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Habitat Selection - Science topic

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I am investigating how my study species interacts with its habitat and prey in-situ to gain an understanding of its habitat preference. This will be used to generate a model to help predict species presence across a latitudinal scale.
I have some repeated sightings of animals throughout the course of my study, however, there are some that have only been seen once. I want to compare the mean percentage of different benthic substrates found with each sighting. I am unsure if I need to group repeated sightings of the same animal for my analysis, and if I do, how do I compare that to animals that have no repeat sightings.
I have read papers that have counted repeated individual sightings over temporal scales as independent data points, but I am unsure if this is the correct practice.
Thank you for any help in advance.
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You can use 'repeated measures ANOVA', which compares means across one or more variables that are based on repeated observations. Scores for the same individual are dependent, whereas the scores for different individuals are independent. Or just state the assumption that the probability of a repeated measure is equal to an individual sighting, then go ahead with standard analysis
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I am working on ecological factors that determine the size of territory in electric fishes, and I would like to know if there is any background on the relationship between concentration of oxygen and territory in freshwater fishes
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Hi Daniel,
This attached article by Julian et al. shows the oxygen consumption rate of some electric fish species. I hope it helps.
Best regards,
Nonato Mendes Jr.
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I'm trying to predict shorebird distributions using habitat variables, using Maxent to model. I have 4 categorical data (wetlands, waterbodies, tilled, beach) and 1 continuous data layer (topographic roughness).
My categorical data layers are binary (1 = habitat is present, 0 = habitat not present). This is because 3 of these layers were extracted from a larger dataset called SOLRIS and I didn't want everything in the dataset, which had 30 different habitat classes. However after extracting the layers I wanted, I ended up having many zeros in these layers because only few cells were classified as beach habitat, for example. All my layers can be seen below, where D1-D4 consist of the binary categorical layers. Cells that are zero appear to be blank, where there is no colour on the maps. However topographic roughness can be seen covered across the entire extents of the study area.
Additionally, Maxent produces variable percent contributions, that show how topo roughness contributed to majority of the model.
As a result, the model prioritized the topographic roughness layer because each cell in this layer had a value indicating how rough the terrain was, as opposed to the rest of the habitat categorical layers which had a few patches of the defined habitat, and when the habitat was not present, the cell was coded as zero.
How do I make sure Maxent runs on the categorical habitat variables across the entire study area?
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Hi,
I would suggest the following:
1) Combine your landcover data. Ideally, you have one categorial data set that has your prefered (=biological meaningful for your targeted species) landcover classes. As I read your data originates from the same landcover, so this should not be a problem. But avoid NoData in your data. If there are a lot of cells that you dont have data for, then the MaxEnt algorythm also cannot calculate occurence probability for those cells.
A solution might be for example to include a additional landcover classes which fills those NoData cells, but be very cautious with that to not alter your landscape.
Also dont forget variables with potential negative effect on species, like maybe human infrastructure.
2) After running some models with a sinle landcover data set, you should get knowledge about which landcover category is prefered and which one is avoided by your species. Then you can go on and included additional "distance to good landcover" and "distance to bad landcover" variables. Those are continuous data spanning your whole study area that represents the positive and negative effects of landcover on its suroundings.
3) I am no expert on shorebirds, but in general I start the modeling process by including topographical variables, landcover variables and climatic variables. You can try to include meaningful climatic variables; MaxEnt will decide itself if those are explaining or not. Dont forget to exclude highly correlated variables though.
4) Dont forget to check "categorial" before running the model. This is trivial, but happens quite often.
Cheers, Florian
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I need to calculate the Global and Local Moran indices in R using a variable distance as threshold: I have a SpatialPointsDataFrame with almost 300 points and I want to calculate the Global Moran index using 4 different distances (e.g 5 km - 10 km - 50 km - 100 km).
and I know the 'ape' and 'spdep' packages but it seems that no adjustment can be done concerning the spatial width to be considered...
Thanks
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For anyone working on this and similar problems, I have found the spdep R package very helpful.
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I need to study habitat selection, feeding and roosting behaviour of ibises. Can any one suggest best method appropriate to collect data on these issues?
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Hello Everyone! I have recently used the adehabitatHS package in R with Design III (widesIII function) data to estimate overall manly selectivity measures (wi) for two study populations. I am interested in habitat selection and the lands have been categorized into 4 land-use types (pine, hardwood, pasture, ruderal). Approximately half of our GPS collared animals are on a high-fenced preserve and the other half are on state-managed lands located adjacently. I have been having trouble finding a significance test that would allow me to compare population habitat selectivity measures, at each habitat type, between our wild and farmed study populations. Is anybody aware of any significance tests that could be applied in this situation? Any assistance would be much appreciated!
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Hi Jeremy Orange. There is a simple and elegant example of how can you perform these comparisons between habitat availability and use in these articles:
Neu, C. W., Byers, C. R. and J. M. Peek. 1974. A technique for analysis of utilization-availability data. Journal of Wildlife Management 38:541-545.
