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Citations since 2017
17 Research Items
I joined Dr. Hadas Hawlena´s group on December 2014 as a Postdoctoral researcher at Ben Gurion university of the Negev. Here, I am currently developing several projects focus on the ecology and evolution of host-parasite interactions in natural communities using the wild multihost rodent-flea-Mycoplasma system as a natural model. In my previous years, during my Phd I assesed the conservation status of different insular populations of the Balearic lizard, according to four indicators of proven reliability (mainly parasitic infestation dynamics) according to ecological and evolutionary history of populations.
Spotted flounder (Citharus linguatula L.) caught in the Gulf of Cadiz (area FAO 27 ICES IXa) were examined for Anisakis larvae and to assess the possible risk of anisakiasis in humans through consumption of this fish. Larvae of the genera Anisakis and Hysterothylacium were identified in the analysis of 128 purchased fish specimens. All Anisakis lar...
Coexistence theories develop rapidly at the ecology forefront suffering from interdisciplinary gaps and a lack of universality. The modern coexistence theory (MCT) was developed to address these deficiencies by formulating the universal conditions for coexistence. However, despite this theory's mechanistic foundation, initially, it has only rarely...
En las últimas décadas, los cambios de origen antrópico en los ecosistemas han alterado la incidencia de las enfermedades infecciosas emergentes. Entre otras causas, el calentamiento global está favoreciendo la expansión y la redistribución de ciertos vectores de patógenos de relevancia en salud pública y animal. Este es el caso de los mosquitos, e...
Aim To advance our understanding of the mechanisms that mediate the relationships between global climatic and anthropogenic processes and pathogen occurrence, it is crucial to evaluate the exact pathways connecting the ecological mediators and the pathogen responses across spatial and temporal heterogeneities at various scales. We investigated the...
Structural equation modeling (SEM) can illuminate complex interaction networks of the sort found in ecology. However, selecting optimally complex, data‐supported SEM models and quantifying their uncertainty are difficult processes. To this end, we recommend a formal model selection approach (MSA) that uses information criteria. Using a suite of num...
Disentangling the mechanisms that mediate the relationships between species diversity and disease risk has both theoretical and applied implications. We employed a model system of rodents and their Mycoplasma pathogens, in which an extreme negative diversity-disease relationship was demonstrated, to test the assumptions underlying three mechanisms...
When selecting optimal habitats, animals should rely on detecting environmental cues that indicate the suitability of a given site. In fossorial animals, restrictions of the underground environment might limit the opportunities for habitat selection. However, field observations of some fossorial amphisbaenian reptiles indicate that microhabitat occ...
The study of the endocrine status can be useful to understand wildlife responses to the changing environment. Here, we validated an enzyme immunoassay (EIA) to non-invasively monitor adrenocortical activity by measuring fecal corticosterone metabolites (FCM) in three sympatric gerbil species (Gerbillus andersoni, G. gerbillus and G. pyramidum) from...
Microbes inhabiting multi-cellular organisms have complex, often subtle effects on their hosts. Gerbillus andersoni allenbyi are commonly infected with the Mycoplasma haemomuris-like bacteria, which may cause mild nutrient (choline, arginine) deficiencies. However, are there more serious ecological consequences of infection such as effects on forag...
Interactions between coinfecting parasites may take various forms, either direct or indirect, facilitative or competitive, and may be mediated by either bottom-up or top-down mechanisms. Although each form of interaction leads to different evolutionary and ecological outcomes, it is challenging to tease them apart throughout the infection period. T...
Based on molecular data, previous studies have suggested a high overall diversity and co‐infection rates of Bartonella bacteria in wild rodents and their fleas. However, partial genetic characterization of uncultured co‐infecting bacteria, limited sound conclusions concerning intra‐ and inter‐specific diversity of the circulating Bartonella. To ove...
The way that some parasites and pathogens persist in the hostile environment of their host for long periods remains to be resolved. Here longitudinal field surveys were combined with laboratory experiments to investigate the routes of transmission and infection dynamics of such a pathogen—a wild rodent haemotropic bacterium, specifically a Mycoplas...
