et al., 2015). Several criteria for taxonomic inference were used, including an assessment based on sequence similarity and genetic distances, and an assessment based on maximum likelihood phylogenetic analyses and clade support. The former consisted on the identification of GenBank sequences producing the best match as well as those below a 4% divergence threshold. The latter extracted the taxonomy of the supported clade including the psbA-trnH sequence from our Galerucinae specimen (inner taxon) and that of the closest supported node moving to the root of the tree (outer taxon).

Spatial association of R pedestris with surrounding environment As

Spatial association of R. pedestris with surrounding environment
As a part of hypothesis testing, we investigate spatial relationships between R. pedestris distribution and the surrounding environment over three years. A 1-m resolution aerial digital image in ArcInfo 10 (Environmental Systems Research Institute, Redland, CA) was used as a filipin map. Land cover layers for the study area were obtained from the Ministry of Environment, South Korea, and overlaid on the R. pedestris distribution maps and the base map for spatial analyses. To determine the spatial relationship of R. pedestris with the surrounding environment, the effect of the distance from trap catches was determined by calculating average trap catches within a specified distance from forested areas and field crops (i.e., buffer). The buffer size ranged from 0 m (trap catches within forested area or field crop) to 300 m (the maximum buffer size) in 20-m increments. Buffer analysis was conducted with Spatial Analyst?, an extension of ArcInfo 10 (Environmental Systems Research Institute, Redland, CA). Specifically, we determined the effect of forested areas (i.e. a major overwintering sites) on overwintered populations of R. pedestris at the first population peak and how field crops affected R. pedestris distribution at the second population peak.
Results
The results from the 3-year monitoring of R. pedestris by using pheromone traps showed four distinct population peaks each year ( Fig. 2). Population peaks were observed in May 7–20, June 24–July 8, August 13–September 17, and after October 8 in 2010. In 2011, R. pedestris populations peaked in May 4–11, June 15–July 6, August 24–September 7, and after October 12. In 2012, April 12–26, June 7–21, August 2–16, and after September 6 were the times when population peaks of R. pedestris were observed. Compared to group 3, fewer R. pedestris in group 2 were caught in the trap, but the patterns of population dynamics were similar. Population levels of group 2 were 7.3% and 27.3% at the first and second peaks, respectively, compared to those of group 1. However, the population level at the third peak showed an opposite trend; 118.6% and 265.5% at the first and second peaks, respectively, compared to those of group 1.
Seasonal occurrence of R. pedestris adults in three habitat groups: group 1, paddy field area; group 2, interface between crops and forest; group 3, forested area.
Figure options
Characterizing spatial distribution patterns of R. pedestris
SADIE\’s aggregation index, Ia, was not consistent throughout the season, indicating that the spatial distribution pattern of R. pedestris was dynamic ( Fig. 3). Except for very early in the season, most of Ia values were > 1, indicating spatial aggregation; R. pedestris populations showed no significant (P > 0.05) spatial aggregations at the first population peak and significant aggregations at the last population peaks (Fig. 3 and Table 1). Distribution maps depicting spatial locations of patches and gaps indicated consistent spatial distribution patterns of R. pedestris ( Fig. 4). Gaps at the first peak of R. pedestris populations were found mostly in the agricultural fields and those for the second population peak were found in the agricultural fields and patches in the forested areas. Most of the patches and gaps at the third and last peaks were found in the agricultural fields and forested areas, respectively.