Byers, C. R., Steinhorst, R. K. and P. R. Krausman. 1984. Clarification of a technique for analysis of utilization-availability data. Journal of Wildlife Management 48:1050-1053.
With GPS track data and quantification of habitat availability this is easy to do.
All the best,
Alexander
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Dear People,
I have data, recorded by a set of 12 cameras in a 5x5km grid, with 1 camera position in each cell for every month (July-December, 4 positions in total/per grid cell). These positions are spatially dependent because of the small grid size and greater homerange size of the target species: Ursus arctos.
My question is, if I can conduct a single season occupancy model for that kind of data, although a requirement seems to be spatially independent sample sites,
or if I should use spatial capture-recapture models (SCR) to analyse habitat selection and spatial behaviour instead?
Another question is: how many trap days should be considered as one repeated visit (occasion- K) for the occupancy model? (3-7?)
Last but not least: does anybody know, where to get daily temperature and precipitation data for Greece :)?
Thank you so much
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I like this discussion
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Hi,
As far as I know, habitat selection (RSF) is mainly applied by collecting habitat/environmental component susing SINGLE point. For example determining used, unused and available habitat for each SINGLE point. However, I am interested to know whether a grouping behavior (fission) select particular land cover and its environmental variables. So at least I have to define two closed individuals which mean TWO points. In this case, how I should define used habitat for those two individuals?
any help will be appreciated. Thanks!
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Hi Muhammad yes in that case each individual would be a separate point or buffer. Maybe the best way of incorporating the fission fusion part is to have a 1 fission or 0 fusion variable for each observation. This can then be entered into the model as an interaction to understand if or how each habitat variable is related to grouping behavior, this may be easier than separating it into two analyses (Fis and Fus) until you find you need to. The random available points should be sampled from the surveyed study area, i.e. only where you have sampled. Unsure how you are collecting the data (GPS collar?) but you may also want to put individual ID in as a random effect. You should use a mixed effects model with a logit link function (logistic regression). There is a lot I don't know about your study and a lot to consider with RSFs to get them correct, hope this helps you make some progress. I have found some of the most helpful RSF papers are by Mark Boyce, Chris Johnson and Scott neilson, and their colleges.
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Dear colleagues, I plan to measure fish abundance in rivers to assess the influence on habitat selection of diving birds. Use of biosonics dt-x sounds good, but it's too expensive. Would you kindly advise me? Thanks a lot.
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The most common methods are angling and electrofishing.
Recent efforts to sample fish assemblages in deeper rivers by using multimesh gillnets developed for lotic environments (Fjälling et al. 2015), i.e. the Norden multimesh Stream Survey Net (NSSN).
Fish abundance or density is ideally expressed as numbers or biomass per area or volume of habitat sampled.
First option is to determine the absolute abundance. However, it is costly and time-consuming. It requires extensive data collection, such as a precise estimate of density at sampling sites and a probability-based array of those sites.
For many populations, indices of relative abundance are sufficient for assessment. I think that this is the best choice for you. Catch per unit effort (C/f or CPUE) is a relative abundance index, which is often directly related, though not always in a linear fashion, to absolute abundance. CPUE is more specifically expressed as numbers or biomass per unit effort (NPUE or BPUE).
You can find detailed description here:
Precise relative abundance estimates should be collected from several areas (randomly distributed samples) in order to be representative of an entire system. In addition, the distribution of some species may need to be understood before undertaking relative abundance estimation, particularly for species that are rare or poorly sampled.
Abundance can be expressed separately for each species in the catch or summed over all species to represent the total fish community. Alternative abundance metrics may be calculated for groups of individuals according to e.g. size, age or functional traits as mentioned above.
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As this looks like a compendum of exciting research emerging at LILA, I wonder if there are any reports or pubs on the work done on fish movements, habitat selection and seasonal hydrology here in 2011- ?
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Most fish live in local places during the dry season. During the wet season, for example cat fish migrates up river to desovates up there while for example trout remins in local places by avoiding the force of the river discharge living in steady ponds usually found besides the main river channel. In this way trout does not spend too much energy dealing with the force of the river discharge during the wet season. There are of course very many differences of the local fish species and the magrating fishes in different places.
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Hi,
I have a set of GPS fixes from GPS tagged owls. The GPS was programmed to send 3 GPS positions (fixes) during the night (2 hours before midnight, midnight and 2 hours after midnight). The GPS senders were active between 1- 3 years. I will calculate Kernel and minimum convex polygon (MCP) home ranges, and finally I will analyse habitat selection.
Within each home range I will create random points reflecting habitat availability. The habitat will be compared between owl fixes and random points to estimate habitat selection.
So far it is straightforward. Although the owl lives along the coast and alternates between being on mainland and group of islands. Hence, within the home range area there are large water bodies, which the owl are only crossing.
I think the best procedure for the habitat analysis is exclude the large water bodies. Although, I would be happy to get some recommendations.