Traditionally, the study of host-parasite interactions has been approached from its simplest organizational level composed of just one host and one parasite species. However, those simple systems are exceptional cases in nature whereas interactions between multiple host and parasite species are the common rule, creating, thus, a complex network sys...
Bacteriocins - extracellular toxins produced by almost all bacteria - are the most abundant and diverse group of bacterial defence systems. Bacteriocins differ from low molecular antibiotics, as they have a relatively narrow killing spectrum, which is restricted to closely related species. The current view is that due to their unique activity spect...
Emerging infectious diseases have increased dramatically over the past three decades, becoming a major health and economical problem. The common wisdom is that changes in ecological conditions are responsible for this challenge. While many studies have focused on small-scale phenomena (i.e., effect of host traits on parasite community composition)...
Elucidating the factors determining reproductive success has challenged scientists since Darwin, but the exact pathways that shape the evolution of life history traits by connecting extrinsic (e.g., landscape structure) and intrinsic (e.g., female’s age and endosymbionts) factors and reproductive success have rarely been studied. Here we collected...
Blood parasites such as haemogregarines and haemosporidians have been identified in almost all groups of vertebrates. However, very little is known about biodiversity of these parasites and their effects on some major groups of reptiles such as amphisbaenians, a distinctive group with many morphological and ecological adaptations to fossorial life....
Parasitism is an ubiquitous phenomenon of evolutionary and ecological importance for all living organisms, including humans. Traditionally, the study of host-parasites interactions has been approached from its simplest levels of organization. Within populations, almost all experimental studies explored the effects of parasites and hosts responses i...
Parasitism is a ubiquitous phenomenon of evolutionary and ecological importance for all living organisms, including humans. Traditionally, the study of host-parasites interactions has been approached from its simplest organizational level composed of just one host and one parasite species. However, those simple systems are exceptional cases in natu...
Background: The study of changes in a host’s energy allocation in response to parasites is crucial for understanding parasite impact on both individual- and population-level processes. Experimental studies have explored such responses mainly in a single subsample of hosts per study, primarily adult males, and have only assessed either the overall e...
The study of host changes in energy allocation in response to parasites is crucial for the understanding of parasite impact on both individual and population-level processes. However, at present, experimental studies have explored such responses mainly in a single host type per study, and assesses the overall energy acquisition, energy expenditure,...
Immunological studies are often context-independent. But eco-immunology is emerging as an alternative, focusing on the natural variation of immune functions of free-living organisms in relation to their ecological constraints and evolutionary context. Immunological studies also tend to study only parasite resistance, that is, mechanisms by which ho...
Many wild populations of lizards in the Mediterranean Basin inhabit small islands frequently visited by humans. Reptiles respond to humans as potential predators by escaping to refuges or by increasing antipredator behaviours which lead to a loss of body condition and may have important consequences for fitness. We assessed effects of human pressur...
In animals, developmental stability is frequently assessed by the level of fluctuating asymmetry. Several environmental and genetic stress factors can increase the developmental instability in a population. Anyhow, the use of fluctuating asymmetry as a measurement of developmental instability and its relationship to other measurements of genetic an...
Economic escape models predict escape decisions of prey which are approached by predators. Flight initiation distance (FID, predator–prey distance when prey begins to flee) and distance fled (DF) are major variables used to characterize escape responses. In optimal escape theory, FID increases as cost of not fleeing also increases. Moreover, FID de...
Lizards and gulls cohabit in several Mediterranean islands. The yellow-legged gull, Larus michahellis, was found to prey several vertebrate species. However, precise information about the interaction between gulls and other vertebrates, particularly with lizards is still scarce. The Balearic lizard, Podarcis lilfordi, shares several coastal islets...
Parasites are able to negatively affect the locomotor performance of their hosts, and consequently, their biological fitness. In this study, we examine the relationship between parasitism and burst speed in an insular population of Lilford's Wall Lizard (Podarcis lilfordi (Gunther, 1874)). Podarcis lilfordi is normally infected with haemogregarine...