a b dorsal habitus c ventral

a–b, dorsal habitus; c, ventral habitus; d, lateral habitus; e–f, antenna; g, frons; h, metacoxal plate; i, fifth abdominal ventrite; j, hypomeron; k, metepisternum; l, metatarsi; m, aedeagus in dorsal view; n, aedeagus in lateral view (scale bar for a–f: 1 mm; g–n: 0.5 mm).
Figure options
Sexual dimorphism. Female very similar to male, but can be distinguished by the following characters: clypeus more wide at ras gtpase than those of male, width of clypeal apex about 2.2 times wider than distance between antennal sockets; antennae relatively shorter, almost reaching anterior margin of metepisternum; third antennomere about 1.45 times longer than fourth; antennomeres 4–10 more stubby; apical antennomere less elongate, about 4.1 times longer than wide, and approximately 1.7 time longer than previous ( Fig. 2f).
Distribution. Korea (New record), Japan, Russia (Far East).
Remarks. Fornax consobrinus is closely related to F. nipponicus, but can be distinguished by ratio of third antennomere to fourth in male: third antennomere about 1.3 times longer than fourth in F. consobrinus, while about twice as long in F. nipponicus. Also F. consobrinus is differentiated from F. victor by its simple claws. Straight-formed larvae in last stage were collected between sapwoods of dead Robinia pseudoacacia in April and emerged as adults in May. Adults are clicking, well-flying, and active-running.
Subfamily Eucneminae Eschscholtz, 1829.
Diagnosis. Body elongate or cylindrical; labrum attached underneath frontoclypeal region; pronotal lateral ridges complete; hypomeron with lateral antennal groove; mesepimeron fused with mesepisternum; protibia with one apical spur; meso- and metatibia flattened, without lateral rows of spines; aedeagus highly modified (Muona, 1993 ; Muona, 2000).
Tribe MesogeniniMuona, 1993.
Diagnosis. Body elongate, cylindrical and usually convex; antennomeres 4–10 subequal, usually increasingly dentate toward apex; hypomeral antennal grooves well-developed laterally, basally closed or constricted; tarsi often delicate, male first protarsomere without sex-comb; abdominal ventrites connate; median lobe modified ras gtpase (Muona, 1993 ; Muona and Malinen, 2017).
Genus FeaiaFleutiaux, 1896.
Diagnosis. Body oblong, cylindrical, and strongly convex; clypeus trapezoidal, feebly trilobed or simply rounded at apical margin; antennae serrate or filiform, not exceeding pronotum; second antennomere slightly shorter than third; elytra strongly striated; hypomeral antennal grooves well-developed laterally; metepisternum strongly expanded posteriad; metacoxal plate subparallel-sided; lateral surfaces of meso- and meta tibiae with setae; fifth ventrite truncated or beaked at apical margin (Fleutiaux, 1896; Muona, 1993 ; Otto, 2016).
Figure options
Heterotaxis nipparensis Hisamatsu, 1957: 45.
Distribution. Korea (new record), Japan.
Remarks. Feaia Fleutiaux has remained obscure ever since its description. Muona (1987) established its identity by fixing the type species. Muona (1991: 172) included five species in Feaia, but without any supporting data. Later Muona (1993: 50) discussed the question in more detail, but inadvertently did not mention Heterotaxis nipparensis.

Fig Fig Angiogram prior to uterine artery

Fig. 4
Fig. 4.
Angiogram prior to uterine artery embolization shows right uterine artery to be enlarged and tortuous. A large uterine mass is present.
Figure options
In an effort to alleviate the patient\’s painful abdominal distention, she underwent CT guided aspiration of 2.5 l fluid from the cyst (Fig 5), which significantly decreased the uterine size and symptoms. The aspirated fluid was clear and serous, negative for malignancy on cytology, and showed no evidence of infection. She was discharged in stable condition and was followed up in our clinic the following week, where endometrial biopsy and a PAP smear were collected and found to be negative for malignancy.
Fig. 5
Fig. 5.
Computed tomography guided aspiration of cystic mass.
Figure options
The patient desired definitive surgical management and underwent an uncomplicated total laparoscopic hysterectomy with bilateral salpingectomy as an outpatient with an uneventful recovery post operatively. Surgical pathology revealed a 1478-g dopamine beta hydroxylase with cystically dilated leiomyoma (Image 1 ; Image 2) and myometrium with changes secondary to embolization (Image 3) as well as benign fallopian tubes with paratubal serous cysts.
Image 1
Image 1.
100 × magnification demonstrating cystically dilated leiomyoma.
Figure options
Image 2
Image 2.
25 × magnification demonstrating cystically dilated leiomyoma.
Figure options
Image 3
Image 3.
200 × magnification demonstrating a cellular area with extensive infarction due to embolization.
Figure options
3. Discussion
ADPKD is a systemic disorder that primarily affects the kidneys, but often presents with extra-renal manifestations, such as liver and pancreatic involvement. Cystogenesis is understood to be caused by a mutation in one of two genes, which encode the membrane-spanning proteins, polycystin-1 and polycystin-2 (PKD1 and PKD2) [3]. These proteins work together to increase calcium entry uterine cysts as an extra-renal manifestation [1] ; [2].
Upon review of the patient\’s records, she had been diagnosed with a 4 × 1 mm intrauterine fluid collection 12 years prior to her presentation. She had been lost to follow-up and it is likely, that this uterine cyst was present for years and gradually grew in size.
In this case, we were able to treat the patient\’s symptomatic anemia and stabilize the active vaginal bleeding with the help of uterine artery embolization. Though not a treatment for the active disease process, the percutaneous cyst aspiration by interventional radiology of 2.5 l alleviated the patient\’s abdominal bulk symptoms tremendously by shrinking uterine size and relieved the pressure onto her great vessels, which allowed for postponing of surgical intervention until her hemoglobin was stable and planning intervention electively and in a minimally invasive fashion, as opposed to emergency surgery at time of admission in the face of heavy bleeding with multiple transfusions and an operation requiring a large midline laparotomy.

