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Spatial Ecology - Science topic

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I have some environmental covariates derived from the digital elevation model (slope, gradient, channel network distance, etc.) in raster format.
I want to identify areas of similarity between the covariates and somehow identify the smallest possible size area (or areas) to serve as a reference area.
  1. Soil data points will be collected to create predictive models in this reference area.
  2. The predictive models developed in the reference areas should fit when extrapolated to regions outside the reference area.
  3. Therefore, the covariates will cover this external area.
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Erupting volcanoes cause sudden, drastic change in an area, forcing organisms to evolve rapidly to adapt to the new environment. Change in an organism's environment forces the organism to adapt to fit the new environment, eventually causing it to evolve into a new species. They eventually become different species. So that changes in environmental conditions can affect the survival of individual organisms or an entire species. Short-term environmental changes, like droughts, floods, and fires do not give populations time to adapt to the change and force them to move or become extinct. However, short and long term environmental changes Quiz - Quizizz. What is an example of how organisms respond to short term changes in the environment? Animals tend to eat a lot more before the change occurs to have stored energy. Animals will go extinct or die due to the short term change. Therefore, in evolutionary theory, adaptation is the biological mechanism by which organisms adjust to new environments or to changes in their current environment. The idea of natural selection is that traits that can be passed down allow organisms to adapt to the environment better than other organisms of the same species.
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I am looking for a solution with the R programming environment that will allow me to simulate animal movement (using a correlated random walk or other chosen model) within a polygon boundary, which acts as a reflective boundary to the movement.
I did find a solution (http://tinyurl.com/jbyuty8), but this has ArcGIS has a program dependency. I prefer to use open-source solutions.
The "adehabitatLT" package has a number of simulation functions, but I cannot find one that allows specification of a bounday argument.
Any helpful hints out there?
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I used R package GLATOS to build Random Walk simulated tracks constrained into a defined polygon
See applcation in our paper:
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Hi,
I am checking for spatial autocorrelation in my dataset. It comprises the ID of the nests, the longitude and latitude for each of the nest boxes and the number of fledged chicks for each nest box. I want to know if reproductive success is spatially autocorrelated in our bird colony.
For this, I computed the distance matrix for nest boxes to know the distance between each nest box and the rest of nest boxes. Following this, I designed distance bands (distance lags) to calculate Moran's I for each lag specifically. As I have multiple data for several years (2014-2020), I wonder if there is any way to get a mean Moran's Index of all the years, instead of calculating an index for each year.
It is my first time doing these types of analysis so any advice would be very much appreciated!!
Thank you.
Iraida
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Hey, Iraida Redondo García sorry for the delay.
I think that the unit will depend on which unit is the coordinate, no? If it's decimal degrees, then the distance is in decimal degrees.
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Burlap traps are a way to mitigate the invasive Lymantria dispar dispar (tussock/gypsy moth) caterpillars, which defoliate mainly hardwood deciduous trees. Burlap is wrapped around trees and tied with twine, then folded to create a flap and ideal conditions where the caterpillars migrate into. The caterpillars are then disposed of in soapy water when the traps are checked.
If I want to study spatial ecology of these caterpillars, using quantitative analysis from each trap at a small lake surrounded by forest, how should I prioritize trap set-up (location, amount)?
Should the traps be completely randomized?
My study area is at maximum 2 square kilometres with a small Lake taking up about 0.25 of those square km.
Ideally I want to minimize confounding variables such as tree species the traps are placed on.
The goal of this project is to determine spatial distribution of the caterpillars and to mitigate them with weekly checks.
Any help would be greatly appreciated!
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As far as the traps concern I recommend using completely randomized block design in setting your traps.
As for surveying the Lymantria dispar, my suggestion is to go for line transects.
As for burlap issue, I would suggest to go for some baits in parallel.
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Hello,
I am currently trying to determine which interpolation method would best fit my GPS tracks. My "model" track has a 100 high quality consecutive locations with no gap and I excluded first random unique locations and then a number of consecutive points starting at a random position, to assess how accurate the different methods are.
I have tested linear interpolation (from move package) and correlated random walk using the method described in Technitis et al. 2015 (spacetime package).
I would like now to test a few curvilinear methods (e.g. exact cubic, natural splines, bezier...), but I can't find a spline or Bezier function that simply fills the gaps in my GPS tracks based on the missing timestamps. The spline function from base R collapses the results to a unique x value, which is not what I want obviously. Dr. Jed Long's package interpolatepathR would have been ideal if I only had a few unique fixes to interpolate, but I have far too many, and the package doesn't seem to handle long tracks (like my model track or longer).
So, does anyone knows of a package in R (I do not have access to Matlab) that handles spline and other curvilinear interpolation for (animal) paths, filling gaps (missing timestamps) from imperfect GPS data?
Thank you for your time,
Chloé
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hi
if you want a example of the spline methods in R you can click this link: i think i will be heplfull for you
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Hello, I have mapped the locations of several individuals of a certain species of bird. I have the GPS coordinates, the date and the time data every half hour for several consecutive days and I am trying to apply the Package 'mkde' to get the kernel density maps based on the movement (home range). Is there someone with experience in using this package who can help me?
Thank you very much!
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Alas, don't know that program. But there are a number of others that might serve your needs including the density analytics in, for example, the ESRI tools like ArcGIS and ArcPro.