During spring and summer, we studied the thermal ecology of two populations of the Balearic lizard, Podarcis lilfordi, from two coastal islets of Menorca (Balearic Islands, Spain): Aire and Colom. We calculated the accuracy of thermoregulation, that is, the extent to which body temperatures are close to species' thermal optima, the thermal quality...
The spatial distribution of a population of lizards is influenced by the distribution of resources and the individuals' skills to gain access to them. On several Mediterranean islands with food scarcity and unpredictable resource availability, some resources can be extremely important during some periods of the year, as, e.g., the dead horse arum,...
While the use of faecal pellets is widely accepted as a primary methodological source of data for dietary studies, a recent paper advocated for the use of gut contents. This was due to the fact that faecal samples would give biased results of the diet of arthropod predators, due to a lower representation of soft-bodied prey in faecal pellets. To te...
Coastal islets of Balearic Islands share several ecological conditions, such as the scarcity of food resources and the lack of a strong predation pressure. During spring and summer 2006, we studied the thermal ecology of two populations of the Balearic lizard, Podarcis lilfordi, from two different coastal islets of Menorca (Balearic Islands, Spain)...
Blood parasites can have negative effects on lizard hosts, such as testicular reduction, low levels of haemoglobine, an increase of immature erythrocytes, low levels of oxygen consumption and a significant reduction of sprint speed. Thus, blood parasites are able to produce a measurable decrease in individual fitness. Factors as host density, abund...
I am not commonly use boxplot to draw data, neither to inference differences between groups so I am kind of confused.
Recently I read that "If the median line of a box plot lies outside of the box of another, there is likely to be a difference between the two groups", is this true? Can someone recommend me a citation confirming it?
anybody knows if there is any package to perform an straightforward goodness-of-fit test for an ANOVA in R (Chisq test, preferably)?
My anove is in the lm form:
lm(DV ~ scale_1 + scale_1, data = Data)
Thanks in advance
Dear colleagues, I'm working with Path analyses in lavaan and MVN packages. There are some results that for me are confusing and features from the MVN that I do not know how to set.
My dataset is composed of 130 rows and 7 variables. Using the MVN package to run Mardia, hz and Royston, results indicate that my data is multivariate normal. However, the Mahalanobis distance at different alpha levels give me different results: alpha 0.5 = 14 alpha 0.6 = 8 alpha 0.65 = 5 alpha 0.7 = 0
I have different questions: 1. what is the meaning of 'candidate' outliers? if MVN and Q-Q plot indicates good fit to MVN, should I not consider the outliers? based on what?
2. if outliers are the main concern to get multivariate normality, how can it be that I get multivariate normality by 3 different methods while I have 0-14 candidate outliers?
3. Most important, where can I read which alpha and tolerance values I have to use in my case? Is there any way I can see any tutorial, or recommendations of the alpha according to sample size and other data characteristics?
I was looking on the web and I have found no answer to this. So, any literature recommendation or advice will be welcome.
Thanks and sorry for taking your time. Sincerely,
Dear Dr.colleagues, I am currently plotting the dynamics of an infection in Excel. For each sampling day, I plotted the mean infection load of the population, using the standard error for the error bars. When representing the values within their natural scale, error bars are symmetric. However, if I use the logarithmic scale offered by in Excel, the error bars (the SEs) become asymmetric.
Looking for a solution, I have found the attached tutorial, but there are some confusing aspects for me that I would like to understand in depth. If I understood correctly, it first use the absolute error and not the SE (right?). Then, transform the absolute to relative error using the 0.434x(δy/y), where δy is the absolute error (still right?) and plot the value of log(y) with the error bars 0.434x(δy/y)
My questions/doubts are the following:
- Is it correct to show a log-scaled plot with asymmetric error bars using the SEs? I guess no, but to confirm. Even further, is the relative error the only correct way to use error bars when using log scale?
