Depletion or functional deficiencies of any BM component are lethal at some stage of development or around birth, though BM defects vary between organs and tissues. Lethality problems were overcome by developmental stage- and skin-specific gene targeting or by cell grafting and organotypic (3D) cocultures of normal or defective cells, which allows recapitulating BM formation de novo. Thus, evidence is accumulating that BM assembly and turnover rely on mechanical properties and composition of the adjacent ECM and the dynamics of molecular assembly, including further “minor” local components, nidogens largely functioning as catalysts or molecular adaptors and perlecan as bridging stabilizer. Collectively, orchestration of BM assembly, remodeling, and the role of individual players herein are determined by the developmental, tissue-specific, or functional context.The epidermis functions in skin as first defense line or barrier against environmental impacts, resting on extracellular matrix (ECM) of the dermis underneath. Both compartments are connected by the basement membrane (BM), composed of a set of distinct glycoproteins and proteoglycans. Herein we are reviewing molecular aspects of BM structure, composition, and function regarding not only (i) the dermoepidermal interface but also (ii) the resident microvasculature, primarily focusing on the  In skin the epidermis represents the outer barrier of the organism, providing protection against physical, chemical, and microbial impacts of the environment. It should be mentioned beforehand that skin in general is serving multiple other functions , thus representing our second largest organ only surpassed by the vascular system. However, these other issues are beyond the scope of this paper. The skin consists of two morphologically distinguishable compartments, the epidermis and the dermis, which communicate in various ways and at different levels to establish, maintain, or restore tissue homeostasis. While in skin the dermis bears the main mechanical load and provides also insulation, the vital barrier function at the outer surface is accomplished by the epidermis which is a constantly renewing, stratifying, and keratinizing epithelium , 2. Specα-/β-dystroglycan [α6β4 which is normally connected to the basal part of the keratin network via the long cytoplasmic tail of the β4 subunit and internal plaque proteins (outlined later). Thus, during BM assembly α6β4 becomes an integral part of hemidesmosomes which represent the firm epidermal adhesion points to the BM [α2β1 and α3β1 covering lateral and ventral surfaces of basal cells, while α6β4 is largely restricted to the ventral site opposed to ECM or BM [α6β4, largely expands into suprabasal layers, which reflects a severe reduction in cell and tissue polarity [Besides their prominence in skin, BMs support all epithelia and endothelia, enwrap Schwann cells and nerve extensions , 21, musroglycan –44, syndroglycan , 45–47, roglycan –51; for roglycan ). Integrroglycan , 53; revroglycan ), and ECroglycan –59. The o the BM –62. In tCM or BM , 49, 51.polarity –65. Lastpolarity –72.α, β, and γ chains by noncovalent bonds, forming large two-dimensional sheets , forming focal adhesion contacts, which promotes cell migration. Sequential cleavage of distinct laminin modules leads to strong cell adhesion via α6β4 and hemidesmosome formation . Iminin-5; ) . Cnin-5r”; –132. Tetnin-5r”; , 133, whnin-5r”; . Severalnin-5r”; , 126, manin-5r”; , 136 andnin-5r”; . Some diThe BM is connected to the dermis underneath by anchoring fibrils, loop structures of collagen VII, which bind to laminin-332 by their NC-1 domains  and are α1 instead of the α5 chain [For the following it should be explicitly stated that in skin the components collagen IV and VII, and perlecan are synthesized by both epidermal keratinocytes and dermal fibroblasts, while the main source of nidogens are the fibroblasts –146 and α5 chain , 151.Since defects in the structural components of BM, that is, respective laminins and collagen IV, are not compatible with early embryonic development, we like to focus here mainly on deletions or functional defaults of the bridging molecules nidogen and perlecan. Complete perlecan deficiency is lethal for mouse embryos at the midgestational stage primarily due to heart failure , 105. Bein vitro [γ1 chain showed specifically defects of the urogenital tract, kidney, and lung but not any detectable anomalies of the cutaneous and capillary BMs [γ3 chain, harboring respective binding sites, may play a role as well. Genetic ablation of nidogen-1  or nidogin vitro , 145. Atlary BMs . Presumalary BMs , while iAnother, apparently restrictive, regulator of BM assembly “extracellular matrix protein 1”/ECM1 came across when analyzing skin biopsies of lipoid proteinosis (LP) patients with mutations in the ECM1 gene  both cell types behave very differently in conventional cultures on plastic dishes and (ii) in addition they intensively communicate with each other or the ECM in vivo which regulates growth and gene expression determining the skin phenotype. In the dermis fibroblasts are embedded in ECM  and acquire a spindle-shaped morphology [in vitro. Differently, keratinocytes form coherent cell layers in vitro like in vivo, undergoing epidermal differentiation. However, this process is incomplete and resembles somehow a regenerating wound epithelium. To a great part this is due to the conventional  culture conditions, where the direction of nutrient supply is reversed from basal cell attachment sites in vivo  to the upper epithelial surface (in vivo providing the water barrier). Despite of the different physiology of human and adult mouse skin, these changes were comparable in keratinocyte cultures from newborn mouse or human tissue [2+, or lowering retinoid levels [ras-gene) generally maintained their more complex, atypical keratin profiles in vitro, by and large related to their tumorigenic properties .γ1 chain laminin, presumably laminin-511, and collagen IV. Accordingly integrins, initially decorating cell surfaces in lower layers, became restricted in distribution, that is, β1 integrins to the basal cell surface and α6β4 mainly to the cell-matrix interface. Furthermore cell proliferation decreased as visualized by BrdU uptake, finally labelling about 5% of basal cells, a rate seen in normal epidermis [Transplants of cultured mouse keratinocytes on the back of immune compatible mice had demonstrated the full differentiation potential of these cells, exposing them again to an authentic microenvironment , 161. Lipidermis  which isβ1-integrins and α6β4 [γ2 chain expression and location have been observed in squamous cell carcinomas of human skin and other carcinomas , tissue polarity, and differentiation in an experimental human tumor model benign and malignant HaCaT-ras cells were transplanted on nude mice, revealing unbalanced, but non-invasive or invasive, tissue-destructive growth, respectively , 171. Reand α6β4 , similarand α6β4 ; referenand α6β4 ). In theand α6β4 . Comparacinomas  from keratinocytes , 191. Inγ1 fragment (γ1III3-5 module) harboring the binding site in the γ1III4 module [α6β4 were only moderately affected, showing still a distinct continuous staining at the interface. BM assembly could be reverted by delayed onset or reactivated by the discontinuation of the treatment, respectively, demonstrating the dynamics of this process. So, already assembled BM structures disappeared again by late treatment with the γ1 fragment, while BM formation was resumed when treatment was halted. Epidermal morphology and differentiation remained largely normal as judged by staining for K1/K10 and “late” markers. Examining ultrastructure revealed that the γ1 fragment completely blocked BM formation (no lamina densa visible) and further abolished the formation of hemidesmosomal adhesion complexes. Consequently keratin filaments retracted from the ventral cellular aspect, while basal cells adhered directly to “dermal” collagen fibrils. For bridging of laminin and collagen IV networks nidogen-laminin binding had been assumed to be the initial step , 109. Ap4 module , 195. ReRemarkably, immune EM revealed that BM constituents and hemidesmosomes were still present, though somewhat reduced and widely dispersed. This was in line with analyzing protein extracts of separated dermal and epidermal parts of cocultures. Thus, the major BM components nidogen-1, collagen IV, laminin-511 (laminin-10), and laminin-332 were detected by immunoblotting at similar levels with no signs of aberrant processing .γ1III3-5 fragments (blocking nidogen binding) next to those experimental tumors in nude mice .Macroscopically and histologically, the skin of mice lacking both nidogens appears to be fairly normal, showing no anomalies of the dermoepidermal BM by immunofluorescence . Only soCollectively the data indicate that in skin primarily the laminin composition of the two BM types  dictatesα3 G45 fragment) has been proposed for invasiveness of carcinomas in several reports, which rather show an extensive turnover of other BM components [A reverse picture was seen in skin biopsies of lipoid proteinosis (LP) patients. In this disorder dysfunction or lack of ECM1 causes excessive BM deposition at the dermoepidermal junction but most pronounced around small vessels, where these enormously sized, multiple BMs obviously impair vascular function , 159; al were marmponents , 219. Almponents . in vitro skin model that they can both induce and accelerate BM formation including epidermal BM adhesion structures. Furthermore the data strongly suggest that nidogens also function as instant stabilizers of molecular interactions, which is particularly important for fast tissue or BM remodeling. Perlecan, on the other hand, apparently provides more spacious links between laminin and collagen IV networks  which mwedded,” . So it win vitro and in vivo models, a promising task would be the generation of mouse strains with skin-specific constitutive or inducible ablation of both nidogens. Though not an easy task, this would permit to study BM formation or stability and epidermal barrier function in adult animals avoiding the deleterious systemic drawbacks. Last not least it is of great medical interest that compromised BM structures in tissues and the vascular system are a major hallmark of cancer progression and invasiveness. Nidogens exhibit a rather high susceptibility against proteases like matrix metalloproteases [The combination of genetic and 3D coculture approaches in future studies appears promising to define further isoform-specific effects, as reported for laminin-511/521  and moleroteases , 233, meroteases , 235, orroteases  getting