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I was doing some geostatistical analysis (variogram+kriging) for a "presence only" type data in a species distribution modeling context. Since, we know that when estimating the (empirical) variogram, the attribute is basically assumed to be a realization of continuous random variables (although an attribute can occur in counts too). If the attribute is just the presence, and no sub-categories then all the values at all positions will be same (say 1, if we denote a presence by 1). Hence the variogram can not be calculated, not even the indicator variogram.  In some papers such as [1] and references there in,  a grid based approach was used. In this approach a grid of certain size (e.g. 10 x 10 m etc) was superimposed on the sampling area and the number of species inside each cell were counted. This constitutes a count/frequency table like data. In the other approach pseudo absences or background data were generated using some algorithm e.g. Maxent etc (see e.g. [2, 3]). The pseudo absences are generated taking many factors into account and stacked/combined with actual data. This is merely generating x, y coordinates and giving it an absence status (say 0s). The result is a binary data with two categories, presence 1 and absences 0.   
Now the questions that are bothering me are
1. For the grid based approach, what should be the optimal cell size? How to find it and decide it? How to proceed with variogram with kriging etc?
2. For pseudo absences/background approach, how many absences (as compared to actual data)? How to decide it? How to proceed with variogram with kriging etc?
Reference
1. Rossi, Richard E., et al. “Geostatistical Tools for Modeling and Interpreting Ecological Spatial Dependence.” Ecological Monographs, vol. 62, no. 2, 1992, pp. 277–314. www.jstor.org/stable/2937096.
2. Tomislav Hengl, Henk Sierdsema, Andreja Radović, Arta Dilo, Spatial prediction of species’ distributions from occurrence-only records: combining point pattern analysis, ENFA and regression-kriging, Ecological Modelling, Volume 220, Issue 24, 24 December 2009, Pages 3499-3511.
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Thanks for your interest. Look at my latest paper about dengue prevalence 2020. I have explained the method very well. Let me if you need help with your data. You can contact me at asad06@gmail.com.
Cheers,
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Dear colleagues. Does anyone know of any work in which the criteria of functional traits of plants related to hydrological ecosystem services (e.g., hydrological regulation, drought mitigation or water saturation mitigation) has been used to spatially delineate an area for conservation planning, management, or decision making?
I think Moore et al. 2017 (Towards a trait-based ecology of wetland vegetation. Journal of Ecology 105: 1623-1635. doi:10.1111/1365-2745.12734) paper is enlightening from a conceptual aspect, but I need criteria and arguments from a spatial approach.
Thanks a lot
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Dear Nicolás, I can't recall specific studies on that to be honest... what I have seen are studies relating the plant-soil interaction with a focus on water budget, but I can't recall that being used for planning purposes.
I found one article that could be mostly related to what you are looking for, hope it helps.
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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 would like to extract a number of bioclimatic variables (mainly from the Worldclim database) from the distribution ranges of several animal species. These distribution ranges are presented as shape files (.shp). Has anyone got some insightful links or info how to perform these analyses in an effective way using R? Unfortunately, I am only familiar with extracting variables from coordinates directly. Thanks!
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I am looking for research and methodology for mapping roof-nesting seabirds in urban environments. This is for a pilot project. Any tips and articles are appreciated! Thanks.
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Dear Sari Christine,
Classic ways to map birds in urban environment are the uses of remote sensing and photographs but you now can (if allowed) use drones to map roof-resting birds and transfer your data into QGIS.
Best regards,
Guy
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Colleagues and I are weighing options on how to fit GPS transmitters to adult Black Swans in New Zealand. Adults weigh ~4-7 kg. We're apprehensive about using collars as they may get caught in vegetation while foraging. Also concerned about satellite uplink capabilities down here (collars are only available from overseas companies). Another option is a dorsal attachment, but we've been advised against using any type of harness. We're leaning towards tail-mounting ~40g transmitters (Sirtrack PinPoint Iridium) to tail feather(s) just after moult. We should be able to get ~3 fixes per day for 9 months, which would cover winter and the following breeding season. However, we're concerned about whether these will stay attached. Tail-mounting has been done on gannets, penguins, gulls... but I haven't seen this on swans or other large waterfowl. Curious if anyone has any suggestions.
Thanks in advance.
Mark
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Hi Mark,
My answer is probably a bit late for you, but have a look at this:
An inexpensive satellite-download GPS receiver for wildlife: field trial on black swans
Rebecca M. Lehrke, Lizzie McGregor, John Dyer, Margaret C. Stanley, and Todd E. Dennis. Wildlife Research 2017 44 (6-7), 558-564
Hope the citation helps!
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I am looking into Spatial Neighbors to address autocorrelation in my dataset, but I find it difficult to find arguments as to which method to prefer. I am using the R package "spdep" and functions dnearneigh and knearneigh to determine the distance-based neighbors and k-nearest neighbors, respectively. However, could someone advise me on the main differences between the two methods, as well as on how to determine d2 (upper distance bound) and k (number of nearest neighbors).
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"semivariograms and distance",there are two problems with Fararini's comment.
With only data values you can only estimate and model the variogram (using an empirical variogram). This is not an exact process, i.e. there is no way to be able to claim that you have chosen "the" model and have exactly estimated the parameters of the model. Moreover for a given (finite) data set the empirical variogram is not unique, you must choose the lag spacings and also you must distinguish between an omnidirectional empirical variogram and directional empirical variograms.