Neuroexam and neuropsychology assessment To controls for more fundamental deficits

2.2. Neuroexam and neuropsychology assessment
To controls for more fundamental deficits in facial motor function, neurologist ratings of orobuccal apraxia and spontaneous facial expressiveness were considered. To assess orobuccal apraxia, patients were asked to imitate 1–2 orobuccal gestures (e.g., “show me how you brush your teeth”). Orobuccal apraxia was scored as either present (1) or absent (0). General spontaneous facial expressiveness was rated on a 4-point scale by the examiner (0 = normal, 4 = severe loss of spontaneous expression).
2.3. Additional socioemotional testing
2.3.1. Face-to-face
Emotion reading was tested using The Awareness of Social Inference Test-Emotion Evaluation Test (TASIT-EET), which consists of 14 brief (~ 20s) video clips of actors depicting dynamic, multimodal expressions of emotion (happiness surprise, anger, sadness, fear, disgust, and neutral) using semantically neutral scripts (McDonald et al., 2003). Once the video ended, the participant selected the perceived emotion from a list of responses displayed on the video screen (range = 0–14).
2.3.2. Caregiver reports:
Interpersonal warmth was assessed using the warmth facet of the NEO-Five Factor Inventory – Extraversion subscale (NEO-PI) (Costa and McCrae, 1992). The warmth facet is an 8-item subscale under the domain of extraversion which captures aspects of friendliness, spontaneous expression, and warmth (e.g., “S/he is known as a warm and friendly person”), and is answered on a 5-point (1 = low; 5 = high) Likert scale (8–40 range).
Empathy was measured using the Interpersonal Reactivity Index (IRI) – empathic concern (EC) subscale (Davis, 1983). The IRI-EC is a 7-item subscale of the IRI that together measures the tendency to be emotionally affected and concerned about others in distress (e.g., “seeing someone being take advantage of makes the patient feel protective towards them”). Informants rated how well each statement reflected the current behavior of the study participant on a 5-point scale of (1 = does not describe at all; 5 = describes very well; 7–35 range).
2.4. Voxel-based morphometry
All patients had a structural MRI scan on a 3 T, Magnetom VISION system (Siemens Inc., Iselin, N.J.) equipped with a standard quadrature head coil. A volumetric magnetization prepared rapid gradient echo MRI (MPRAGE, TR/TE/TI = 10/4/300 milliseconds) was used to obtain T1-weighted images of the entire brain, with 15-degree flip angle, coronal orientation perpendicular to the double spin echo sequence, 1.0 × 1.0 mm2 in-plane resolution and 1.5 mm slab. All imaging was done within 3 months of the experimental session at UCSF. Scans were checked for motion artifact and those with excessive movement were not preprocessed for structural VBM analysis.
VBM preprocessing and analysis were performed using the VBM8 toolbox (http://dbm.neuro.uni-jena.de/vbm/) and SPM8 software package (www.fil.ion.uc.ac.uk/spm/software/spm8). The images were visually inspected for artifacts, bias-corrected, and tissue classified (gray matter, white matter, cerebrospinal fluid segments). This was followed by spatial normalization of the segmented images to MNI space with a 1.0 mm cubic resolution using affine and nonlinear transformations with the diffeomorphic anatomical registration through exponentiated lie algebra (DARTEL) method, as implemented in the toolbox (Ashburner and Friston, 2005 ; Ashburner, 2007). The DARTEL method was also used to create a customized template from older healthy controls (n = 300). In all preprocessing steps, default parameters of the VBM8 toolbox were used, with the exception of using the light clean-up procedure in the morphological filtering step. The spatially normalized, segmented, and modulated gray matter images were smoothed with an 8-mm FWHM isotropic Gaussian kernel.