Mirounga leonina;n = 12) were used, in conjunction with satellite telemetry and environmentaldata, to examine their foraging habitat and diet during their first foraging migration. The trophicposition of seals from Macquarie Island  was estimated usingstable carbon (δ13C) and nitrogen (δ15N) ratios along thelength of the whisker, which provided a temporal record of prey intake. Satellite-relayed dataloggers provided details on seal movement patterns, which were related to isotopic concentrationsalong the whisker. Animals fed in waters south of the Polar Front (>60°S) or withinCommission for the Conservation of Antarctic Marine Living Resources (CCAMLR) Statistical Subareas88.1 and 88.2, as indicated by both their depleted δ13C(<−20‰) values, and tracking data. They predominantly exploited varying proportionsof mesopelagic fish and squid, and crustaceans, such as euphausiids, which have not been reported asa prey item for this species. Comparison of isotopic data between sub-yearlings, and 1, 2 and 3 yrolds indicated that sub-yearlings, limited by their size, dive capabilities and prey capture skillsto feeding higher in the water column, fed at a lower trophic level than older seals. This isconsistent with the consumption of euphausiids and most probably, Antarctic krill (Euphausiasuperba), which constitute an abundant, easily accessible source of prey in water massesused by this age class of seals. Isotopic assessment and concurrent tracking of seals aresuccessfully used here to identify ontogenetic shifts in broad-scale foraging habitat use and dietpreferences in a highly migratory predator.Development in foraging behaviour and dietary intake of many vertebrates are age-structured.Differences in feeding ecology may correlate with ontogenetic shifts in dispersal patterns, andtherefore affect foraging habitat and resource utilization. Such life-history traits have importantimplications in interpreting tropho-dynamic linkages. Stable isotope ratios in the whiskers ofsub-yearling southern elephant seals ( The interplay between the physical and biological regimes of the Southern Ocean e.g. food, space and mates) is one of the main hypothesises advanced to explainnatal dispersal in the life history of most species Natal dispersal is a fundamental, but poorly understood, demographic parameter Mirounga leonina) is such an example of a polarspecies which exhibits an extreme natal dispersal strategy The southern elephant seal (M. angustirostris) e.g. relatedto size, sex, diving capacity), familiarity with habitat, temporal shifts in haul-out behaviour andreduced mortality risks of extensive movement Surviving seals disperse over many thousands of kilometres 13C/12C; δ13C) and nitrogen  isotopes in various body tissues, is being increasingly used to study theforaging habitat and trophic position of highly migratory animals What is known about the diet of this species stems largely from studies of stomach contents andfaecal analysis e.g.13C) concentrations change by only ∼0.8 to 2‰ per trophic level, reflectingthe source of carbon at the base of the food chain e.g.15N) concentrations in consumer tissues typically increase at ∼3‰ per trophiclevel Carbon (i.e.δ13C and δ15N) of seals in relation to foraging location, water masstype and seal age.In this study, stable isotope and satellite telemetry for consumers and correspondingenvironmental data are combined to quantify the feeding habits and trophic position of sub-yearlingelephant seals in relation to habitat during their first feeding migration from Macquarie Island.The specific aims of this study were to determine: 1) the growth rates of whiskers of sub-yearlingsin the six months after weaning, and 2) the trophic position .Facial vibrissae (whiskers) were collected from southern elephant seal pups equipped withSatellite Relayed Data Loggers , which consisted ofa data logger interfaced to a 0.5-W Argos radio frequency unit Protomyctophum tenisoni, n = 8; Electronaantarctica, n = 10; Gymnoscopelus piabilis,n = 2; G. nicholsi, n = 10 and G.fraseri, n = 8) were collected by the RV La Curieuseduring bathypelagic trawls, to the northeast of the Kerguelen Archipelago  in June 1998  were obtained from the stomach contents of juvenile southern elephantsseals  during their annual haul-out periods as they returned ashore atMacquarie Island , from November 1997 and December 2000. Details of the capture, handling and stomach lavaging of study animalsare provided elsewhere In the absence of available prey stable isotope data for this region of the Southern Ocean weused published and unpublished values corresponding to latitudinal ranges similar to this populationof juvenile southern elephant seals, although outside the current foraging range. We have assumedthat despite the geographic disparity, the prey isotope values will be broadly consistent with thosewithin the seals foraging areas. Mid-latitude (<55°S) specimens of fish, euphausiids , hyperiid amphipods  and squid  were collected by the Japanese TRV Umitaka Maru usingpelagic trawls in the Dumont d’Urville Sea, ranging from Terre Adélie to the MertzGlacier tongue, in George V Land  aspart of the Collaborative East Antarctic Marine Census (CEAMARC) in January/February 2008 High latitude (>60°S) specimens of fish  were discarded. The methodology used to fit the model to elephant seal location data isdescribed in detail in Jonsen et al. We fitted a first-difference correlated random walk switching (DCRWS) model Using the ARS locations, we calculated the proportion (%) of time spent by the seals indefined ACC Inter-Frontal Zones (IFZs). To map the position of ACC fronts, we used 19 years (1992 to2011) of weekly sea surface height (SSH) gradients. The approach used to identify fronts in SSH datais described in detail by Sokolov and Rintoul To map fronts in the Southern Ocean, twelve SSH contours were used, as in Sokolov and RintoulEach elephant seal satellite location was ascribed to an IFZ These IFZs were summarised into seven distinct zones as follows: 1. S of SACCF-S ; 2.ACC to PF-S ; 3. PF ; 4. PF to SAF (IFZ 7); 5. SAF ; 6. SAF-N to SAZ (IFZ10), and 7. SAZ to STZ-S (IFZ 11). Numbers in brackets correspond to IFZs above.The whiskers were cleaned with successive rinses in a 2∶1 chloroform:methanol solution, andthen dried in an oven at 60°C for 72 hours. The twelve post-trip whiskers were weighed andsectioned into approximately 2 mm sections. The sections from each whisker were numberedsequentially, starting from the base, in order to track the temporal integration of isotope valuesalong the length of the whisker.15N-depleted molecule) to proteinare found in undarkened, darkening and darkened parts of squid beaks, with much more chitin inundarkened than in darkened parts 15N relative todiet and were therefore used for stable isotope analysis. The lower beaks of squid were cleaned withsuccessive rinses of distilled water, before the wing parts of beaks were cut away using scissors.Wings of lower beaks were then dried in oven at 60°C for a minimum of 16 hours and ground tofine powder. Relative abundance of 13C and 15N were determined using anIsoprime  continuous-flow isotope-ratio mass spectrometer. Results are reported usingstandard δ notation in parts per thousand (‰) relative to Pee Dee Belemnite (PDB) forδ13C and atmospheric N2 (Air) for δ15N asfollows:13C orδ15N, and R is the ratio of 13C/12C or15N/14N.Isotopic analysis was performed on the white muscle of fish, the mantle and lower beaks of squid,and whole specimens of amphipods and euphausiids. Muscle tissues (fish and squid) and wholespecimens were freeze dried and ground to fine powder before lipids were removed from all samples13C and δ15N,respectively. Stable isotope analysis was performed by the Environmental Biology Group, ResearchSchool of Biological Sciences, Australian National University (ANU), Canberra, Australia.Replicate measurements of internal laboratory standards (Alanine) indicate measurement errors<0.20 ‰ and <0.21 ‰ for δin-utero development and post-partum, whileisotopic values in the post-trip whisker reflect a shift from maternal investment to independentforaging. As pups mature, the process of weaning leads to a change in isotopic signal when theassimilation of carbon and nitrogen shifts to sources other than mother’s milk, such asfree-ranging prey Southern elephant seals undergo a 24 day lactation period 15N values along the length of the post-trip whisker to the basalsection of the pre-trip whisker . Note that thepoint of interception occurs during the drop of 3.9‰ in δ15N from 12.4 to8.5‰, which we interpret as a trophic level shift from mother’s milk and/or fasting toindependent foraging at sea. Once the lowest δ15N is reached (i.e.the transition is complete), we consider this and all subsequent samples to represent amino acidsderived from independent foraging. The proportion (mm) of post-trip whisker that representsδ15N values incorporated during independent foraging at sea is 14 mm  surface waters, ranging from high δ13C values inwarm subtropical waters in the north, to depleted values in cold Antarctic waters in the south 13C values of top predators in the Southern Oceane.g. SAF, PF and sBdy Within the Southern Ocean, there is a well defined geographical δThe greatest proportion of seal ARS locations occurring in a particular IFZ defined their habitatuse. Isotopic signatures of elephant seals were then grouped according to habitat.13C and δ15N values of marine organisms from mid- and highlatitude Southern Ocean waters to infer the diet of elephant seals in relation to foraging habitat(We used δ habitat. FormidM. hyadesi and H. eltaniane)contained in stomach lavage samples of returning juvenile Macquarie Island southern elephant sealswere also examined . Cephalopod beak structure is species specific 13C and δ15N valuesof small, medium and large LRL sized beaks according to species (for details see The beaks of two species of squid (Electrona antarctica), euphausiids(Euphausia triacantha) and amphipods (Themisto gaudichaudii)sampled in pelagic waters at depths of <500 m ; thedeep-sea squid Bathyteuthis abysicola, sampled at depths of <1000 m , and the glacial squid Psychroteuthis glacialis, sampled at depths of<200 m in high latitude waters . Values of Antarctic krill(Euphausia superba) sorted from emperor penguin (Aptenodytesforsteri) regurgitates from Adélie Land From high latitude waters (>60°S) north of Adélie and George V Land in the IndianOcean sector, we used myctophids  and accordingly, contain lower δ15N values comparedto the mantle (∼3.5‰) and buccal mass (∼2.6‰) soft tissue of cephalopods 15N values of H. eltaninae andM. hyadesi beaks prior to isotopic comparison with elephant seals and other marineorganisms.We used