Secondly, the variogram is only indirectly related to correlation (unlike a covariance function or (auto)correlation function). A variogram model need not have a true range, e.g. the exponential and Gaussian models do not, moreover the Power models do not even have a sill. It is common to refer to a range for both the exponential and Gaussian models but this is not a theoretical result, rather it is the distance at which the value of the variogram model is 95% of the sill (95% is an arbitrary choice). If the variogram has a geometric anisotropy then the range depends on the direction (in 3D there are two direction parameters)
However the suggestion of using the empirical variogram to choose a maximum distance is not a bad one but recognize that you are choosing a distance, you are certainly NOT determining the "exact distance at which the autocorrelation stops/starts". It is critical to distinguish between an empirical variogram and a theoretical model.
Finally, "semivariogram" is long out dated terminology (since about 1988 when G. Matheron stopped using it)
To Chris Broekhoven, since you are using R packages, you might want to check references on the author of those packages. Do a search in Google for "R package dnearneigh", you will find a lot of documentation and some tutorial examples as well as for "R package knearneigh"
Both dnearestneighbor and knearestneighbor pertain to data sets where the interest is mostly or completely in the locations of the data points, variograms pertain to data sets where there is a value (for some variable, e.g. hydrologic parameter) at each data location and the interest is in how that value relates to the position coordinates.
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Exploring options (brand, make and model) for satellite transmitters to be attached to captured Northern Goshawks in northern Arizona.
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Deven,
We have had really good results with Microwave Telemetry PTTs on both Whooping Cranes (leg mount) and Reddish Egrets (Microwave's service department has also been really easy to work with).  For the latter project, we attached 17 g transmitters (solar powered Argos/ GPS PTTs; http://www.microwavetelemetry.com/bird/solarArgosGPS.cfm) backpack style with a Teflon harness on Adult Reddish Egrets (I believe all >600 g).  We haven't had any transmitter failure with these (first ones deployed May 2016), and the lifespan on Whooping Crane transmitters was >3 years in most cases.  Early on we tried Northstar transmitters with Whooping Cranes, and we didn't have comparable lifespan.  Also, if you have good cell tower coverage, you may want to consider GSM/GPS transmitters because they work off of cell towers rather than communicating with satellites (http://www.microwavetelemetry.com/bird/GSM.cfm).  Using GSM/GPS transmitters can cut the annual data costs considerably over ARGOS/GPS PTT transmitters.  ARGOS satellite services is ~$100/month per individual (~$1200 per year), so you can see how this could get expensive pretty quickly with 20-30 birds tracked for 3 years.
I hope this is helpful.  If you would like more info, you are welcome to send me a message.
Best,
Will
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Hi, I have mapped a data set where I have two populations - positive and negative for a certain bacterium (irrelevant to my question though). I used BatchGeo to simply map the data, no probem. Now I need to do cluster analysis of the points, or point pattern analysis, to see whether there is a pattern in the positive samples. I have tried to use ArcGISpro, and have mapped the points. I can't get any further though. I can't see how to label two distict groups (pos neg) and then analyse them. Anyone ever done this and can help? Or can anyone suggest a more user-friendly program to do so? I am sure ArcGIS can do this, but I have zero training in this and am stuck! I know nothing about coding and ArcGIS requires some knowledge, apparently.
Thanks for your help!
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Hello Sharon! In ArcGIS there is "Search toll" (Open ArcMap and set Ctrl+F), find by keywords to apply wherever... as you know what you want to do (clusters analysis, identify patterns... and so on...) It will not so difficult.
Or, other possibility is to use Geoda software. It works very well for spatial analysis.
Hope to help. 
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I want to compare the niche overlap in 4 species of arboreal lizards. The other issue I'm having trouble wrapping my head around is that in typical overlap analyses where habitat categories are used, they are discrete categories like "forest", "field", "wetland"... I have characteristics of trees that have sub categories such as bark roughness broken into 3 sub categories (low, medium, and high), tree complexity (low, high). This is because each tree has it's own habitat classification. In my data, different species prefer/avoid particular categories (based on manly selection ratios - adehabitatHS). 
I'm wondering if I should do overlap analysis for each category (ex. bark roughness) separately because if I combine all categories (bark roughness, tree complexity, etc.), they % of use doesn't add up to 100% (although it does if I looked at tree complexity or bark roughness separately).
Hoping this is not too confusing, thanks for any input.
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Thanks for the feedback, I'll have a look at these suggestions.
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Dear all,
Does someone know if any R package can be used to perform meta-analysis take into account spatial and temporal autocorrelation (maybe separately)?
#spatial
I work on fish abundance data and associated diversity metrics at 35 stations located along several large French rivers (Rhône, Vienne, Loire, Meuse, Seine).
Some of the stations are closers than others : for example, 7 stations are located in the same area (distance <10 km) while some of them are located in different catchments without direct connectivity. Consequently, I expect that my data/results will be strongly spatially autocorrelated.
I am looking for a way to correct the time series meta-analysis for this spatial heterogeneity in R. Ideally, I was thinking of a method that would allow the weighting of the different time series in the meta-analysis according to their relative distance along the river network.