Best regards,
Ronny
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I agree with you Ronny. We have to accept some dependence (literal meaning) between animal location at the time of a previous fix, where it is now, and where it can potentially go before the next fix. But I understand mathematical people focus on something different, statistical independence of points as a basic assumption of kernel density estimation. Nevertheless, in many previous animal tracking studies it was acceptable to use kernel methods with long intervals between fixes (reducing the issue of auto-correlation), and could okay for your fixes 3 times per night with 2 h intervals. Newer methods like aKDE from Fleming et al and Brownian Bridge movement models I think are more important for people who get high frequency fixes. Might be interesting to try both with your owl data. It sounds a very interesting study, best wishes for your analysis.
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I'm about to analyse habitat selection in a owl inhabiting coastal and maritime heath habitats. I have GPS-positions and want to check if the owl are more likely to stay at elevations in the terrain. I have digital elevation models (DEM) for the area and want to calculate habitat openness. I tried Package 'horizon' - cran.r, but got some strange results. Anyone that could help calculating habitat openness from a digital elevation models (DEM)?
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Via the processing toolbox you can find a tool called Sky view factor. It is based on this work
and calculates, among others, the visible sky as a percentage of the unobstructed hemisphere above a certain location.
I tried it with your data and it could work for your owl habitat mapping:
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I am working on integrated fish farming system. The size of pond is about 0.1 hectare. I need to study the growth of fish at different interval. The size of fish stocked are not uniform. Please suggest me how sampling can be done. should I keep some fishes in cages. what statistical design will be implement 
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Thanks Dr. Sahadeven Payyadakath, It will be very helpful in my studies.
Regards
Sahar
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I need to determine the diet composition and relative proportion of plant species in mule deer diet. What I currently understand is that fecal samples need to be fresh in order to accurately sequence plant DNA in them. Unfortunately, the deer I am studying live deep in the wilderness and are difficult to spot and watch poop, so I've had trouble getting enough fresh samples. 
Does anyone have experience collecting older fecal samples for genetic analysis of diet, and how well did plant DNA amplify?
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One thing I know is that it also is dependent on the read length. This is also dependent on the brand/type of sequencer (ion torrent, Illumina etc) you will use.
Fecal samples are tough matter to do DNA analyis on, thats for sure. 
You might also want to make sure that the way you isolate and analyze does not create some kind of bias towards plants with large amounts of DNA or DNA that is easily degraded because of leaf structure.
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If I have a 100 Square Km of forest site (homogenous vegetation accros the site), how many ligne transects should I design to get reliable density estimates of forest primates through Distance Sampling methods  - Is there a relation between study site size and number of transect somewhere ?
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Well, you have to put your transects where the birds are! No bird present = no bird counted lol. So put the transects where the birds are because you simply want to know what their density is in those locations. There's nothing wrong with doing that. Sampling doesn't have to be random remember :)
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Hello all,
I want to analyze the role of spatial vs environmental effect (through variation partitioning) on Notonecta species distribution among fishless ponds.  I have been using adespatial package to do that.
After calculating the MEMs, I need to estimate the Moran`s I spatial autocorrelation values, in addition, to the positive and negative part of this index for the ten MEMs I have.
In adespatial package, in the dbMEM example of mite data, Moran`s I is calculated using moran.randtest, without using the listw function as follows:
test<-moran.randtest(mite.dbmem1, nrepet=99)
However, in the explanation of the test itself, listw is been used:
moran.randtest(x, listw, nrepet = 999, ...)
I have analyzed my data with and without using the listw and I get different results. I was not sure which one of the two methods is more appropriate since both examples are given in the same package. I was wondering if anyone has used the function moran.randtest and if yes, do I need to use listw to calculate the Moran`s I index?
I would really appreciate your kind guidance!
Mitra
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Hello Sacha,
I have copied the analyses I have done to calculate spatial correlation here. I think as you mentioned in your earlier comment, my x (notonecta.dbmem1 has a listw attribute), so even if I do not type it in moran.randtest, listw will be used to calculate spatial autocorrelation here: 
> notonecta.dbmem1 <- dbmem(pond_coord, thresh=0.01707804, MEM.autocor = "non-null", store.listw = TRUE, silent = FALSE)
> attributes(notonecta.dbmem1)$values
[1] 0.14567211 0.04095702 -0.03654004 -0.09026366 -0.09084915 -0.09090909 -0.09090909
[8] -0.09090915 -0.14911688 -0.18256433
> attributes(notonecta.dbmem1)$listw
Characteristics of weights list object:
Neighbour list object:
Number of regions: 11
Number of nonzero links: 78
Percentage nonzero weights: 64.46281
Average number of links: 7.090909
Weights style: B
Weights constants summary:
n nn S0 S1 S2
B 11 121 76.8873 151.6504 2420.918
> test <- moran.randtest(notonecta.dbmem1, nrepet = 99)
> test
class: krandtest lightkrandtest
Monte-Carlo tests
Call: moran.randtest(x = notonecta.dbmem1, nrepet = 99)
Number of tests: 10
Adjustment method for multiple comparisons: none
Permutation number: 99
Test Obs Std.Obs Alter Pvalue
1 MEM1 0.22924883 5.0022965 greater 0.01
2 MEM2 0.06445536 2.9170811 greater 0.02
3 MEM3 -0.05750422 0.8315211 greater 0.19
4 MEM4 -0.14205079 -0.6149102 greater 0.68
5 MEM5 -0.14297220 -0.7215775 greater 0.74
6 MEM6 -0.14306653 -0.7440757 greater 0.77
7 MEM7 -0.14306653 -0.6722448 greater 0.80
8 MEM8 -0.14306663 -0.8240201 greater 0.82
9 MEM9 -0.23466999 -2.6063308 greater 0.99
10 MEM10 -0.28730731 -3.0581972 greater 1.00
Best,
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Hi,
I am using Jacobs' Index (D) to study habitat preferences. The references said that D value range from -1 to 1. I am getting values lower to -1. Does anybody have information of D values out of suggested? Thanks.