whisker-specific isotopic fractionation values for δTo assess age-related shifts in trophic level and diet structure we compared the isotopicsignatures in whiskers of sub-yearlings  to that of one(n = 5), two (n = 40) and three(n = 27) year old elephant seals sampled between 1999 and 2000 from MacquarieIsland 13C and δ15N) of migratory, sub-yearling elephant seal whiskers wereinfluenced by sampling year or the location of foraging (as inferred by the proportion of ARSlocations occurring in IFZs) we used multivariate analyses of variance (MANOVA) fitted with theMANOVA function in R. To determine if δ13C or δ15N values wereinfluenced by sampling year or the location of foraging separately, we used a linear model fittedwith the lm function in R, with whisker δ13C or δ15N values as thedependent variables, year and foraging location as factors and the two way interaction term. We usedAnalysis of Variance (ANOVA) along with Tukey’s Honestly Significant Difference (HSD) post-hocanalysis to indicate where response variables differed. Proportional data were arcsine transformedprior to statistical analysis.We performed all statistical analysis using R version 2.15.0 13C or δ15N,with sampling year as a random factor, and age and sex as fixed factors. If the distribution wassignificantly different from normality, the data were log-transformed and normality verified.Interactions between sampling year and dependent variables were examined. Effects of age and sexwere, likewise, tested systematically in all analyses. We further assessed qualitative patterns ofvariation in habitat and trophic position through graphical examination of δ13C andδ15N values, respectively. We assessed significance for statistical tests at the 0.05level. Mean values are given ± standard deviation (SD).Linear mixed-effects models were used to examine the effects of age class and sex on variation ofstable isotope values. The dependent variable was either δPost-trip whiskers ranged in length from 42 to 144 mm forδ15N . A difference of ∼4.0‰ inδ15N values reflects more than one trophic level of difference (∼3.0‰).The decline in δ15N values  for δforδ15N . From thll again . The num15Nabundance along the temporal span  showed only an initial increase in ral span , withwhral span . For thebundance  was stilin-utero/lactation/fasting rangedin length from 85 to 102 mm  and in uni-variate analysis . Mean isotopic values of sub-yearling elephant seal whiskers foragingin both the ACC to PF-S and PF were −21.2±0.4‰ forδ13C, and 9.4±0.7‰  for δ15N  of elephant seal whiskers and the mean latitude oftheir foraging locations , with considerable overlap in bothδ13C and δ15N values and mean latitude of foraging location amongseals  and potential prey items were plotted together (E.antarctica (δ13C: −23.2‰ and δ15N: 8.3‰),G. fraseri , Krefftichthys anderssoni (δ13C: −22.3‰and δ15N: 7.6‰), and the squids Kondakovia longimana(δ13C: −25.1‰ and δ15N: 7.6‰) andGaliteuthis glacialis (δ13C: −24.7‰ andδ15N: 8.1‰), and lower trophic level mesopelgic fish, such as P.tensioni , and the squid M. hyadesi .Mean δtogether . Seals f13C and δ15N values recorded in the seals , E. superba and T.gaudichaudii , and higher trophic level fish and squid  and M. hyadesi or euphausiids and squid,such as E. frigida  and P. glacialis or even a combination offish, squid and crustaceans.The δnd squid . In at l3,126 = 10.116, P = 0.001),reflecting spatial variability in foraging habitat between age classes. Mean δ13Cvalues ranged from −21.4±0.6‰ (range = −21.9 to−20.5‰) for sub-yearlings, compared to −20.2±0.9‰(range = −21.7 to −17.7‰), −20.2±1.0‰(range = −22.3 to −18.2‰) and −20.7±0.9‰(range = −23.0 to −19.1‰) for one, two and three year olds,respectively .Sub-yearlings also appeared depleted in δ13C compared to three olds, however nosignificant variation was detected (P = 0.06).Stable carbon values in whiskers showed significant variation with age . Mean δ15N values ranged from 9.6±1.0‰(range = 7.9 to 13.9.0‰) for sub-yearlings, compared to11.0±1.3‰ (range = 8.8 to 13.3‰), 11.2±1.4‰(range = 8.7 to 13.8‰), and 10.7±1.1‰(range = 9.4 to 13.2‰) for one, two and three year old age classes,respectively .Some one and two year old seals however, showed similar depleted δ13C andδ15N values as sub-yearlings, indicating overlap in both foraging habitat and trophicposition.Stable nitrogen isotopic values in whiskers showed significant variation with age  unable to access larger, higher trophic levelprey deep in the water column (>300 m), feeding closer to the surface where lower trophic levelcrustaceans, such as euphausiids, offer an abundant source of prey in addition to fish andsquid.Using a combination of tracking and stable isotope data, we found the diet of sub-yearlingsouthern elephant seals from Macquarie Island, foraging predominantly in waters at or south of thePF (∼60°S) and within CCAMLR Statistical Subareas 88.1 and 88.2, to be consistent with theconsumption of mesopelagic fish and squid, and crustaceans. The predominance of mesopelagic fish andsquid in the diet of older juvenile and adult seals has been well documented 13C and δ15N values of sub-yearlings however is the consumptionof euphausiids (and copepods and amphipods). Moreover, tracking data confirms that the foragingrange of sub-yearlings at this time of year overlaps with the maximum sea-ice extent and knowndistribution of euphausiids in this region of east Antarctica Precaution must be taken in interpreting dietary trends inferred from isotopic results, as thecombination of prey types using this technique can never be accurately quantified From a conservation and management viewpoint, our findings have important implications. Weprovide evidence of a new crustacean (krill) predator, the southern elephant seal, within watersmanaged under CCAMLR. Our findings show that regions associated with the Ross Sea constituteimportant foraging grounds for southern elephant seals during their critical, first year of life inwhich they transition to independent foraging.Bio-logging and natural biochemical tracers are increasingly being used to provide spatiallyexplicit dietary information for highly migratory marine predators i.e.from the distal to proximal region. However, this pattern was more pronounced in δ15Nvalues, with an initial rise and distinct peak in 15N abundance indicative of the shiftin food source from in-utero development to mother’s milk during lactation.Nursing offspring essentially feed at a higher trophic level than their mothers do as has been shownin several species, including pinnipeds 15N compared to that of their mothers fromearly  to late lactation , while in Steller sealions (Eumetopias jubatus), Stegall et al. 15N enriched (+2.0‰) over their diet(ingested mother’s milk) during lactation.Accounting for the growth dynamics of whiskers enables the correct interpretation of thetime-series of diet information incorporated along the length of the whisker 15N values, equivalent to more than one trophic level of adifference (∼3‰) and indicative of a diet shift from mother’s milk and/or fastingto free ranging prey. We suggest that the reason we do not see this decline in δ15Nvalues in the other five seals is that slower whisker growth meant that the new material,synthesized after weaning, never appeared above the skin and was therefore not sampled. The portionof whisker grown during independent foraging at sea accounted for 11.7±9.5 mm or9.1±6.4% of total growth, indicating that an average of 12 mm of growth is containedunder the skin for the latter group of seals. In contrast, the portion of post-trip whisker grownduring in-utero/lactation/fasting accounted for 93.4±6.9 mm or77.2±5.7% of total whisker growth (n = 8). In elephant seals,whiskers are established early during in-utero development with foetal whiskersgrowing as much as 27 mm in length, and are not shed during their annual pelage moult but randomlyafter seals are at least two years of age Erignathus barbatus) suggest periods of rapid, somatic growth may bereflected in the growth of the whiskers Of the twelve sub-yearlings with concurrent isotopic and tracking data, seven seals showed asubsequent fall in δi.e. from maternalinvestment to independent foraging. A significant finding of this study was that whiskers collectedafter the first foraging trip were predominantly still composed of in-utero andpre-weaning material as a result of relatively slow whisker growth rates during the 3 to 4 monthforaging trip. The results of this study also highlight the need to sample plucked and not cutwhiskers for future isotopic dietary analysis in order to capture the most recent isotopicinformation contained in the root of the whisker under the skin.In summary, we found whiskers to be extremely useful tools for accessing time integrated dietinformation of elephant seals during their first year of life as we can trace the origin of thesignature incorporated along the length of the whisker, The foraging behaviour of a predator can change in response to the distribution of prey resourcesin a large heterogeneous environment. Identifying changes in the movement behaviour of a predatorcan therefore be informative as to the distribution and consumption of prey resources at a range oftemporal scales. Over a period of 4 to 6 months satellite tracking data showed that sub-yearlingelephant seals dispersed, in some cases thousands of kilometres, to the southeast of MacquarieIsland  and the SACCF  and bathymetric features such asthe MOR. The foraging locations of the single seal which travelled to the southwest of MacquarieIsland were primarily associated with frontal branches of the PF and the SEIR. In this study, theforaging locations of only one seal were primarily associated with waters to the south of the sBdy.The movement and foraging behaviour of sub-yearling elephant seals from this site has beenpreviously investigated 13C values incorporated along the temporal spanof the whisker (as represented by the growth of the whisker) were contained in the portion ofwhisker grown during independent foraging at sea  to high latitude foraging areas (ARS tracks) located ator south of the PF (∼60°S) to the southeast of Macquarie Island (We found that the most depleted δe Island ..15N andreduced whisker growth (∼10 mm), at this time , indicatingthat even though seals were using similar latitudes , they were in fact in different water masses, and there was nodifference in δ13C between those water masses. The structure and flow of the ACC iscomplex, consisting of multiple frontal filaments or branches that are strongly influenced bybathymetry 13Csignatures are not adequate to resolve habitat (water mass) at this scale and consequently, sealslocated to the southeast of Macquarie