#temporal
The stations were sampled annually and the time series range from 18 to 36 years. So, consecutive years are likely to be more correlated than the first and the last years for instance. I would like to correct the temporal autocorrelation in the meta-analysis. For now, I have applied a Mann-Kendall trend analysis that account for the temporal autocorrelation, and I have extracted the correlation coefficient to be used in the meta-analysis. Do you think of another way to perform this correction?
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The package below might be an option, but I agree with the recommendation above to test if your errors are independent, and then, if they are not, test if they are spatially auto-correlate, before trying to use a spatial model. https://cran.r-project.org/web/packages/MARSS/vignettes/UserGuide.pdf 
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1. I'm looking for GIS tools (software/plugins/etc) which are dedicated to analyze species habitat selection. Something that gives in the output habitat selection indexes or model/function.
2. Is there any way to estimate Resource Selection Functions in GIS software?
Thank You in advance for answers and comments!
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Arc/GIS and Erdas Imagine is best software
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Hello,
I would like to ask about any method for testing spatial dependence on categorical data (e.g. vegetation types polygons) and tools for modeling it against environmental data.
Is Multinomial Regression suitable for this task?
Thanks!
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Hi!
You can perform spatial analysis on categorical data by using join count statistics.
A quick overview of the method can be found here: http://www.gitta.info/DiscrSpatVari/en/html/spat_depend_join_ct_stat.html
Mathematical Functions of the Method: http://www.people.fas.harvard.edu/~zhukov/Spatial2.pdf
Hope this helped :)
Best,
Karolina
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I'm trying to manipulate raster dataset using gdal software. When clipping bigger map to smaller raster data, it seems both gdal_translate and gdal_wrap work. What are the differences in using these two functions in general?
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The gdalwarp utility is an image mosaicing, reprojection and warping utility. The program can reproject to any supported projection, and can also apply GCPs stored with the image if the image is "raw" with control information.
The gdal_translate utility can be used to convert raster data between different formats, potentially performing some operations like subsettings, resampling, and rescaling pixels in the process
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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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Could you share references about works of remote sensing based on comparison between "Mean shift segmentation", "Watershed sgmentation" and "Multiresolution segmentation"?
I need to review the state of art in forestry aplications, vegetation delineation, trees detection, etc.
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Hi Oliver, it is hard to find out papers that exclusively discussed about only these three types of segmentation methods. The easiest way to find papers that might be of your interest to search papers with the key words, "survey" or "review" on segmentation methods used in remote sensing. However, I have here enlisted some papers that might be of your interest. Here they are:
1. A REVIEW ON IMAGE SEGMENTATION TECHNIQUES WITH REMOTE SENSING PERSPECTIVE by V. Dey a , Y. Zhang , M. Zhong.
2. A Survey: Image Segmentation Techniques by Waseem Khan, International Journal of Future Computer and Communication, Vol. 3, No. 2, April 2014.
3. EVALUATION OF REMOTE SENSING IMAGE SEGMENTATION QUALITY –
FURTHER RESULTS AND CONCEPTS 
4. A review on image segmentation techniques, Pattern Recognition, vol: 26, Issue:9, 1993
Regards
Chandrama
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 I would use the habitat model Invest software that requests as input data a series of weights for each land use on the basis of its suitability
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This additional information is very helpful.  The distinction between land-use and land-cover classifications is significant here in the USA.
In looking at the Corine land cover codes, I see that while there are several that can easily be weighted at 0, and a few that can be weighted at 1, there are actually many that probably merit an intermediate weighting.  These intermediate ones merit such a weighting based on the probability that they include areas that might serve as habitat for heron guild as much as how suitable they tend to be for habitat.
I agree that there will need to be local input, especially regarding the actual local conditions of land that falls into the various Corine categories.
For some of the intermediate categories, I would actually suggest that instead of giving them a blanket weight you might instead buffer around the weight "1" areas where they adjoin these intermediate ones.  For example.  Instead of giving all woodlands an intermediate value, only give them a non-zero value based on their proximity to wetlands and watercourses (weight "1" areas).  One could similarly buffer at along the shorelines of large water bodies, since the shore areas are the actual habitat.
I'm giving you a straw-man starting point here for your local experts to refine.  This is a quick effort based on the nature of these land covers in the area I live.  I suspect that there are some important differences in the nature of the various land covers as they occur in the Po River valley and so some dramatic adjustments wouldn't surprise me.
I expect it will also elicit some comments here too, and that would be good.
1.1.1=0
1.1.2=0.1
1.2.1=0
1.2.2=0
1.2.3=0.1
1.2.4=0.1 (largely because they don't want birds around airports)
1.3.1=0
1.3.2=0
1.3.3=0
1.4.1=0.3
1.4.2=0.5
2.1.1=0.1
2.1.2=0.3
2.1.3=0.8
2.2.1=0.1
2.2.2=0.2
2.2.3=0.1
2.3.1=0.3
2.4.1=0.1
2.4.2=0.1
2.4.3=0.3
2.4.4=0.2
3.1.1=0.1
3.1.2=0.1
3.1.3=0.2
3.2.1=0.3
3.2.2=0.5
3.2.3=0.1
3.2.4=0.2
3.3.1=0.1
3.3.2=0
3.3.3=0.1
3.3.4=0.1
3.3.5=0
4.1.1=1
4.1.2=0.8
4.2.1=1
4.2.2=0.1
4.2.3=1
5.1.1=1
5.1.2=0.8
5.2.1=1
5.2.2=1
5.2.3=0
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Hello everyone! 