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Instead you can use resource selection function (RSF) for use-availability data, e.g., package “ResourceSelection” in R.
Best,
Andrzej
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Hello, I'm looking for references and comments about the use of satellite chlorophyll tracking system for fish abundance estimate. Looking forward to seeing your comments on this. regards. pierre
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Only chlorophyll abundance cannot be used for figuring out the potential fishing zones. It has to be used in conjunction with temperature data. 
So, you need to have chlorophyll map and temperature map - Digitize the temperature gradient and chlorophyll levels - overlay both these vector files - The region of intersection of these vectors will give you the potential fishing zone. 
The validity of such maps produced will be only 2 to 3 days from the date of acquisition of the satellite imagery.
Regards,
Nithyapriya B.
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 I study the dynamic eagle owl (bubo bubo) population in Tarn department, France (census of breeding pairs, nesting habitat, diet analysis, productivity...). I also collect many data about the wildlife by camera trapping. I want to use my statistic skill (data mining) to analyze these ecologic data in relation with the landscape structure.
Currently I study with my colleague 60 sites and we would like analyze how bubo bubo select its nesting habitat.
So, we are looking for a free software which should allow us to assess the parameters of landscape structure such as areas of (open land, shrub, woodland...), ecotone number, orientation (azimut), distances to (water, building, road, village...).....
We use QGIS software but the calculation of these parameters, especially areas, are tedious and long (50 sites + 50 random sites = 100 !!!)
I'm interested in any information regarding those subject
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Hi,
This is a follow up to a previous question I asked: when generating "landscapes" with various levels of spatial autocorrelation, is it possible to specify the minimum and maximum values that a field is composed of? For example, one field with maximum of .5 and minimum of 0, and another with a maximum of .05 and minnimum of 0. How would the sill parameter come into play here?
Thanks in advance!
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Remember that for stationary random functions the sill is equal to the variance of the sample data.
I would be inclined not to restrict the simulated values to a range, but to specify a variance (= sill)  such that some high percentage of simulated values lie within the upper and lower limits, say 99%.  Standard tables of the normal distribution can be used to calculate that variance. 
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I am comparing habitat availability vs. use by a bat species using the R compana() function (package adehabitat) which uses Aebischer et al. (1993) method. It is a 3rd order habitat selection analysis, so the availability of habitats is different for each bat.
The compana randomisation test resulted in a nice habitat ranking plus a table with the significance of paiwise comparisons. The only thing I need more, are log-ratio differences or Manly selection ratio's (considering that these are given in almost every paper on habitat selection). Thus, I tried the Wi function that computes Manly's selection ratios. The problem is that the resulting ranking of habitats is very different from the one produced by compana, and the compana result seems to fit much better to the raw data. Apparently the results of these methods cannot be combined.
So now I am a bit lost. I hope someone is willing to explain
  • Why these two methods (compana and Manly selection ratio's) produce such different habitat rankings and how they relate to each other.
  • If I can obtain log-ratio differences within the compana function.
...Or any other advice on how to proceed.
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In my opinion, by using a GLMM you will overcome many problems originated by other methods. Additionally, GLMMs are much more flexible than other methods.
Example with radio-tagged individuals:
Weisshaupt, N., R. Arlettaz, T.S. Reichlin, A. Tagmann-Ioset & M. Schaub. 2011. Habitat selection by foraging Wrynecks Jynx torquilla during the breeding season: identifying the optimal habitat profile. Bird Study 58: 111-119.  
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Hi all, if I count a bird species on let's say 100 plots with 10 predictors and most outcomes are 1 or 0, but just a few can be 2, 3 or 4 - how can I use this information in the statistical analysis? Is replicating cases a valid approach - I mean if I analyse the habitat selection with eg. logistic regression and then replicate the case 2, 3 or 4 times. Thanks!
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Hi, have you heard about occupancy modelling as a tool for habitat selection?
I think that this analysis will be useful to you for estimate habitat selection.