Island and predominantly associated with frontal branches ofthe PF and SACCF  and the MOR showsimilar δ13C values.There was considerable overlap in the δ15N values ofseals both between years and foraging locations (IFZs) suggesting that all seals fed at a similartrophic level irrespective of foraging habitat. However, there was considerable individualvariability in their diet. Pronounced individual variability has also been described in divingbehaviour within water masses for this species Very little is known about the diet of southern elephant seals during the course of theirmigrations, particularly in relation to core foraging areas. Lavaged stomachs of both juvenile andadult seals returning to colonies, which represent the most recent prey intake at the end offoraging trips, consist largely of mesopelagic fish and squid at Macquarie Island 15N: ∼8‰, such asE. antarctica, G. fraseri, K. anderssoni, G. glacialis, K.longimana and P. glacialis), lower trophic level mesopelagic fish andsquid  and lower trophic level crustaceans  characteristic of that sampled withincolder, high latitude eastern Antarctic waters 15N signatures compared to older juvenile are taken from outside the foraging range of juvenile elephant seals from this site Electrona andGymnoscopelus spp. P. glacialis and H. eltaninae in the stomachcontents of older juveniles from Macquarie Island Aptenodytespatagonicus) Caution must be taken however, in over-interpretation of these observed dietary trends as preydata  in east Antarctic waters duringlate summer/early winter e.g. king penguins androyal penguins, Eudyptes schlegeli) Moreover, the southern boundary of foraging areas of sub-yearlings aligned with the sBdy∼65°S; , coincid13C and δ15N values than older juvenile seals, indicatingontogenetic segregation in both foraging range and trophic position, respectively. These observedisotopic differences are in good agreement with sub-yearlings feeding at a lower trophic level thanolder seals, since their size, dive capabilities and predation skills are limited P. glacialis and Alluroteuthis antarcticusOntogenetic changes in movement patterns, foraging habitat use and diet have been reported forolder juvenile southern elephant seals from Macquarie Island Figure S1Tracks overlaid with state estimates from the two-state first-difference correlated randomwalk switching (DCRWS) model. Tracks of 12 weaned southern elephant seals during their firstforaging migration from Macquarie Island, colour coded by state estimates. Grey: transit locations;light blue, blue and green: Area Restricted Search (ARS) locations for S of SACCF-S, ACC to PF-S andPF zones, respectively.(PDF)Click here for additional data file.Table S113C) and nitrogen (δ15N) values of variousmarine organisms from the Southern Ocean.Stable carbon (δ(PDF)Click here for additional data file.


Dimensionality reduction (DR) enables the construction of a lower dimensional space (embedding) from a higher dimensional feature space while preserving object-class discriminability. However several popular DR approaches suffer from sensitivity to choice of parameters and/or presence of noise in the data. In this paper, we present a novel DR technique known as consensus embedding that aims to overcome these problems by generating and combining multiple low-dimensional embeddings, hence exploiting the variance among them in a manner similar to ensemble classifier schemes such as Bagging. We demonstrate theoretical properties of consensus embedding which show that it will result in a single stable embedding solution that preserves information more accurately as compared to any individual embedding . Intelligent sub-sampling (via mean-shift) and code parallelization are utilized to provide for an efficient implementation of the scheme.Applications of consensus embedding are shown in the context of classification and clustering as applied to: (1) image partitioning of white matter and gray matter on 10 different synthetic brain MRI images corrupted with 18 different combinations of noise and bias field inhomogeneity, (2) classification of 4 high-dimensional gene-expression datasets, (3) cancer detection (at a pixel-level) on 16 image slices obtained from 2 different high-resolution prostate MRI datasets. In over 200 different experiments concerning classification and segmentation of biomedical data, consensus embedding was found to consistently outperform both linear and non-linear DR methods within all applications considered.We have presented a novel framework termed consensus embedding which leverages ensemble classification theory within dimensionality reduction, allowing for application to a wide range of high-dimensional biomedical data classification and segmentation problems. Our generalizable framework allows for improved representation and classification in the context of both imaging and non-imaging data. The algorithm offers a promising solution to problems that currently plague DR methods, and may allow for extension to other areas of biomedical data analysis. This is an issue where the number of variables (features) is disproportionately large compared to the number of training instances (objects)  ∈ ℝN, where uf(c) is the response to a feature operator for pixel c. In the case of gene-expression data, every sample c is considered to be associated with a high-dimensional gene-expression vector, also denoted F(c) ∈ ℝN.In the case of image data , we have derived texture features  on a perN to ℝn were graph embedding (GE) [n, is calculated as the intrinsic dimensionality of ℝN via the method of [F(c) ∈ ℝN is denoted XΦ(c) ∈ ℝn, while the result of consensus DR will be denoted GE, PCA}. For ease of description, the corresponding classification results are denoted Ψ(F), Ψ(XΦ), DR methods utilized to reduce ℝing (GE)  and PCA ing (GE) . These ming (GE) ,48. The ethod of . To rema1, consisting of simulated proton density (PD) MRI brain volumes at various noise and bias field inhomogeneity levels. Gaussian noise artifacts have been added to each pixel in the image, while inhomogeneity artifacts were added via pixel-wise multiplication of the image with an intensity non-uniformity field. Corresponding labels for each of the separate regions within the brain, including white matter (WM) and grey matter (GM), were also available. Images comprising WM and GM alone were obtained from 10 sample slices (ignoring other brain tissue classes). The objective was to successfully partition GM and WM regions on these images across all 18 combinations of noise and inhomogeneity, via pixel-level classification  ∈ ℝN, N = 14,(i) the high-dimensional feature space F(c), denoted XGE(c) ∈ ℝn, n = 3,(ii) simple GE on N, denoted as XMDS(c) ∈ ℝn, n = 3 ,(iii) multi-dimensional scaling (MDS) on distances calculated directly in ℝn = 3.(iv) consensus embedding, denoted F), Ψ(XGE), Ψ(XMDS), The final slice classification results obtained for each of these spaces are denoted as Ψ staining of the histology sections. The cancer areas were then mapped onto corresponding MRI sections via a deformable registration scheme [Two different prostates were imaged n scheme . Addition scheme .ex vivo T2-weighted MRI and corresponding digitized histology images were considered. The purpose of this experiment was to accurately identify cancerous regions on prostate MRI data via pixel-level classification, based on exploiting textural differences between diseased and normal regions on T2-weighted MRI [M embeddings, CreateEmbed) along with their corresponding embedding strengths SelEmbed). The algorithm proceeds as follows,For this experiment, a total of 16 4 Tesla hted MRI . For eactrS) and testing (teS) sets of the data (MRI images) were created.(a) Training  For each element (image) of tr Swere then identified and selected.(c) Those embeddings voted as being strong across all the elements (images) in teS, corresponding embeddings were then combined  to yield the final consensus embedding result.(d) For the data (images) in XGE), and (2) consensus GE denoted as A leave-one-out cross-validation strategy was employed in this experiment. A comparison is made between the pixel-level classifications for (1) simple GE denoted as Ψ(2 with corresponding class labels for each sample [trS) and testing (teS) subsets, and these were utilized within a supervised framework for constructing and evaluating the consensus embedding representation.Four publicly available binary class gene-expression datasets were obtainedh sample ; the purt-statistics as described in [Prior to analysis, each dataset was first pruned to the 300 most class-informative features based on ribed in . The supCreateEmbed is run concurrently on data in tr Sand teS, such that the same subsets of features are utilized when generating base embeddings for each of tr Sand teS.(a) First, SelEmbed is then executed on base embeddings generated from tr Salone, thus selecting strong embeddings from amongst those generated. Strong embeddings were defined based on (b) te Sare then combined within CalcConsEmbed to obtain the final consensus embedding vectors denoted as GE, PCA}, n = 4.(c) Corresponding (selected) embeddings for data in S) and unsupervised  measures of embedding strength were evaluated in terms of the classification accuracy of the corresponding consensus embedding representations.For this dataset, both supervised , but without performing DR on the data. Each such kNN classifier was then used to classify corresponding data in teS. The final classification result for each sample in te Sis based on ensemble averaging to calculate the probability of a sample belonging to the target class. Classifications compared in this experiment were Ψ(F), Ψ(XSSGE), An additional comparison was conducted against a supervised random forest-based k-means clustering [For image data , classification was done via replicated ustering , while fustering . The choTo visualize classification results as region partitions on the images , all the pixels were plotted back onto the image and assigned colors based on their classification label membership. Similar to the partitioning results shown in Figure Classification accuracy of clustering results for images as well as gene-expression data can be quantitatively evaluated as described previously (Section 2). Image region partitioning results as well as corresponding classification accuracies of the different methods  were used to determine what improvements are offered by consensus embedding.F)         , Ψ(XMDS), and Ψ(XGE).Figure ) Figure , Ψ(XMDS)) Figure , and Ψ(X) Figure  are only) Figure  shows mo) Figure . When RF) Figure , Ψ(XMDS)) Figure , and Ψ(X) Figure . Ψ(X˜GE)) Figure , however) Figure . Increas) Figure , Ψ(XMDS)) Figure , and