What you think about the Evidence Likelihood transformation from categorical factors (geological, soils) to use the output as continuous predictors in Species Distribution Models? 
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Evidence likelihood statistics make intuitive sense to me over and above the mainstream and Bayesian statistics I have used so far. The Achilles heel of the Bayesian paradigm seems to me the a priori probabilities required (as in attached 1).
The element of the testing of alternative hypotheses on predictors (environmental variables) based on the literature or comparing the SDM predictions with an alternative explanation (island theory) are both part of my set of SDM studies (attachment 2 and 3). These aspects I have not seen in other SDM papers and appreciate references.
However, I need a bit more background on the 'transformation' of categorical variables/predictors in this context. Concretely, I envisage  in the case of bear (attachment 2), that  the probability for a bear record in the 'beech forest' depends also on the relative coverage of this forest category within the research area. In other words, the statistical findings of an SDM depend on the delineation of the research area. An issue rarely questioned in SDMs. We have some insight in the impact of delineation for the bear study as we ran a model on the southern half of the research area (with proportionally more beech forest).  And maybe this spatial likelihood probability is inherent  in say the concept and algorithm of maxent.
Looking forward to some practical explanation on the 'transformation'.
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Dear all,
I'm running the D and I overlap test metrics adapted for Warren, 2008 - 2010 in the ENMTools. According to Warren, D have ecological significance, while I test shows the comparison as a probability of distribution. 
Have you used these metrics? how you explain the differences in the values of both tests?
Thank you very much in advance!
Ricardo
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Hi Ricardo,
The traditional D metric used for calculating the overlap/similarity of two or more species niche according to direct ecological observations/datasets (see Schoener, 1968, Ecology). Whereas, the traditional Hellinger distance metric (I) used for measuring the difference between two probability distributions (mathematical studies back to 1900s), and it was modified for ecological studies (see Warren, 2008, Evolution).
The ENMTools uses the (normalized) (modelled) suitability of occurrence of studied species in a defined space (i.e. study area) to calculate D and I metrics by comparing each grid in the study area between two species or time periods, etc.
D and I metrics (calculated from ENM) represent same ecological information. But, sure, these metrics are strictly linked to how you built distribution models. The difference between the calculated D and I is just because of their formulations. I can recommend you to read Rödder & Engler, 2011, Global Ecology and Biogeography.
Cheers,
Semra
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Am working on Spatial and temporal variability of rainfall in West coast of Karnataka using IMD gridded data of resolution 0.25 degree. Currently I did trend analysis for my study area and found drastic variation of rainfall in recent decades. I need experts suggestion to carry out further work for my research. Please help in this context. 
Thank you,
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Your question is about spatial variation of rainfall, but your further explanation is about temporal changes/ trends in the last decades. Could you please give some more information about the area and the precise changes and/ or variation in space and time you see in the data? Are you concerned about average rainfall, seasonal rainfall, monthly extremes, annual extremes?
Spatial variation of rainfall can be influenced by many factors like for instance elevation, slope, aspect and prevailing wind directions. This depends on the geographical and climatological characteristics of your study area.
Best regards,
Martijn
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I have 4 sites with a total of 22 species (i.e. site1 has 7 species, site 2 has 15, etc.). I also have multiple weeks’ species abundance data for each site. I want to analyze the species diversity on temporal and spatial scale based on high throughput sequencing.
Several methods have been proposed to compare sites for the species richness, many of which only use presence/absence data. I want to use abundance data, and do the following:
 -use entire dataset, compare the similarity statistically, and obtain an optimum species richness/diversity value (say x number of species needed to reach 95% coverage of the whole dataset)
-use subsampled dataset (time-wise and site-wise), and analyze at which stage the previously obtained optimum number of species is reached (i.e., at 2 months of sampling instead of 12 or in one site instead of 4, etc.)
 Any recommendations on the use of abundance data for answering these questions?  
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Hi all, thank you for your answers. I did use several similarity indices (i.e., sorensen, morisita etc.) to obtain somewhat comparable values between different site communities. I am basically looking at beta diversity. What I need is rather a test, that could help me compare these similarity values statistically.
Moreover, I've looked at 'species diversity versus number of individuals' graphs per site (at which point these sites saturate in their species numbers), and I have different values for each site. Based on these values, I would like to decide an optimum number of species, and hence decide which site is enough to give me that number.
This is important from bio-assessment point of view, so we don't have to sample 4 sites but maybe one site only. This is where I am stuck, and hence needed further opinions/suggestions.
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To avoid over-fitting, which variables should be removed first after preliminary MaxEnt run?
Is it those which are showing 0% contribution to the models, or those which are giving less than 0.8 AUC value when used in isolation (by examination of test jackknife data)?
Thanks
P
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I prefer using a correlation analysis, PCA and natural history of the species to delimit number of predictors. 
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I'm looking for case studies of biological observations in floodplains that could follow the Intermediate Disturbance Hypothesis. The study noted below seems to be one of the only ones, but I'm guessing there should be similar observations looking at biota.
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Dear Mauricio
Budke, J. C., Jarenkow, J. A., & de Oliveira-Filho, A. T. (2010). Intermediary disturbance increases tree diversity in riverine forest of southern Brazil. Biodiversity and Conservation, 19(8), 2371-2387.