- Following your tutorial, can I use the same steps to transform the SE to some kind of "relative SE" and use it as error bars?
- Lastly, is there any way to transform SE to absolute errors or I must do it with the computed average?
It would be very useful if you can recommend me any lecture or, better, a tutorial at this respect. Thanks in advance,
I am tryng to understand what the option "planned comparisons of LS means" in Statistica is doing?
Let's say I have a factorial ANOVA with two categorical explanatory variables: sex (2 levels) and treatment (4 levels). So sex*tretment includes 8 subgroups.
"Traditional PostHoc" do all possible paired comparisons between the 8 groups, and includes a correction taking into account that there are 8 groups.
BUT with "planned comparisons of LS means" I can ask to do a specific comparison between, for examples, males and females with treatment 1. How is doing it? I suspect that is not comparing among all categories and show only the output for males and females with treatment 1, right?
If I extract the data for males and females with treatment 1 and do a simple ANOVA between this two group, result is the same?
And, overall, what is the correspondence in R?
Thanks, I am an R user and I cannot find how statistica work regarding this issue
Im running a model selection analyses with several linear models
I want model.sel or mscAIC output to keep the order of my models in the output, and not to rearrange then according to any IT index.
I explain, I have saved models, from m1 to m7 and I want to keep this order in the output.
When I run:
ModSel_mods<-model.sel(m1,m2,m3,m4,m5,m6,m7) I get
(Int) MIN.CI MAX.CI MEA.CI MIN.SAV MAX.SAV MEA.SAV df logLik AICc delta weight m2 -5.6940 10.78 3 16.888 -26.1 0.00 0.698 m4 -3.7000 6.813 3 16.018 -24.3 1.74 0.292 m3 -2.1550 3.828 3 12.608 -17.5 8.56 0.010 m5 0.9983 -4.079 3 8.894 -10.1 15.99 0.000 m1 0.2213 2 7.055 -9.3 16.75 0.000 m7 0.8751 -3.21 3 7.882 -8.0 18.01 0.000 m6 0.5288 -1.41 3 7.279 -6.8 19.22 0.000
And I want to keep the order according to first column:
(Int) MIN.CI MAX.CI MEA.CI MIN.SAV MAX.SAV MEA.SAV df logLik AICc delta weight m1 0.2213 2 7.055 -9.3 16.75 0.000
m2 -5.6940 10.78 3 16.888 -26.1 0.00 0.698 m3 -2.1550 3.828 3 12.608 -17.5 8.56 0.010
m4 -3.7000 6.813 3 16.018 -24.3 1.74 0.292 m5 0.9983 -4.079 3 8.894 -10.1 15.99 0.000 m6 0.5288 -1.41 3 7.279 -6.8 19.22 0.000 m7 0.8751 -3.21 3 7.882 -8.0 18.01 0.000
But I want to keep order according to 1st column, or even better, in case models have specific names, to keep the order I created in: ModSel_mods<-model.sel(m1,m2,m3,m4,m5,m6,m7)
Thanks in advance. it should be something easy, but I am stucked with that. Best regards,
I have a bulk of fitted univariate regressions using lm, for example:
What is the best R package to compute effect size in univariate (simple) linear regression?
model1<-lm(DV ~ IV1, data = data)
model2<-lm(DV ~ IV2, data = data)
model3<-lm(DV ~ IV3, data = data)
model4<-lm(DV ~ IV4, data = data)
I want to compute partial r for all the IV in the most straight forward way and in a run, if possible. I mean, creating a loop or similar to compute all partial r all at one
Which package and function would you recommend? Thanks
Im currently exploring on the use of AIC and other I-T indexes criteria for backward, forward and stepwise regression.
Usually, when applying IT indexes for Multimodal Inference, we choose a set of 'good models' depending on different criteria, but mainly, all models with delta AIC<2, and then we average the estimates between the set of models or make conclusions based on the set of models, no need to average.