Ψ(X) Figure . Note frAccϕ(F), Accϕ(XMDS), Accϕ(XGE), Accϕ(F), Accϕ(XMDS), Accϕ(XGE)) in 17 out of 18 combinations of noise and inhomogeneity. This trend is also visible in Figure For each of the 18 combinations of noise and inhomogeneity, we averaged the WM detection accuracies t-test was conducted between Accϕ(F), Accϕ(XMDS), and Accϕ(XGE), with the null hypothesis being that there was no improvement via p <0.05) than all of Ψ(F), Ψ(XMDS), and Ψ(XGE) in 16 out of 18 combinations of noise and inhomogeneity.For each combination of noise and inhomogeneity, a paired Students' Accϕ(F), Accϕ(XMDS), and Accϕ(XGE), it can be observed that Ψ(F) and Ψ(XMDS) perform similarly for all combinations of noise and inhomogeneity  shows improved performance at every combination of noise and inhomogeneity as compared to either of Ψ(F) and Ψ(XMDS). F), Ψ(XMDS), and Ψ(XGE), reflecting the advantages of consensus embedding.Comparing WM pixel-level detection accuracy results for consensus embedding using two different ML estimators (median and mean) were averaged over all 10 MNI brain images considered and summarized in Table ConsEmbedMS algorithm in detecting prostate cancer (CaP) on T2-weighted MRI, each row corresponding to a different 2D MRI image. Comparing the pixel-level CaP detection results  in Figures XGE) results in a large false positive error. In contrast, p <0.05) outperform Ψ(XGE) in terms of accuracy and specificity of CaP segmentation over all 16 slices considered.Figure  Figures  appears XSSGE)), as well as supervised classification of the original feature space (Ψ(F)). These results suggest that consensus embedding yields a superior classification accuracy compared to alternative strategies. We posit that this improved performance is due to the more accurate representation of the data obtained via consensus embedding.Table F, which yielded a worse classification accuracy than unsupervised classification (of consensus-GE and consensus-PCA) in 3 out of the 4 datasets. Moreover, semi-supervised DR, which utilized label information to construct XSSGE, was also seen to perform worse than consensus embedding (both supervised and unsupervised variants). We posit that this is because SSAGE does not explicitly account for noise, but only modifies the pairwise relationships between points based on label information (possibly exacerbating the effects of noise). By contrast, any label information used by consensus embedding is used to account for noisy samples when approximating the "true" pairwise relationships between points. The difference in the final embedding representations can be visualized in 3D in Figure The presence of a large noisy, high-dimensional space was seen to adversely affect supervised classification performance of Further, comparing the performance of supervised M ∈ {200, 500, 1000}) in the CreateEmbed algorithm  and code parallelization, computational feasibility and practicability of our method is ensured.Results of quantitative and qualitative evaluation in over 200 experiments on toy, synthetic, and clinical images in terms of detection and classification accuracy demonstrated that consensus embedding shows significant improvements compared to traditional DR methods such as PCA. We also compared consensus embedding to using the feature space directly, as well as to using an embedding based on distance preservation directly from the feature space (via MDS ), and foIn experiments for high-dimensional biomedical data analysis using gene-expression signatures, consensus embedding also demonstrated improved results compared to semi-supervised DR methods (SSAGE ). Evaluaθ). Note we have reported results for a fixed value of θ in all our experiments/applications, further demonstrating the robustness of our methodology to choice of parameters.It is however worth noting that in certain scenarios, consensus embedding may not yield optimal results. For instance, if very few embeddings are selected for consensus, the improvement in performance via consensus embedding over simple DR techniques may not be as significant. This translates to having a sparsely populated distribution for the estimation of the consensus pairwise relationship, resulting in a lower confidence being associated with it. Such a scenario may arise due to incorrectly specified selection criteria for embeddings; however, it is relatively simple to implement self-tuning for the selection parameter . Preliminary results in developing an unsupervised evaluation measure using the R-squared cluster validity index  are extrk parameter in kNN classifiers [Another area of future work is developing algorithms for the generating uncorrelated, independent embeddings. This is of great importance as generating truly uncorrelated, independent embeddings will allow us to capture the information from the data better, hence ensuring in an improved consensus embedding result. As mentioned previously, methods to achieve this could include varying the parameter associated with the DR method (e.g. neighborhood parameter in LLE ) as wellssifiers  or varyissifiers . Note oussifiers  to generThe overarching goal of consensus embedding is to optimally preserve pairwise relationships when projecting from high- to low-dimensional space. In this work, pairwise relationships were quantified by us using the popular Euclidean distance metric. This was chosen as it is well understood in the context of these methods used within our algorithm (e.g. the use of MDS). Alternative pairwise relationship measures could include the geodesic distance  or the sAM and SV co-conceived the core algorithm and theoretical justifications of consensus embedding. SV further developed, evaluated, and refined the implementation and experiments. AM directed the research and the development of the manuscript. Both authors contributed to writing and editing, and have read and approved the final manuscript.p, we utilize the traditional formulation of a prior probability,In order to calculate the probability With reference to Equation 1, "instances" are triplets. Therefore Equation 3 becomes,Independent of the above, we intuitively arrived at a mathematical formulation for embedding strength.n will depend on how well pairwise relationships are preserved from ℝN.The strength of any embedding ℝThis in turn can written in terms of the triplet relationship as well,The following proposition will demonstrate that c, d ∈ C in any embedding ℝn with respect to the true pairwise relationships in We first define the mean squared error (MSE) in the pairwise relationship between every pair of objects cd is the expectation of the squared error in the pairwise relationships in ℝn calculated over all pairs of objects c, d ∈ C. We can hence calculate the expected MSE over all K base embeddings specified above as,where EK observations K is the expectation of K observations. The MSE in Given cd is the expectation of the squared error in the pairwise relationships in c, d ∈ C. It is clear that if for all c, d ∈ C that where EProposition 2 Given K independent, strong embeddings, which are used to construct Proof.εK,X is low, K embeddings, εK,X.Proposition 2 implies that  Bagging  where coWhile embedding strength may be seen as a generalized concept for evaluating embeddings, in this work we have examined applications of DR and consensus embedding to classifying biomedical data (Section 3). We now derive a direct relationship between embedding strength and classification accuracy, presented in Theorem 1 below.c, d, e ∈ C are considered to be associated with class labels l(c), l(d), l(e) ∈ {ω1, ω2}, respectively, such that if l(c) = l(d) = ω1 and l(e) = ω2 then Λcd <Λce and Λcd <Λde. Note that ω1, ω2 are binary class labels that can be assigned to all objects c ∈ C.For the purposes of the following discussion, all objects Definition 6 An unique triplet  ∈ C with l(c), l(d), l(e) ∈ {ω1, ω2} is called a class triplet, l, if either l(c) ≠ l(d), or l(d) ≠ l(e), or l(c) ≠ l(e).class triplet of objects, two out of three objects have the same class label but the third has a different class label, e.g. if l(c) = l(d) = ω1 and l(e) = ω2. Further, in the specific case that Δ = 1 for a class triplet l, it will be denoted as Δl. For the above example of a class triplet, we know that Λcd <Λce and Λcd <Λde (see above). If Δ = 1, cd < δce δand cd < δdeδ. This implies that even after projection from ℝN to ℝn, the class-based pairwise relationships within the data are accurately preserved .Thus, in a ω1, then ω2. Based on the total number of unique triplets Z, the total number of triplets which are not class triplets is,Consider that if Y will be a constant for a given set of objects C, and is based on forming unique triplets  where l(c) = l(d) = l(e) (triplets which are not class triplets). U = (Z - Y) will correspond to the number of class triplets that may be formed for set C. If all U class triplets have Δl = 1, then it is possible to construct U classifiers which correctly classify the corresponding objects in these class triplets.Definition 7 Given U unique class triplets l ∈ C and an embedding ℝn of all objects c, d, e ∈ C, the associated classification accuracy c, d, e)l for which Δl = 1 will correspond to those objects which will be classified correctly in ℝn. Therefore, the classification accuracy Accϕ(ℝn) may simply be defined as the fraction of class triplets l ∈ C for which Δl = 1.As illustrated previously, class triplets  Accincreases monotonically as a function of ϕ(ℝn).Proof.ESψ(ℝn) increases monotonically with Accϕ(ℝn).   □As n with a high embedding strength will have a high classification accuracy. Practically, this implies that ESψ(ℝn) may be estimated via any measure of object-class discrimination such as classification accuracy or cluster-validity measures. We have exploited this relationship in our algorithmic implementation (Section 2).Thus an embedding ℝ1http://www.bic.mni.mcgill.ca/brainweb/2These datasets were downloaded from the Biomedical Kent-Ridge Repositories at http://datam.i2r.a-star.edu.sg/datasets/krbd/