Montero, J. C., Piedade, M. T. F., & Wittmann, F. (2014). Floristic variation across 600 km of inundation forests (Igapó) along the Negro River, Central Amazonia. Hydrobiologia, 729(1), 229-246.
Rosales, J., Blanco-Belmonte, L., & Bradley, C. (2008). Hydrogeomorphological and ecological interactions in tropical floodplains: The significance of confluence zones in the Orinoco Basin, Venezuela. Hydroecology and Ecohydrology: Past, Present and Future, 295-316.
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I am in a spatial ecology class and we are conducting marked point pattern analyses this week.  If selfing rates aren't possible I would also be interested in fruit set or flowering.
Thanks
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Dear Nathan, I'd assume that most plant species employ both self- and cross-pollination. For these species the proportion between the two pollination methods may widely change, according to the ambient conditions. Like: self pollination may be preferred under harsh condition.
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I am currently doing species distribution models using presence-only data, and I would like to perform spatial filtering of presence points in order to lower the sampling bias and to make sure that the data for building/evaluating models (I'm doing the data partition-based evaluation) are independent.
I have R function which leaves in one occurence point and removes other points in specified nearest-neihborhood distance area. However, choosing the distance within which the data will be rarefied is often arbitrary.
My concern now is how to justify or to select the distance threshold. As of now, I am using 1km threshold (duplicate points within 1km buffer are removed). My study area is ~250x50km, it is quite spatially heterogeneous, and resolution of my most coarse environmental variable is 1km (downsampled to 30 meters).
I feel that 1km threshold is somewhat adequate, but I cannot reasonabily justify this choice. Does anyone has tips on this issue or some articles to direct me towards.  One method that I've came across in the literature so far is using variogram range where the points become spatially independent, but I am still not clear on how to use my environmental variables to build the semivariogram, so any tips here would be very appreciated.
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You too should be consider the species that you want modelling. The requirements for invertebrates (eg. insects) can be very different to the requirements of plants or vertebrates (eg. birds, reptiles).The distance threshold to use in spatial filtering (in the same habitat) can vary between some of these groups according to their ecological characteristics.
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I'm trying to estimate the degree of space sharing by pairs of individuals using the adehabitatHR package. After reading up on potential overlap metrics I settled on Bhattacharyya's affinity, but I'm finding that in some cases the home range overlap of an individual with itself, is greater than 1. I'd like to understand why this is the case, so that I can determine how best to deal with the resulting matrix! Thanks in advance for any help!
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The Bhattacharyya distance is defined as the negative logarithm of the Bhattacharyya coefficient (affinity). The Bhattacharyya bound integrates over all positions in the domain and assumes that the sample belongs to only one of the two classes. This assumption is a major restriction on the scope of the method as it should strictly only be applied to simple two class problems where this is known to be the case. It is therefore not an absolute similarity measure but rather a relative separation measure.
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Hello,
I would like to ask the better choice for sampling a finite population on an area (both large enough, such as households in a political unit). I have seen some published papers and books (e.g. "Sampling Spatial Units for Agricultural Surveys") but I'm still aware of spatial dependence problem.
Any tip?
Thanks!
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Hi everyone.
I would like to know about the spatial analysis program(for Species Distribution Modelling).
I tried using the MaxEnt and ArcGIS(ArcMap 10.1). MaxEnt is pretty useful. while using this program or software, I have question. I wonder if the new spatial analysis program Like MaxEnt.
If you know new(in my case... haha) program, please recommend!!!!!
ps1. i'm not good at R. if possible, please Recommend except for R package.
Thank!! You have to be happy.
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Hi,
I've used mostly R packages, which is the best option (with packages such as ENiRG, biomod2, sdm and dismo). ENiRG has a user interface that makes it easier to use.
Outside of R I've tried these:
But I still think that using R is the better option, since you have a lot more control over your models and you can combine resources from several packages.
Cheers,
Frederico
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How can I fix a leak from the pre-column side that is connected to injector?
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Thank you!
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what's the 'midpoint' latitude really meaning?  
it is not very clear how to calcualte the midpoint of a species' latitude and longgitude when I try to pull together geography information from GBIF for species trait data. Generally, there are many distribution location records for one species, and only one latitude or longitude is needed in order to test effect of  latitudial gradients on certain species' variable, i.e., plant height.  sometimes I saw midpoint latitude was use in some paper but the methods was vague. centric or mean?
it seems there are risk to use midpionts in stead of exact site geographic locations, however, it is the only options in some circumstance, as sometimes the site location information is unavailable for many species in a compiled dataset.
any better way or comment? thanks.
Jingming Zheng
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Hi Dr. Zheng,
In my opinion, you can use species' latitudinal midpoints to distinguish them on broad spatial scale, e.g. polar vs temperate vs tropical. But it doesn't say anything about the width of the said distributions. While 2 species can have similar midpoints, one may present a narrow range whereas the other is ubiquist. So it is as important to determine the latitudinal width of the species ranges.
Technically speaking, just add the lowest latitude to the highest one and divide by 2.
You should also use percentile distribution to avoid using the most extreme records. And assessing the shape of the latitude data distribution might also be necessary, depending on how many points your species have.