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In my model, I would like to test how different plant characteristics (e.g. tree height, dbh, crown size) and their distance from the forest edge effects the fruit production. Several articles e.g.Bunyan et al. 2012 explain that the inclusion of multiple edges significantly improved their model fit based on AIC value. I would like to know, if I measure e.g.distances for the forest edge in the 4 cardinal directions for each tree, how could those four measurements be incorporated in one model, to test whether multiple edges have a higher effect than one-edge models.
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Is it this reference? – M. Bunyan, S. Jose and R. Fletcher, "Edge Effects in Small Forest Fragments: Why More Is Better?," American Journal of Plant Sciences, Vol. 3 No. 7, 2012, pp. 869-878. doi: 10.4236/ajps.2012.37104.
They include multiple edges in their model. You could always write to them.
In contrast, my fruiting model used toroidal wrap, which meant there were no edges! The birds reappeared in the forest arena if they flew past the edge. This seemed more realistic. We used toroidal distances to account for this instead of euclidean distances. You could consider doing this?
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Hi all,
I'm attempting to model habitat selection by bandicoots using spatial environmental variables, with the aim to predict habitat use across a landscape.
Using variograms, I've identified spatial auto-correlation within the data. I have subsequently included a correlation factor to the model. Models containing the correlation factor are shown to be a better fit for the data based on AIC model selection.
When confirming whether the spatial auto correlation has been accounted for appropriately within the data - the variogram of the normalized residuals shows a cyclic pattern.
Any advice as to how to remove this spatial dependency, or, the reasons this cyclic pattern is produced would be very much appreciated.
Anthony.
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Hi Donald, 
Thanks for your response. We have converted our tracking data into a spatial count of presences / unit area metric, however, we have since decided to shift our analysis as we thought the complexity of this model wasn't necessary given our core questions. 
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I am creating resource selection functions to examine habitat selection using radio telemetry data and 1:1 paired logistic regression models. Habitat features at each telemetry point are paired with a single measure of the availability of those features that is unique to a particular telemetry point. I first fit models using R's glm function by differencing the used and available points and fitting a no-intercept glm:
glm(point ~ -1 + diffx1 + diffx2 + diffx3 ... diffxn, data, family=binomial)
I have also fit the same model using the survival package's clogit/coxph functions. In this case, each 1:1 pair of used/available points is grouped into a stratum:
clogit(point ~ x1 + x2 + x3 ... xn + strata(pair), data)
I have 16 covariates and some of these are correlated. However, when I calculate pair-wise correlation coefficients and variance inflation factors I get different results depending on whether I use the differenced data used to fit the glm or the original data used to fit the clogit/coxph. Both glm and clogit/coxph give me identical results but the different degrees of collinearity have a strong influence on the resulting analyses.
Does anyone have a recommendation of which form the data (differenced or original) I should use for assessing collinearity.
Many thanks!
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Hi Melanie and Hein,
Thanks for joining in on the discussion (and for the papers Hein, I look forward to reading them). Hopefully the following text answers both of your questions.
When we fit a conditional logistic regression using the glm, the response data are all 1's and the independent covariates are the differences between the used and available habitat values (i.e., within a 1:1 paired design). We remove the intercept to fit a no-intercept model which forces the relationship between relative probability of use and the independent variables to go through 0.50. The R syntax is:
glm(point ~ -1 + Difference1 + Difference2, data=data, family=binomial)
When I fit the same model using coxph, I do not difference the used and available data and the response variable is a an R object of class "Survival" (see the Survival package for details) of syntax:
Surv(time, point)
where time is always one (time-to-event = 1) and point is 0/1 indicating use/available. Additional, the strata argument must be used to identify matched (i.e., paired) points. The complete syntax for fitting a conditional logistic regression with coxph is:
coxph( Surv(time, point) ~ X1 + X2 + strata(pair), data=data)
These two formulas give identical beta estimates, AIC model rankings (but different absolute AIC values), standard errors, and p-values. I think this is because the syntax for coxph "tricks" the function into maximizing a likelihood that is identical to the likelihood function for a conditional logistic regression. I think that coxph differences observations within a pair "behind the scenes" but I am not positive.
Now regarding collinearity, if you calculate a correlation matrix for Difference1 and Difference 2 and then another matrix X1 and X2, you get different correlation coefficients. This makes sense because Difference1 = X1used - X1available and, in our study design, availability can vary widely from point to point even if used is fairly similar. These differences in collinearity means that different covariates are included in the final model set, depending on the cutoff for determining collinearity. Hence my original question, if these two different formulas which use different structures for the response variable give identical results, which data structure should be used to assess collinearity?
Hope this helps and thanks again for your questions,
Javan
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if we collect preys from natural habitats of one fish species which are similar to these in fish stomach to feed this fish in the experiment and get the better performance of growth and survival of fish compared with other food, can we identify that these preys affect habitat selection of fish ?
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Thanks!
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The communities abundances were measured under 3 different treatments, over a time period of 55 days.