Acomys cahirinus and diurnal A. russatus in field populations as well as in two field enclosures populated by both species and two field enclosures with individuals of A. russatus alone. When alone, A. russatus individuals become also nocturnally active. We asked whether nocturnally active A. russatus will respond to moon phase and whether this response will be obtained also in diurnally active individuals. We studied giving-up densities (GUDs) in artificial foraging patches and fecal cortisol metabolite levels. Both species exhibited elevated fecal cortisol metabolite levels and foraged to higher GUDs in full moon nights; thus A. russatus retains physiological response and behavioral patterns that correlate with full moon conditions, as can be expected in nocturnal rodents, in spite of its diurnal activity. The endocrinological and behavioral response of this diurnal species to moon phase reflects its evolutionary heritage.We studied stress hormones and foraging of nocturnal  Rodents are vulnerable to predation Owl strike success rate is high In laboratory biomedical experiments using rodents, light is routinely used as a cue for stress. Light pulses during the night were shown to cause an increase in GC levels in rats Although most current mammals retain the mammalian ancestral state of nocturnality, species within both closely and distantly related taxa have evolved to become diurnal  it is also active at night A. russatus lineage diverged ca. 6–8 million years ago, but it has been suggested that the shift to diurnality occurred at the evolutionary scale ca. 0.3–0.5 million years ago when it encountered the younger lineage of A. cahirinusWe studied lunar cycle patterns of stress hormone levels and foraging behavior of nocturnal and diurnally active rodents asking whether stress hormone levels correlate with moon phase, whether foraging behavior correlates with moon phase, and whether they do so both during the day and during the night. Vulpes cana, although they form only a small part of the fox's diet, Strix butleriEchis coloratus); during the day they are probably preyed upon by diurnal raptors  A. russatus, a histological mechanism for tail loss In the field, spiny mice are preyed upon at night by Blanford's foxes ; during night, it is a threats both under and between boulders and in open areas, habitats where snakes either lie still or move actively at night A. russatus than being active at night, among them differences in predation regimes A. cahirinusA. cahirinus prefer to forage in sheltered microhabitats where they are safe from avian predators In sum, being active during the day poses different challenges to A. cahirinus in the laboratory as well as in the field (negative masking), but not in A. russatusLight increases activity in diurnal mammals (positive masking) and suppresses it in nocturnal ones (negative masking), while darkness acts in the opposite way A. russatus is a nocturnal species forced into diurnality; it retains several nocturnal physiological and morphological traits, including nocturnal circadian rhythms, but it has evolved also some morphological and physiological adaptations for diurnal activity A. russatus, active in nature only during the day It appears that A. cahirinus and A. russatus during new moon and full moon nights and diurnal foraging behavior of A. russatus in the days that follow them.In order to address these questions, we carried out a field study, including a controlled and replicated removal experiment in field enclosures, enabling us to gain insight into the role of the lunar cycle on stress hormones levels and on foraging behavior see also  during dWe framed our working hypotheses as follows:A. cahirinus stress hormone levels will increase, and it will reduce foraging behavior on full moon nights.A. russatus retains its response to moon phase, then when a manipulative field experiment enables it to extend its activity into the night, it will respond to the lunar cycle in a similar manner to A. cahirinus; its stress hormone levels will increase, and it will reduce foraging behavior on full moon nights.If A. russatus stress hormone levels and diurnal foraging behavior may be affected by moon phase as well, in either an adaptive way  or a maladaptive way that reflects an evolutionary constraint (increased stress hormone levels and decreased foraging during the day in response to moon phase).If that is the case, A. russatus behavior and stress hormones levels will not be affected by moon phase.Our alternative hypothesis was that the endocrinological and behavioral response to moon phase has been lost in this diurnally active species and that A. cahirinus and A. russatus, and two populated with A. russatus only (for details see below), as well as in free ranging populations of both species (for hormone levels only).The study was conducted in four research enclosures, 2 populated with both We conducted the study at the Ein Gedi Nature Reserve, in the Judean Desert near the Dead Sea . We erected four mouse-proof enclosures (20×50 m) over linear rock terraces , marked them individually, and introduced them into the enclosures , allowing them several months to acclimate. The first experiment took place during June and July, 2000. We placed Sherman live traps in the enclosures for at least two consecutive days and nights every other week; individuals of both species can be easily and repeatedly trapped and both show similar trappability ca. 6 months prior to the experiment. We monitored the sex ratios, kept at ca. 1∶1 for each species. Foraging experiments began in June (2000) after trapping results indicated a shift in activity patterns of A. russatus held alone . Traps were set at different areas in each sampling session, and mice were marked with paint before they were released in order to avoid re-sampling. Only one fecal sample was collected from each individual. Sampling sessions lasted 24–72 h, dependent on trapping success. A. russatus were always trapped during the day, while A. cahirinus were always trapped during the night. Trapping is a stressor; in spiny mice, trapping stress is evident in the fecal stress hormone metabolite levels after 8–12 h (see We trapped free-living spiny mice using 100–200 Sherman live traps during full and new moon nights during December–June 2003–2004 (12 h see . Traps tA. russatus enclosures during days following full moon (n = 12) and new moon (n = 12) nights, using 10 Sherman live traps in each enclosure. Each individual was sampled once during each moon phase. Traps were opened at sunrise and checked 4–6 h later. Food was not supplemented at the time of the feces collection.During June and July 2003, we trapped individuals in the Hormones were extracted based on Harper and Austad A. russatus: 4 hr: t = −0.33, df = 14, p = 0.37; 8 hr: t = −1.15, df = 14, p = 0.13; 12 hr: t = −2.61, df = 11, p<0.05, A. cahirinus: 4 hr: t = −0.81, df = 14, p = 0.21; 8 hr: t = −0.33, df = 13, p = 0.51; 12 hr: t = −1.49, df = 13, p = 0.07; 2 = 0.817 for A. russatus and p = 0.0083, R2 = 0.828 for A. cahirinus; A. russatus mass effect −1430.9±291.1, R2 = 0.83, t = 4.9, df = 5, p<0.01; A. cahirinus mass effect −698.3±127.1, R2 = 0.86, t = 5.5, df = 5, p<0.01; [µg/dL±SD]; In spiny mice, unlike most rodents, the major glucocorticoid is cortisol We studied foraging behavior of spiny mice using the giving-up density method GUD, , which aOur artificial food patches comprised aluminum trays (30×20×4 cm) containing two liters of finely sifted local soil and two grams of crushed and sieved sunflower seed (1–2 mm length), mixed thoroughly and protected from foraging birds by heavy wire frames and fine filament fish netting (see ca. 12 m apart and trays in each station were placed in a triangle ca. 3 m apart from one another. We collected two types of data: a) number of trays foraged, as a measure of total activity level (since spiny mouse numbers were held constant across enclosures); b) giving-up densities, which should be independent of the number of mice that have visited the tray We placed nine trays in each enclosure, divided into 3 stations, 3 trays in each. The tray stations were placed A. russatus in absence of A. cahirinus, and of A. cahirinus on full moon and new moon nights (June and July). We also studied diurnal foraging of A. russatus during days following full and new moon nights. Four runs were conducted: two on full moon days and nights and two on new moon days and nights. Each experiment lasted a week: three days of pre-baiting and population monitoring using traps followed by four days of actual foraging experiments, when no trapping took place. We examined trays for footprints and GUDs measured at sunrise and at sunset, as in A. russatus or A. cahirinus) based on trapping results , suggesting that nighttime activity was widespread among individuals of A. russatus in the A. russatus enclosures We collected data from several trays at the same enclosures multiple times. As a result, if the same individual foraged in several trays on multiple occasions, these differences in GUD would not represent independent data points. We dealt with this issue by analyzing the trays data with the enclosures as the experimental units. We used mixed-effects modeling to analyze data because of the repeated measurements and the hierarchical nature of the sampling (trays sub-samples within each enclosure). Such models allow for the use of all data while correcting for pseudoreplication below the enclosure level e.g. . We usedA. cahirinus, we examined the effect lunar phase, and for A. russatus we examined the effect of competition (presence or absence of A. cahirinus), lunar phase, and activity phase. We then statistically compared the effect of moon phase between the species. At models 1 and 2, we added the enclosure type, enclosure index, line of trays, microhabitat, the day of measurement and the Julian month as random factors. At model 3, we added the enclosure type, enclosure index, the day of measurement and the Julian month as random factors.We analyzed (1) presence and absence of foraging on trays using generalized linear mixed-effects models (GLMM) , (2) the GUD data using linear mixed-effects models (LMM), and (3) the amount of seeds eaten at each enclosure using LMM. In each model, we separated the statistical inference for each species: for We used deviance information criterion DIC; , which cA. russatus and nocturnal A. cahirinus. In both species, fecal cortisol metabolite levels were significantly higher in full moon nights and the days following them than during new moon nights and the days following them  main effect: 0.59±0.62; Moon (new) * Competition (yes): −2.13±0.90; Moon (new) effect in the presence of interspecific competition: −1.56±0.64), while during nighttime A. cahirinus patch foraging probabilities were higher during new moon than full moon nights (Moon (new) effect: 0.87±0.29). Hence, in the two species enclosures, moon phase effect was significantly different between the species (ΔA. cahirinus – A. russatus]new moon effect [ = −2.43±0.71). Patch foraging probabilities of A. russatus were lower during nighttime compared to daytime (day part [night] effect: −3.13±0.65) in which A. russatus foraged in fewer patches during nighttime regardless of moon phase  effect:−0.13±0.05) during both parts of the dial cycle . No significant main effect of competition was found (Competition (yes) effect: 0.12±0.30). However, patches were foraged to a significantly lower GUDs during daytime than during nighttime (day part (night) effect: 0.39±0.05). No significant day-part * moon phase interaction (0.05±0.08) was found. A. cahirinus also foraged to significantly lower GUDs under new moon (Moon (new) effect: −0.30±0.05). Moreover, the effect of moon was significantly higher on A. cahirinus compared to A. russatus (ΔA. cahirinus – A. russatus]new moon effect new moon effect [ = 0.63±1.23).Moon phase also influenced the total amount of seeds eaten at each enclosure at both daytime and nighttime , Table 1Moon phase affected fecal cortisol metabolite levels and foraging behavior of populations of both species in all experimental conditions; fecal cortisol metabolite levels were elevated as were GUDs.A. cahirinus visited fewer trays than during new moon nights; the opposite pattern occurred in A. russatus, but it was significant only in the two species enclosures . Nevertheless, the total amount of seeds consumed was lower in full moon nights and the days following them for all populations, indicating a significant effect of moon phase on foraging behavior. The fact that in spite of their foraging in more patches, total seeds consumed was lower during full moon nights suggests two possible scenarios: a) that A. russatus had shorter visits to each tray, resulting in shorter foraging time per patch and therefore higher GUD values; b) similar time was spent on each patch, but more time was devoted to vigilance  individuals with high pre-foraging corticosterone levels spent less time foraging, and stayed closed to the colony than penguins with low pre-foraging corticosterone levels The behavioral response may be mediated, at least in part, by the elevated cortisol levels, reflected in our study by change in fecal cortisol metabolite levels, as has previously been demonstrated under laboratory settings. GCs were shown to have an effect on different behaviors that may result in reduced foraging. For example, in a light dark box test, GC treatment resulted in increased latency in the dark compartment, and in the elevated plus maze (elevated maze with two exposed and two sheltered arms) it increased time spent in the sheltered arms A. russatus are active in nature only during the day and have probably been so for millennia A. russatus revert to nocturnal activity, they respond to moon phase both in hormone levels and foraging behavior, suggests that this ‘hard-wired’ response is evolutionarily constrained; A. russatus individuals respond to elevated illumination although in all likelihood they have never faced risk of owl predation, suggesting that this response is genetically determined and was not lost in spite of the shift in activity patterns A. russatus are diurnally active and have even evolved some adaptations for this activity pattern  are active during both day and night; Halle A. cahirinus, respond behaviorally to elevated illumination regardless of natural moon-phase Root voles . We suggest that GC levels increase in response to previous experience – elevated risk of predation during moonlit nights, and that these elevated hormone levels induce predator-avoidance behavior in the field. Hormonal and behavioral responses to predation risk appear ‘hard wired’ in A. russatus, remaining evolutionarily constrained in spite of the shift in activity patterns.In sum, we found that both species exhibit high fecal cortisol metabolite levels and reduced foraging when the moon is full. We suggest that reduced foraging may be mediated by increased GC levels. More surprising are increased GC levels and reduced foraging in diurnally active Figure S1A. russatus and (B) A. cahirinus . * - P<0.05.The effect of trapping on mean fecal cortisol metabolite levels (µg/dL ± SE) in (A) (TIFF)Click here for additional data file.Figure S2A. russatus  and A. cahirinus . Data for fecal cortisol metabolite levels and activity levels are presented as % of the highest value obtained for each individual. Dark background represents the dark hours.Daily rhythms in mean fecal cortisol metabolites level , relative activity levels  and body temperature  of (TIFF)Click here for additional data file.Figure S3A. russatus (A) and A. cahirinus (B) extracted and fecal cortisol metabolite levels (µg/dL). Dashed line represents the regression line: A. russatus– R2 = 0.83, p<0.01; A. cahirinus – R2 = 0.86, p<0.01).The relationship between increasing pooled fecal mass (g) of (TIFF)Click here for additional data file.Text S1Validation of fecal cortisol measurements.(DOC)Click here for additional data file.


Silene ciliata and Armeria caespitosa are two representative species of cryophilic pastures above the tree line in Mediterranean high mountains. No explicit estimations of dispersal kernels have been made so far for these or other high-mountain plants. Such data could help to predict their dispersal and recruitment patterns in a context of changing environments under ongoing global warming.Plant recruitment depends among other factors on environmental conditions and their variation at different spatial scales. Characterizing dispersal in contrasting environments may thus be necessary to understand natural intraspecific variation in the processes underlying recruitment. Silene ciliata and Armeria caespitosa along an altitudinal gradient in Sierra de Guadarrama . We considered four commonly employed two-dimensional seedling dispersal kernels exponential-power, 2Dt, WALD and log-normal.We used an inverse modelling approach to analyse effective seed dispersal patterns in five populations of both S. ciliata did not exhibit significant among-population variation in mean dispersal distance, whereas significant differences in mean dispersal distance were found in A. caespitosa. Both S. ciliata and A. caespitosa exhibited among-population variation in the fecundity parameter and lacked significant variation in kernel shape.No single kernel function provided the best fit across all populations, although estimated mean dispersal distances were short (<1 m) in all cases. This study illustrates the complexity of intraspecific variation in the processes underlying recruitment, showing that effective dispersal kernels can remain relatively invariant across populations within particular species, even if there are strong variations in demographic structure and/or physical environment among populations, while the invariant dispersal assumption may not hold for other species in the same environment. Our results call for a case-by-case analysis in a wider range of plant taxa and environments to assess the prevalence and magnitude of intraspecific dispersal variation. Dispersal has important implications for plant species. It determines gene flow rates and the spatial distribution of genetic diversity Dispersal studies distinguish between primary and effective dispersal. Primary dispersal is the initial movement of seeds to the location where they are first deposited, whereas effective dispersal is the complex result of primary dispersal and the post-dispersal processes that take place after the seed is deposited on the soil surface until it is a successfully established seedling The fecundity of reproductive adults and the distribution of dispersal distances are fundamental characteristics of the dispersal process which have been extensively used as basic descriptors of this process Most seed dispersal research, employing inverse modelling or other methods, has been carried out in single populations, disregarding potentially relevant environmental variation across space that could affect the natural processes underlying the recruitment of particular species High-mountain habitats have been identified as one of the most fragile environments in the world, and global warming is thought to be especially critical for plant populations in mountain systems Silene ciliata Poiret and Armeria caespitosa (Gómez Ortega) Boiss. in DC. Previous studies carried out on these species showed that the altitudinal gradient is associated with an environmental stress gradient, with the lowest population experiencing the most stressful conditions, constraining seedling establishment and reproductive performance We used an inverse modelling approach to measure the net reproductive rate and effective dispersal kernel parameters along an altitudinal gradient in a threatened Mediterranean high-mountain pasture community in central Spain. Analyses were performed on five populations of each of two representative species of the community: All necessary permits were obtained for the described field studies. Mr. Juan Antonio Vielva from the Administration Bureau of the Natural Park of Cumbres, Circo y Lagunas de Peñalara and Mr. Antonio Sanz from the Administration Bureau of the Regional Park of Cuenca Alta del Manzanares gave their permission to work in the protected natural areas. Field studies did not involve any endangered or protected species.www.aemet.es). Dry cryophilic pastures occur in the higher summits above the tree line between 1900 and 2430 m.a.s.l and are dominated by Festuca curvifolia Lag. ex Lange and other perennial plants interspersed in a shrub matrix characterized by Cytisus oromediterraneus Rivas Mart. et al. and Juniperus communis subsp. alpina (Suter) Čelak.The study was carried out in the orophyllous cryophilic pastures of Sierra de Guadarrama, a mountain range located in central Spain. Mean annual precipitation, measured at the Navacerrada Pass , is 1330 mm with a pronounced dry season  from May to October. Mean annual temperature is 6.3°C, with mean monthly temperatures ranging from −1°C in January to 16°C in July (Silene ciliata Poiret (Caryophyllaceae) is a chamaephytic cushion perennial plant that occurs in the Mediterranean mountain ranges of southern Europe Festuca curvifolia. It blooms in late summer, with a peak in early August Silene ciliata seeds have an average mass of 0.59 mg and their diameter ranges between 1.1 and 1.5 mm S. ciliata populations, which show significant levels of inbreeding Armeria caespitosa (Gómez Ortega) Boiss. in DC. is a high-mountain dwarf chamaephytic cushion plant, endemic to the mountains of central Spain , which occurs at altitudes from 1600 to 2430 m. It grows in the same dry cryophilic pastures dominated by Festuca curvifolia, although isolated individuals have also been found on the ledges of granite and gneiss rocks. Its flowers are grouped in short-scaped flowerheads with 19±8 flowers each. Each flower has a single ovule, which yields one seed that remains enclosed in the papyraceous calyx. Average seed mass is 1.18 mg and its diameter ranges between 2.0 and 4.7 mm A. caespitosa is