Instead of midpoint, you should also try the median, but some studies have identified the midpoint as more robust (i.e. less sensitive to sampling effort and distribution data).
Best,
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When adding the maximum background points(30.000) to the presence points (99.865) they don’t add up to the number of points used to determine the Maxent distribution (161.268 used  when it should be 129.865). Or is it that the extra points are for about 20.468 different background points in each of my 3 crossvalidations? In this case why such a low number when my file as a few millions background points to sample from? Does Maxent decide that even if you tell it to use a max of 30.000 background points it doesn’t need that many?
The follow settings were used during the run:
66576 presence records used for training, 33289 for testing; 161268 points used to determine the Maxent distribution (background points and presence points); maximumbackground: 30000; replicates: 3
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In resume here are apparently the number of points used to determine the Maxent distribution (background points and presence points):
 1 covariate&10 replicates:46373 each run
1 covariate&no replicate:46373
7 covariates&10 replicates:135727 each run
7 covariates&no replicate:145651
I fell to see the patern knowing that the total number of presence points is 99518 and that the total number of background points is 46373 although I ask for a max of 30.000 but wonder if Maxent listen if you use background csv file instead of grid (maybe the option of max background point is just for grid file)?
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I am struggling conceptually with how I can best model my dataset. My data is the abundance of spiders collected from 20 permanent traps in a forested area. These traps have been repeatedly re-sampled every summer for ~15 years.
I have two sets of explanatory variables that I wish to model. The first is climatic data, namely rainfall and temperature. These have been obtained for each sampling year, so they are the same for all permanent traps. The second is geophysical data, including slope, elevation, aspect etc. These have been obtained through an online terrain model, so are unique to each permanent trap, but are obviously the same for each sampling year.
I'm trying to understand conceptually how to take these different scales of explanatory variables into account. Climate is regional, top-down whereas geophysical is local and bottom-up. I am after a relatively simple procedure if possible.
Any suggestions would be greatly appreciated.
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To me it seems a fairly simple mixed model would do the job here:
Spider abundance as a function of Rain + Temp + Geophys data and a random factor Trap ID. You can include interaction between the two types of explanatory variables.
Since each trap is presumably sampled just once in each year, I don't see why Year would need to be a random effect. A year effect would be the same for all traps. It's the traps that really are sampled repeatedly and Trap ID would therefore be included as a random effect. If traps are sampled multiply in each year, you could include Year as a random effect as well, with Trap ID nested within it.
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Hi all,
My issue is that I am getting AUC values higher for a one explanatory variable model than for a multi explanatory variable model (that include the one from previous model). As my supervisor and I believe Maxent use all the info from the best variable and so adding variable could only improve the model, I have trouble to explain and this postpones a bit my thesis defense.
Could you give me an explanation here on how Maxent could allow such thing,
Thanks
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Adding a variable does not necessarily have to improve a model. This can be due to lack of sufficient data to learn the extra parameters your model is estimating. But, it can be also that you are in a problem where data is very noisy. Realize that the data is not reality, it is an observation of it and it has its own measurement error. Hope is useful. It not necessarily means the variable you are adding is not important for a mechanistic point of view. There can be additional issues like that this extra variable is meaningful at different spatial or temporal scales your model is not considering. An example is if the timing of spawning during the year is important, but your model forecast annual values using annual variables.
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It is suggested for reducing over-fitting of models, the variables should not be auto-correlated.
When each variable was tested through SAM module, it gave individual values of Moran's I which ranged from 0.2-0.6.
The question is how to know that that the variable has spatial auto-correlation?
Does Moran's I value of 0.2 with P level of 0.001 indicate auto-correlation?
and what is the cutoff limit for the value of Moran's I to select less or non auto-correlated variables for model development?
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Hi Prakash Pradhan jee,
if you are looking for Moran's I test in spatial statistics through ARCGIS then there is a separate method where and how to handle this issue. If you are handing  community data then I recommend you have a look vif (variance inflation factor) in vegan package. If you are working with multiple regression I suggest you have a look at step function and AIC values in glmm package.
Goodluck.
Chitra
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I’m looking for an index/metric that will permit to quantify the contrasting spatial distribution of a cover A (which is located mostly in the south of my study area) vs. a cover B (which is well distributed over the study area). I know that Fragstats can do that but I dont have the spatial analysis extension in my computer. Thank you for your answers !!
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Dear Yan,
Fragstats is a stand-alone program so you don't need any extension. You can calculate metrics also with R, by using the SDMTools package and the ClassStat function!
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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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What is the basis for a single species to occur in physiographically distinct landscapes (like montane and coastal).
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Clearly - in those cases - climate is not the primary driver of distribution. Other, proximate deterministic factors may be more important. Or, randomness (past dispersal events). There is always the possibility too that there are local genetic adaptations to either environment, as yet undetected. Such disjuncts offer interesting opportunities to examine what shapes a distribution. For example, the wood frog of North America has primarily a montane distribution in the southern end of its range (and in the North goes all the way to Alaska). But, in the south there is a documented population, disjunct, on the Atlantic coastal plain. Why?
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Spatial prioritization for species recovery programmes needs to be evidence based, and it may be better to target effort in certain parts of a species rangeB but I'm struggling to find examples where interventions have been applied with good spatial replication across a species range. Agri-environment schemes perhaps provide the best opportunity for such studies. Maybe there are other interventions applied across big areas, that have been monitored and assessed (both the level of intervention and the animal/plant population response).