d e Filter F View the MathML sourceGF ss

d4 = 3.21252e?37
Filter F1 View the MathML sourceGF1=(ss+sc1)1+R3R2ssc2+1+sc2/sc3s+s2/sc3 R3/R2 = 210.53
fc1 = 2.40 Hz
fc2 = 16.75 Hz
fc3 = 7.234 kHz
Filter F2 View the MathML sourceGF2=11+s/sc2 fc = 16.389 kHz
Table options
Table 3.
melanocortin 1 receptor Input impedance and gains of the transmitting and receiving circuits.
45 Hz 90 Hz 360 Hz 1440 Hz 5760 Hz
GTX (dB) 0.0 ?0.0001 ?0.0014 ?0.0219 ?0.3746
GTX (°) 0.236 0.473 1.890 7.564 30.372
GRX (dB) 23.1559 23.5006 23.5839 23.0667 16.9741
GRX (°) 25.243 10.397 ?10.082 ?52.823 ?192.418
Rin (GΩ) 0.1795 0.0226 ?0.0249 ?0.0249 ?0.0095
Cin (pF) 0.287 0.288 0.289 0.295 0.335
Table options
As an example, the nodal equation at the TX1 transmitter circuit (conductor 2) can be written:
equation(9)
View the MathML sourceIS(2)=iωVoGTRCT=iω[V(2)-V(1)](CT+CS)+iω∑n=17V(n)KM(2,n)
Turn MathJax on
which implies that: KE(2, 2) = ?KE(2, 1) = ?KE(1, 2) = CT + CS (symmetrical matrix) and a term (CT + CS) in KE(1, 1) (see Table 4). The source term IS(3) relative to TX2 is developed in the same way as for TX1. Since the TX1 and TX2 circuits are identical, but with opposite signs for the voltage source, IS(3) = ?IS(2). All other components of IS are equal to zero because of the absence of any source and because IS(1) = IS(2) + IS(3) = 0.
Table 4.
Electronic matrix [KE].
1 (HUY) 2 (TX1) 3 (TX2) 4 (RX1) 5 (RX2) 6 (RP1) 7 (RP2)
1 (HUY) 2(CT + CS) + 2Cin? + CRP ?(CT + CS) ?(CT + CS) ?Cin? ?Cin? 0 ?CRP
2 (TX1) ?(CT + CS) CT + CS 0 0 0 0 0
3 (TX2) ?(CT + CS) 0 CT + CS 0 0 0 0
4 (RX1) ?Cin? 0 0 Cin? 0 0 0
5 (RX2) ?Cin? 0 0 0 Cin? 0 0
6 (RP1) 0 0 0 0 0 0 0
7 (RP2) ?CRP 0 0 0 0 0 CRP
Table options
The derived matrix coefficients are KE(3, 3) = ?KE(3, 1) = ?KE(1, 3) = CT + CS, and the term to be included in KE(1, 1) is (CT + CS) (see Table 4).
We recall that melanocortin 1 receptor the potential reference in the simulations is zero at infinity while the potential of the Huygens capsule differs from zero. Accordingly, all simulated electrode potentials have to be shifted by the potential of the reference Huygens capsule before any comparison between measurements and simulations.
Receiver circuit. The receiver circuit includes, for each boom, a coupling capacitor housed inside the boom itself, a preamplifier in the HASI-I box, inside the capsule, as well as a differential amplifier and several circuits for accommodating the signals to the ADC input, on the PWA-A board. The parameters, gain and input impedance have been estimated from the nominal component values or derived from calibration measurements.
The outputs of the two HASI-I1 and HASI-I2 MIP preamplifiers are connected to a differential amplifier in PWA-A. This signal is then fed into the ADC through two filters whose gains GF1 and GF2 are estimated from the components listed in Table 2.
The preamplifiers represent the most sensitive parts of the receiver for two reasons. First, they are connected to the electrodes through very small capacitors, and stray capacitances that cannot be evaluated with accuracy. Second, preamplifiers and PWA-A board are located inside the capsule that is heated with radioactive elements, while the coupling capacitors lie outside at ~?180 °C. Such a configuration is impossible to simulate in laboratory. The preamplifiers have been calibrated in two experimental conditions: (1) HASI-I MIP preamplifiers and booms at room temperature, and (2) HASI-I MIP preamplifiers at room temperature and booms with coupling circuits at ~?180 °C (liquid nitrogen). The construction of an accurate analytical model supported with calibration data, is detailed in Appendix B. For Titan’s environment, the GPA gain analytic expression is shown in Table 2. The numerical values of gain and input impedance components, Rin and Cin, are shown in Table 3.