self-incompatible S. ciliata and A. caespitosa distributed along an altitudinal gradient at the study site  receivers  with an accuracy of 5 cm for x and y coordinates. We also measured plant diameter and the number of inflorescences of each reproductive plant. To account for potential seedling sources outside the study area, we also recorded the location, diameter and number of inflorescences of reproductive plants in a buffer zone of two meters around the plots. The seedling stage in S. ciliata and A. caespitosa includes up to one-year individuals , because the seedlings that survive a second growth period grow into the reproductive stage S. ciliata and A. caespitosaWe mapped every http://eciencia.urjc.es/handle/10115/11835).The dataset used for this study is available from the institutional repository of Rey Juan Carlos University  equals the sum of the seedling shadows across this plot of all m maternal plants, in the form:b and jr are, respectively, m-length vectors of individual plant size measurements ib and spatial distances ijr between the spatial coordinates  of plant i, and the coordinates of plot j , Q is an allometric function with parameter β yielding the number of seedlings produced by a single plant, and f is a two-dimensional isotropic seedling dispersal probability density function  as a function of dispersal distance δ and shape parameter u. The integral in eqn. 1a provides the exact probability of dispersal from plant i into the whole surface jA of plot j. All the studies we are aware of have assumed that the probability of dispersal from any given plant to any of the points lying within area jA of each plot is constant, yielding the usual simplified expression:ijr is the spatial distance between plant i and the centre of plot j. The approximation in eqn 1b will be closer to the exact values predicted by eqn. 1a the smaller the seedling plot areas and the longer the distance between maternal plants and seedling plots.We used inverse modelling to estimate seedling dispersal kernel parameters for each population of each species and to test for dispersal parameter variation among populations within species. Following Ribbens iQ = iβb, because previous studies have shown that plant size is linearly related to seed production in both species We assumed a linear relationship between seedling production and plant diameter, 2 study area into c = 100L−2 equal adjacent cells of area L2, and for each cell j, we computed observed (js) and predicted (jŝ) seedling densities. We used equations 1a and 1b to estimate jŝ, as cell areas were relatively large compared to some of the mother-cell pairwise distances , and it was not obvious a priori that the approximation in equation 1b would be sufficiently accurate. However, virtually identical dispersal and fecundity estimates were obtained for all populations and assumed kernels using each equation (results not shown). Thus, we only present the results for the latter, whose computational efficiency enabled us to calculate confidence intervals and conduct hypothesis tests (see below) in a reasonable number of CPU hours. We also tested the effect of three different values for cell side length, L = 0.125, 0.25 and 0.50 m, on parameter estimates, with lower and upper values approximating DGNSS accuracy and dispersal range, respectively. Distribution of seedling densities across cells exhibited overdispersion relative to a Poisson distribution (p<0.05) in all populations except the Nevero population of S. ciliata, and we thus modelled seedling counts using a negative binomial distribution S is the set of c observed seedling counts js, Γ is the gamma function, and θ is the negative binomial parameter, determining the distribution variance . Maximum-likelihood estimates for β, δ, u and θ were obtained for each population of each species by maximizing For each population of each species, we divided the 100-ma and shape parameter u: the 2Dt kernel As most studies show that no single dispersal function provides consistently superior fits across species δ2Dt = aΓ(3/2) Γ(u−3/2)/Γ(u−1), LNδ = exp (a + 0.5u2), δWALD = a and δEXPOW = aΓ(3/u)/Γ(2/u), respectively. Given that δ provides a more intuitive description of dispersal range than a, independent of the assumed kernel, we parameterized the model (eqns 1 and 2) in terms of δ and u, rather than in terms of a and u, facilitating the estimation of profile-likelihood confidence intervals for δet al.δ is constant across populations within species, we obtained parameter estimates based on the likelihood of the whole species data set qS, incorporating information from all q populations of the species:qβ, qu, and qθ are q-length vectors of population-specific fecundity parameters kβ, kernel shape parameters ku and negative binomial parameters kθ. This model has 3q degrees of freedom and provides an average species-level estimate of δ. Note that, unlike Clark et al.δ) vary among populations within species , based on the likelihoodi, while the remaining q−1 populations have a constant mean dispersal distance δi−. We applied the sequential Bonferroni correction to correct for multiple testing We tested for potential differences in dispersal and fecundity parameters across populations using likelihood ratio statistics as in Clark S. ciliata populations did not converge when 0.25 or 0.5 m cell side lengths were used  as leptokurtic functions with either exponential (WALD) or fat  tails within the same populations. Using the WALD kernel across all S. ciliata populations, species-level estimates of mean dispersal distance and the shape parameters were δ = 0.39 m (95% CI: 0.35–0.45 m) and u = 0.80 (0.63–1.13). For A. caespitosa, the log-normal kernel ranked among the best models in all four populations where estimation converged, whereas the WALD, 2Dt and exponential-power models showed a similarly good performance in three populations . Using the log-normal model across all A. caespitosa populations, the species-level estimates of kernel parameters were δ = 0.43 m (0.32–0.60 m) and u = 0.98 (0.87–1.14).Differences in AIC values indicated that no single kernel function provided the best fit across all populations of either of the two species  and 3. Fulations . SupportA. caespitosa populations (r2 = 0.17–0.35) to fair or poor (r2 = 0.01–0.12) in S. ciliata populations (p<0.0001) in all cases except in the population with the lowest seedling number . Population estimates of mean dispersal distance (δ) and fecundity (β) parameters were generally consistent when assuming different kernel families .The fits of the most parsimonious models ranged from good in ulations  and 3 anfamilies  and 3. NS. ciliata, estimated mean dispersal distances ranged between δ = 0.23 and 0.71 m . Estimated shape parameters ranged between u = 0.55 and 2.26 and were not significantly different from each other . Fecundity parameters ranged between β = 0.001 and 0.009 seedlings/cm across S. ciliata populations and were significantly different . Per-population tests indicated that all population estimates of β were significantly different from the species average . Per-population tests showed that only the population with the largest δ (Loma de Cabezas with 0.78 m) differed significantly from the species average . Fecundity parameter estimates exhibited significant differences in A. caespiotsa , ranging from β = 0.011 to 0.024 seedlings/cm. Per-population tests indicated that maximum estimates of β (at Cabezas de Hierro) and minimum estimates of β (at Collado de las Vacas) were significantly higher and lower than the species average, respectively  and the essentially barochorous seed dispersal mechanism, the ratio of seeds from other adults to seeds from the closest adult is likely to be very small, and, therefore, the effect of this bias negligible. Moreover, all the studied S. ciliata populations exhibited significant positive fine-scale spatial genetic structure (FSGS) at the 0–1 m distance class , indicating that the spatial aggregation of genotypes is consistent with the low effective dispersal distance observed in our study.Most effectively dispersed seeds were established near conspecific adult plants . ConsequS. ciliata. Although the behavior near the origin and the tail is not independent in the assumed phenomenological kernels , which are essentially the same in all populations, were the most important factors in determining the effective dispersal kernel in our study sites during the study period. Post-dispersal processes controlling seedling emergence and establishment seem to operate similarly across populations, regardless of evident variation in local demographic and environmental conditions . This provides new insight into the prevalence and magnitude of intraspecific dispersal variation. Although our results are consistent with previous studies The few studies that have previously used inverse modelling with a multi-population approach found local variation in dispersal parameter estimates within species τ = 1, p<0.001 for S. ciliata; and τ = 0.67, p = 0.167 for A. caespitosa), which represents a measure of the average number of successfully established seedlings produced by each mother. By contrast, the fecundity parameter was not associated with mean seed production per adult plant . Furthermore, seedling density was positively related to altitude . These results suggest different seedling mortality rates at different altitudes, which have probably influenced the observed differences in the fecundity parameter among populations. These results are also congruent with previous studies on the demography of the two species S. ciliata and A. caespitosa through variation in effective seedling establishment probabilities, but not as much through variation in the effective seed dispersal range. The explicit incorporation of finer-scale environmental factors and landscape features (e.g. shrub cover) in our models  and seedlings (crosses) in each study plot of (PDF)Click here for additional data file.Table S1Silene ciliata (cell size length 0.25 and 0.50 m). δ, mean dispersal distance (m); u shape parameter; β, fecundity parameter (seedlings/cm); θ, negative binomial parameter; – logL, log-likelihood; nc denotes models that did not converge.Estimated parameters for the models fitted to the seedling recruitment data of (PDF)Click here for additional data file.Table S2Armeria caespitosa. (cell size length 0.25 and 0.50 m). δ, mean dispersal distance (m); ; u shape parameter; β, fecundity parameter (seedlings/cm); θ, negative binomial parameter; – logL, log-likelihood; nc denotes models that did not converge.Estimated parameters for the four models fitted to the seedling recruitment data of (PDF)Click here for additional data file.