However, if Im not wrong, the goal of backward etc is to get to one 'best' final model. I understand the use of AIC in this framework but, is there any criteria to select the best model in this case? Do I simply have to choose the model with lowest AIC no matter whether there is another model whose delta is less than 2?
For example, if my 'maximal' or saturated model has the lowest AIC and the model dropping one variable has a delta of 0.5, which model to choose?
I was looking in the web and I have found no answer to this. So, any literature recommendatin or advice will be welcome.
Im using the MuMIn package in R, by defaults, the output order models by AIC (or similar values), from lowest ot highest.
(msAICc <- model.sel(model1, model2, model3))
How can I manipulate the code to order the models by its name or the entry in the code?
For example, I want the output to be
I have doubts about the of interpretating a lower order interaction terms in the presence of a significant higher order interaction effect. Also, if it depends on whether the latter is significant or not
It is assumed that, due to the marginality principle, a main effect cannot be straightforward interpreted when an interaction in which is involved is present in the model.
- is the same applicable with lower order interactions when higher order interaction is present?
- Furthermore, is the interpretation of the lower order dependent on whether the higher order interaction is significant or not?
- I take advantage to ask the same regarding main effects, can I interpret their results if the interaction where is involved is not significant or I have to repeat the model taking out the interaction?
Thanks, it would be great if you recommend me some references at this respect. Not very mathematical, please!!
I have a feeling that when a model, let's called it 'saturated', fits to a gaussian distribution (the residuals of the model follow a normal distribution), all the nested models derived from it also follows a normal distribution, is that right? is there anything published at this respect?
Thanks, I would be happy to have (or not) a confirmation
Im running a Path analysis (SEM without latent) with one binary independent variable, several continuous mediators, one categorical mediator and a categorical dependent variables with two levels (infected/not) in MPlus using MLR estimator. The fit statistics of my model are good.
I want to extract the predicted values from the fitted model in a binary outcome (as 0 or 1). MPlus support gave me the option to extract the Propensity scores and to consider 0 if propensity is <0.5 and 1 if is >0.5. However, all propensity scores in my model are 0.527 or 0.526. According to that, it means that the fitted model predits that all individuals must be infected.
My questions are:
1. is applicable the rule that when propensity is <0.5 can be converrted as 0 and 1 if is >0.5? Based on what? I would like some literature at this respect
2. Is there any rule of thumb to interpret propensity values? I mean, if is 0.2-0.5 it means something and if is 0.5-0.7 means other thing
3. is logic to have so close propensity scores as I have in my model?
4. is there a way to extract the predicted values in a binary (0/1) outcome?
Thanks, Im a little confused with this
Parasitism is an ubiquitous phenomenon of evolutionary and ecological importance for all living organisms, including humans. Traditionally, the study of host-parasites interactions has been approached from its simplest levels of organization. Within populations, almost all experimental studies explored the effects of parasites and hosts responses in single host types, mainly non-reproductive adult males. At community level, the vast majority of studies focused on single parasite-single host species interactions. However, such simplicity is exceptional in nature, where interactions between multiple parasite and host types/species are the common rule. Thus, one of the current challenges in examining host-parasite interactions is to integrate the natural heterogeneity at both experimental and theoretical levels. To help to fill these gap, we are exploring the role that heterogeneity plays in shaping host-parasite interactions, at both population and community levels and under environmental heterogeneity. Rodent–flea–bacteria communities in the western Negev dunes in Israel offer ideal, natural experimental conditions for exploring the association among host-species- diversity and local adaptation of microbes to their hosts. Its sand dune sytem is a unique system that balance between realism and simplicity. The region includes heterogeneous dune areas composed by either shifting sand (semi-stabilized sand) or stabilized sand hosting rodent communities of different species composition in close proximity to one another. The 3 gerbillus species (Gerbillus andersoni, G. pyramidum and G gerbillus) belong to the same taxonomic and functional group of hosts and are infested by the same flea species (Synosternus cleopatrae), which are likely to transmit Mycoplasma haemomuris and Bartonella sp. the two dominant flea-borne bacteria in these communities.