Figure options Boundaries structure effect on G griseoflavus

Figure options
3.3. Boundaries structure effect on G. griseoflavus
We analyzed the effect of the boundaries structures in G. griseoflavus, the species presented in the three types of boundaries. In the ecological boundary under grazing, G. griseoflavus was negative associated with basal gaps ( Table 2). At ecological boundary EBr, basal gap negatively affected the abundance of G. griseoflavus. Percentages of shrub, grass, and litter cover were correlated positively, whereas the percent of bare buy Fmoc-Ser(tBu)-OH was negatively correlated ( Table 2). In the socio-political boundary, the small mammal responded positively to location in the adjacent patches of Prosopis woodland under restoration, with the presence decreases as it approaches to the transition area. Moreover, it was associated with smaller basal gaps, and a lower percentage of bare soil ( Table 2). For the three ran models, it was not identified an effect from the season or for the year, therefore, the random effect was not present.
Model selection, based on AICc comparisons, using Generalized Linear Mixed Model (GLMM) to describe the abundance of G. griseoflavus within ecological and socio-political boundaries. Season and year were included as a random factor. HL = Hosmer and Lemeshow (1989) test. The best-fitting model was chosen according to the lowest Akaike\’s information criterion (AIC). The difference in AIC from the best-fitting model (ΔAIC) and the Akaike weights (wi) are also provided. Competing models were those with ΔAICc < 2. For the selected model (in bold), positive association for a given variable is indicated by (+), negative by (?). Model HL value (p value) AICc ΔAICc wi EBg boundary: Larrea shrubland – Prosopis woodland under continuous grazing conditions; EBr boundary: Larrea shrubland – Prosopis woodland under passive restoration; SPB boundary: Prosopis woodland between continuous grazing and passive restoration. Table options 4. Discussion 4.1. Small mammal response to boundaries The division of a landscape into administrative, ownership, or management categories creates boundaries that likely enable habitat change and therefore the fragmentation of contiguous land covers (D?browska-Prot and Wasilowska, 2012 and Dallimer and Strange, 2015). Previous studies in agricultural matrix revealed that boundary habitats are less disturbed than agricultural fields, maintaining high plant cover throughout the year, thereby providing good habitat conditions for mobile organisms as small rodent species (Hodara and Busch, 2006 ; Bilenca et al., 2007). However, in drylands, where cattle grazing is the most extensive land use, habitat conditions across boundaries and their consequences on mobile organisms have been almost unexplored (but see Wilson et al., 2010). Our results indicate that habitat structure of socio-political boundaries have a relevant impact on the abundance of desert small mammals compared with ecological boundaries. As we expected, the socio-political boundaries were more contrasting in habitat variables than the ecological ones. This high contrast was perceived by small mammals as quality changes across boundaries, leading to less richness in the mammal assemblage. But our prediction was not fulfilled for abundance, due to socio-political boundaries present the highest abundance comparing with ecological boundaries.

Vegetation data and analysis Plant

2.3. Vegetation data and analysis
Plant and ground cover data has been collected since 1995 on long term plots that were designed to reflect conditions on a cross-section of the conservation area. Locations of these plots were selected to represent conditions across all ecological sites and grazing pastures (USDA-NRCS, 2003). Plots included five pairs of grazed/exclosure plots matched for soil, slope, Ecological Site, and initial cover. A sub-set of plots was sampled every year from 1995 to 2014. This paper focuses on analyses of plot readings conducted from 2004 to 2014 because methods and samples sizes were modified in 2004 (Gori et al., 2010). We restricted analyses to plots with four or more readings over this btk inhibitor 11-year span, which resulted in 30 plots and 206 readings (Table 1, Supplemental Materials Table S1). Data collection during this time used standard line-point intercept methods, with additional shrub foliar cover measurements taken periodically using line intercept methods (Herrick et al., 2009). The choice of which plots to read each year was neither systematic nor completely at random. While completely random sampling is ideal from a statistical perspective, data collection on working landscapes tends to have other constraints such as limitations in staffing or other logistical considerations. In this case we saw no systematic biases in sampling that violated key assumptions of repeated measures analyses that accommodate missing data and unbalanced sampling designs (below).
To characterize changes in vegetation through time, we used General Linear Models with Mixed Effects for longitudinal analyses (mixed-effects models), using the MIXED procedure in SAS version 9.3 (SAS Institute Inc., Cary NC). This approach accounted for some of the inherent variability among plots and accommodated missing data for plots not measured in all years and a sampling design that was unbalanced for some parameters. Trend analyses were run for total perennial grass basal cover (square root transformed to normalize residuals), bare ground (square root transformed), and leaf litter (no transformation needed). Trend models were run with one to three fixed effect variables: year, topographic soil groups (described below), and an interaction term to test whether trends differed among soil groups. We used a plot identification variable as a random effect to account for repeated measures at the same locations over time, and pasture name as an additional random effect to account for the potential lack of independence of some plots located on the same pasture (Table 1). Variability of cover measurements within plots matched ARH1 covariance structure better than other plausible options evaluated (Variance Components, Unstructured, Compound Symmetry, AR1, ARH1, ANTE1, TOEP and TOEPH). Pasture covariance structure was best described by Variance Components (also evaluated: Compound Symmetry). Random effects of plot and pasture both accounted for substantial amounts btk inhibitor of variation.