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Not sure how you sampled through time, but you could, potentially, use a GLM, and test for differences through time, across treatments, and as an interaction of time and treatment. You can do this pretty easily in SPSS and R. That would help shape an understanding of how changes through time affected each community as well as just the treatment. When you talk about community composition, what metric are you using for that? Is it like one index value or is it a consideration of richness, diversity, evenness all as separate indices? If all you did was sample at the end of the 55 days, you can't really map changes through time, then just comparing the treatments through ANOVA should probably work. Dissimilarity indices are useful too, and applying them in concert with a GLM might help you better understand what drove some of the community changes.
Hope that helps.
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I hope to conduct a directed dispersal study of the remnant population of Chinese yew.
I am particularly focused on the habitat selection of frugivorous birds after they foraged seeds. 
Is there anyone with experience for combining habitat selection by birds and directed dispersal?
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There is abundant published research on that general topic, both empirical and theoretical. Just as some examples that could serve as introduction to previously used methods and theoretical frameworks:
Russo, Sabrina E., Stephen Portnoy, and Carol K. Augspurger. Incorporating animal behavior into seed dispersal models: implications for seed shadows. Ecology 87.12 (2006): 3160-3174.
Herrera, Jose M., Juan M. Morales, and Daniel García. Differential effects of fruit availability and habitat cover for frugivore‐mediated seed dispersal in a heterogeneous landscape. Journal of Ecology 99.5 (2011): 1100-1107.
Spiegel, O. and Nathan, R. (2012), Empirical evaluation of directed dispersal and density-dependent effects across successive recruitment phases. Journal of Ecology, 100: 392–404.
Aukema, Juliann E., and Carlos Martínez del Rio. "Where does a fruit-eating bird deposit mistletoe seeds? Seed deposition patterns and an experiment." Ecology 83.12 (2002): 3489-3496.
Santamaría L, Rodríguez-Pérez J, Larrinaga AR, Pias B: Predicting spatial
patterns of plant recruitment using animal-displacement kernels.
PLoS One 2007, 2:e1008.
Schupp, Eugene W., Pedro Jordano, and José María Gómez. Seed dispersal effectiveness revisited: a conceptual review. New Phytologist 188.2 (2010): 333-353.
Rodríguez-Pérez J, Wiegand T, Santamaría L: Frugivore behavior
determines plant distribution: a spatially explicit analysis of a
plant-disperser interaction. Ecography 2012, 35:113–123.
Morales JM, Carlo TA: The effects of plant distribution and frugivore
density on the scale and shape of dispersal kernels. Ecology 2006,
87:1489–1496.
Russo SE, Portnoy S, Augspurger CK: Incorporating animal behavior into
seed dispersal models: implications for seed shadows. Ecology 2006,
87:3160–3174.
Robledo-Arnuncio JJ, Klein EK, Muller-Landau HC, Santamaría L (2014) Space, time and complexity in plant dispersal ecology. Movement Ecology 2:16. (see section 2 in particular)
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Can you give me examples of studies where species responded to different habitat types, depending on the spatial scale use in the analysis?
Studies with amphibians would be particularly helpful!
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Hello André,
I hope these (please find below) articles can help you:
Saab, V. (1999). Importance of spatial scale to habitat use by breeding birds in riparian forests: a hierarchical analysis. Ecological applications, 9(1), 135-151.
Orians, G. H. and J. F. Wittenberger. 1991. Spatial and Temporal Scales in Habitat Selection. The American Naturalist 137:S29-S49.
Rettie, W. J., & Messier, F. (2000). Hierarchical habitat selection by woodland caribou: its relationship to limiting factors. Ecography, 23(4), 466-478.
Byrne, M. E., J. Clint McCoy, J. W. Hinton, M. J. Chamberlain, and B. A. Collier. 2014. Using dynamic Brownian bridge movement modelling to measure temporal patterns of habitat selection. Journal of Animal Ecology 83:1234-1243. 
Bleach, I., C. Beckmann, G. P. Brown, and R. Shine. 2014. Effects of an invasive species on refuge-site selection by native fauna: The impact of cane toads on native frogs in the Australian tropics. Austral Ecology 39:50-59.
Blaustein, L., M. Kiflawi, A. Eitam, M. Mangel, and J. E. Cohen. 2004. Oviposition habitat selection in response to risk of predation in temporary pools: mode of detection and consistency across experimental venue. Oecologia 138:300-305.
Best regards
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Hello to all, 
I am doing a Phd in Population ecology using the Lesser Kestrels as a model species. I am using lighweight  (~4 gr) GPS units from Technosmart to record the habitat selection and the movements during the Egg laying, Incubation and the Chick rearing period. I want to use a lighweight gps/gsm lightweight unit to track the juveniles leaving the natal areas and to monior recrruitment in colonies, unfortunately setting a  base stationand  radiotrack (due to time consuming) are either  unappropriate methods . I am planning to track about 20 individuals and  the area is bigger than 8.000 square kilometers. Does anyone has any idea?
Thanks in advance,
Konstantinos
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Hello,
We use Microwave Telemetry's 22g Solar Argos/GPS PTT for our lesser-prairie chicken work and we've been very impressed with the transmitters and the company. I'm sure 22g is too big for your species, but I know in maybe the past month or so they came out with a 5g solar PTT that might work for you.  I would contact them and ask about it.  I think they're only producing a limited number of 5g PTTs for the first year, but they also make a 9.5g solar PTT. Here's the website:
If you call in, ask for Cathy.  She's been incredibly helpful in answering my many questions.  When I first called in, she also gave me the names of several researchers who were using their product in similar projects.  Talking to them was very helpful and I'm sure she could point you in the same direction.
Best of luck,
Ashley 
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How to combine the habitat selection rule of disperser species into a dispersal effectiveness study?
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Dear Ning Li,
Non foraging habitat use is often explained after some of the following factors (they are not mutually exclusive): thermal insulation (especially in cold areas), protection against predators, access to sexual mates, proximity to preferred feeding areas, and in some birds you could also consider territorial landmarks. Other factor could be of importance depending of the species. Depending on the species and its natural history, you could develop some hypothesis (i.e. after foraging move to secluded places, or males go to landmarks and female to sunny warm areas to help digestions) and test that against your observations.
You could also expect some kind of relationship between post-feeding areas and habitat requirements of preferred food species seeds.
I do not know if that is of any help.
Regards,
Jabi
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I want to find out the abundance and density of insects in my study area. I have 5 habitat types. This data will be used further for habitat selection studies. My study area is around 10km  sq. I will be carrying oht quadrant sampling with eaxh quadrant 5x5 m size.i want to know how many samples to collect for each habitat type 
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Thank u sir.could you refer me to any paper or book?
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I would like to know if it is possible to use GPS locations of wolf's scats as a radiocollar fix. My doubts are that this datas violate the assumptions of statistical analysis (eg. indipendence of samples) such as multiple logistic regression.
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Expanding on answers by Williams and by Zollner above, wolves are highly territorial and use scat and urine for scent marking territorial boundaries.  Consequently, use of scat location data for wolf habitat selection studies will tend to have a bias towards territorial boundary areas and the core areas may be under-represented.  I have found that a single monitoring approach is rarely adequate for studying presence and habitat use by wolves and thus multiple approaches (e.g., scat/track, telemetry, camera traps) are often necessary to balance the strengths and weaknesses of each approach. 
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How do we differentiate between positive and negative selection. These two terms sound like they are complementary to each other.
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Positive selection: also called (Darwinian selection) variants that increase in frequency until they fix in the relevant population. The selective pressure that leads to this fixation is termed positive selection.
Negative selection: Also called purifying selection, it means that selection is purging changes that cause deleterious impacts on the fitness of the host.
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My study area includes a number of residential areas (rural) with different population size that are distributed unequally from the grouse sites. Do you think that we can generate fuzzy map with population size (I feel population size variable is a discrete data) across region? if so, how?
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I think you would benefit from considering both the human population size and the spatial distribution of human development. For instance, a clumped pattern of development usually have a lower impact in the environment than a scattered pattern. There are several spatially explicit indicators that allow you quantifying these dimensions. Please, let me know if you need further information on these indicators and provide more details about your study and available data.
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Its complicated to explain this question: I have two species, competitors by interference, but in my study area they have considerable spatial overlap. I think it might be based on the limited resources of the area, that "force" them to be together, even when risks are present. Like in a strong drought, all animals come together in water sources. 
Is there an theoretical concept for this? I was thinking first in environmental filtering, but it doesn't seem correct. 
Any existing concept? Thanks!
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As Fathul suggests spatial processes can mediate competition in a number of different ways to facilitate coexistence. There is an excellent review of theory and data here: http://onlinelibrary.wiley.com/doi/10.1046/j.1461-0248.2003.00530.x/full, free pdf here: http://izt.ciens.ucv.ve/ecologia/Archivos/ECOLOGIA_DE%20_POBLACIONES_Hasta%202004/ECOL_POBLAC_Hasta%202004_(A-G)/Amarasekare%202003.pdf.
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I am studying the effects of landscape metrics on Caucasian grouse breeding display site selection. One of independent variables I am interested to know its role is the impact of human residential population size. But I do not know how to make a relationship between population size variable and habitat selection with increasing distance from residential areas. I would be pleased to get the opinion of my colleagues in this regards.
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I think that you can use R where there are a lot of application
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Vertical distribution is the position in that each bird is placed in each one shelf panel of mist nets and can be useful for monitoring habitat selection.
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Hi Rubén,
I didn't  know about this publication. Thank you very much.
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We have a lot of line-transect samples of bird communities (spanning from 2 to 10 km long). Within transect the data are obviously autocorrelated, we therefore cannot compare transects simply splitting the data to get variability.
How can we cope with this problem, to obtain comparable data for between-transects comparison?
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Ciao Guido. Do you want to compare transects in term of bird community indices i.e. species richness or model species density or just presence absence?
I think you could use the lenght of your transect as an offset in poisson GLMs.  or  the article Spatial Models for Line Transect Sampling by Sharon L. Hedley and Stephen T. Buckland (Journal of Agricultural, Biological, and Environmental Statistics, Vol. 9, No. 2 (Jun., 2004), pp. 181-199) could help you (see link)
By the way, as long as each single transect is your sample unit you should correct your bird community indicies by the length of the transect and be more worried about between transects autocorrelation.
I would be worried about spatial autocorrelation within trasects only if you want to model each presence point inside a transect and compare it with other transects.
I hope it helps.
Enrico
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When doing habitat surveys by doing vegetation registrations for a given reptile it is important to also include vegetation registration for random points, this to do habitat selection analysis.
For our survey I wish to get some feedback if my solution is a good approach.  
I already have vegetation data at locations with observations of a given reptile. It is along a "transect line", or strictly speaking it is along a dry river bed. 
Along this river bed I could create a buffer-shapefile by the use of GIS. The buffer will be the same range as for the reptile observations (i.e. range of the distance from the center of the river bed to the reptile observations). Within the buffer I will use GIS to create random points. These random points will be the places that we have to visit for registrations.
To find the points we have to use a handheld GPS, unfortunately the accuracy is +/- 5 m. So when arriving the point, we have to randomly select the location for registration.
One solution is as following when arriving the random point:
--> That we close our eyes and turn around some times and then toss something light weighted object that easily attach to branches, and we “treat” the object as a reptile and do the vegetation registration as it was a reptile observation. 
Is this a good approach? Remarks and suggestions are welcome.
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Is this reptile's movement restricted to the riverbed, and is that why you are forcing your random points to be within the riverbed? As for choosing a random point on the ground to conduct your vegetation registration....your GPS is already generating that randomness for you. I suggest that you simply walk to the point where the GPS indicates you should be (i.e, when the "distance to waypoint = 0" for the first time). This GPS location is the actual (true) location + random error (<=5 +- meters). I don't see a need to introduce additional randomness. If you felt that you had to add more randomness then I simply suggest that you a priori generate a list of random distances from a uniform distribution bounded between 0 and 5 (R code: runif(n=20,min=0,max=5)) and similarly generate a list of random compass bearings. Then when you get to the GPS location you use the first random distance and bearing from the list to find your sampling location. 
Either of the two approaches I suggested is less subjective and less open to criticism then the 'throw an object into the air' sampling approach.
Cheers,
Jason
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I am interested in examples where one invasive species was replaced by a second invasive species. I am working with invasive parasites of the genus Anguillicola. To our knowledge A. novaezelandiae was replaced by A. crassus from a Lake in Italy. Do you have examples of other species?
Thanks!
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On Mauritius there are several examples of  exotic birds that were flourishing, that declined and were replaced by a subsequent introduction.  The Java Sparrow was replaced by the House Sparrow, the Fire Finch was replaced by the Common Waxbill, and the Cape Canary replaced by the Village Weaver.  Also less convincingly the Madgascar Lovebird was replaced by the Ring-necked Parakeet.  These cases are published in a paper I wrote a long while ago, and summarised in Lost Land of the Dodo, by Cheke and Hume.
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Habitat preference, Habitat occupancy, habitat selection and habitat usage, any definitions?
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Habitat occupancy is the population limit of a given habitat. Habitat preference is the habitat most likely to be chosen by a species given the opportunity or which habitat the species is best suited for. Habitat usage is how a species manipulates its surroundings to better its odds of survival, how it interacts with its habitat. Habitat selection is the process by which a species chooses its habitat.
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I need articles for my research about raptors habitat preference from autumn to February. If you have some articles or references about this theme, please write in the discussion below or directly to me. Many thanks, Vlado.
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Thank you very much, Spanish is ok.
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Habitat usage by animal species is driven by availability of food, nest sites, protection from predators, presence of competitors or facilitating species, etc.
However GIS-based models typically include land cover, few factors related to human disturbance, climatic and topographic factors. Usually the model is built with the only relevant factors for which georeferenced data are available for the study area.
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I think Federico Morelli observation is very important, define the resolution and scale for the input variables and subsequently the models are crucial. Nowadays many HDM models use Global data sets in particular for climate data. Providing the coarse resolution of these data, they cannot be used to produce good models at local or regional scales. In some regions of the world there is a lack of more accurate environmental data, and then the use of these databases may be acceptable nevertheless in such cases the number of variable need to be keep to a minimum avoiding redundancy so not to carry on with the intrinsic errors produced by the lack of accuracy of such variables. In mountain regions and complex landscape this is a delicate issue and decisions in terms of data quality are crucial as this change the whole outputs of the models. I advise also the use of platforms like BIOMOD that will help to analyze the performance of different models according to the variables used and the object to be modeled. We found very different results in terms of the different model performances for animals vs. plants. It is clear that adding movement trajectories to the animal models provide much better results but obtaining the data is also more expensive.