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Thanks. I am interested in this issue as I'm currently looking at spatial prioritization for single species recovery programmes, to inform conservation policy. There are several spatial questions related to this - for example is it better to direct resources in a species core or edge of range? I have found very few tests of conservation interventions applied right across a species range (i.e. covering a theoretical, or measured, core and edge).
I am also interested in examples of where a conservation intervention has been applied on the edge or outside a current range but where the dispersal ecology of the species, or range shift under climatic change, means colonisation or sustained population persistence is unlikely (i.e. where resources have been directed with little prospect of success).
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I would like to analyze data from different LTER sites in order to evaluate the effect of climate change on different ecosystems. The problem is that each site collected data with different study design and with different response variables (diversity of different taxa, variations in snow cover, biogeochemical cycles, ecc.).
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Cross-site analyses of LTER data can be difficult. LTER investigations are aimed at understanding a certain system, and thus focus on differing questions and differing methodologies. This has been a source of criticism levied toward LTER with respect to answering regional to global scale questions and is at least part of the argument for NEON.
The difficulty here is deciding whether you are comparing apples to apples: Are the variables measuring the same thing and thus comparable. It would probably be helpful to know what you are planning. Is there a specific question you have in mind? You might be able to select a subset of LTERs that have certain types of data that would be appropriate for answer a focused question. For example, if you were focused on soil water chemistry, there are 27 soil lysimeter studies in the LTER network at nine or ten sites (based on a quick search of the LTER Network Data Portal). If you just want to look for interesting patterns, I would be skeptical of the utility of cross-site comparisons of LTER to show you much.
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I am working on spatial modelling of Himalayan terrain in connection to Climate Change and am required to make a spatial model and a decision support system. So what projection system I should use to get the best results am looking for habitat changes in fauna and flora as a whole.
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Arun, "best" entirely depends on what geometric characteristics you need to maintain for your analysis. Conformality (maintenance of local angles of bearing) and equivalence (maintenance of relative area) are mutually exclusive properties for any map projection, so you must choose one, or a compromise. At 1:50,000 you're pretty large scale (i.e., zoomed in), so you can go with something that compromises area somewhat safely - as long as exact areas aren't crucial to your analysis! The Himalayan area being wider than it is tall, a conic projection, say the Lambert Conformal Conic, sounds good, as long as you've chosen the central meridian and the two standard parallels well for the region (some info on that projection here: http://resources.arcgis.com/en/help/main/10.1/index.html#//003r00000034000000).
The main thing is to be aware of the certain kinds of geometric distortion different projections introduce, and to choose a projection such that you avoid or minimize distortion that would introduce error into your analysis. For example, if direction (i.e., azimuthality) is important to your analysis, you'd need to be sure your map maintains that property. Also, many projections are named for properties they maintain, but they may not maintain that property everywhere. For instance, an azimuthal map typically maintains direction from one point to all other points, but a line drawn between two points that don't include that one point will not have the correct, real-Earth azimuth.
The best discussion about projections in general I've ever come across is Chapter 3 of the GeoCart software users manual, essentially ten pages of map projection education. I highly recommend reading it: http://www.mapthematics.com/Downloads/Geocart_Manual.pdf
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I have created polygon shapefiles of vegetation patches (attached) and I would like to analyze them on the degree of clustering, orientation, directionality, etc. in comparison to a null model of randomly distributed patches. I am having difficulty finding software or scripts that will work for such an analysis (most specifically focus on point pattern analyses). I have tried 'spatstat' in R, but I am having no luck. Does anyone know of any tools that could help me? I would prefer any that may be free/open source, or can be used in common statistical programs (R, JMP, SPSS, etc.)... the more accessible the better!
Thanks!
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I used ER Mapper to have statistics of pre-arranged polygons (average in multispectral space, medians, covariance matrices) as well as to do cluster analysis based on these stats. If I can be at your help, don't hesitate to contact me.
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When you run a simple mantel test, do the distance matrices have to be in the same order and to correspond the rows to the same individual or register?
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Not sure I fully understand your question, but almost all spatial analysis is going to require that you keep track CONSISTENTLY of the entities. So I'd say yes, you need to preserve the ordering. In fact, one of the ways that we might do a simple simulation test, would be to compute for the OBSERVED labels, and then scramble them (say reorder them 100 times) and see how DIFFERENT the observed value is from the distribution of values from the variations? Maybe this does not make sense from my poor explanation, but take a look at  a book like EXPLORATORY SPATIAL DATA ANALYSIS by Bailey and Gatrell. I think it does a good job on the idea of simulating random variations on the observed data.
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I am working on a study of the spatial distribution of leprosy in an armadillo population. We have point locations for each captured individual and a binary measurement of whether each individual did or did not test positive for leprosy. This sort of marked point pattern is a form of univariate labeled data and I want to do a network (cross) Ripley's K analysis with a conditional randomization test, where the point locations are held constant and the mark (leprosy: yes or no) is permuted among the locations (rather than permuting the locations themselves). I have tried both SANET and GeoDaNet, which do a network version of a cross K analysis, but the randomization scheme seems to be for true bivariate data (not univariate labeled data).
Does anyone know of software that can do a "conditional randomization" test for network (cross) Ripley's K? Our software (PASSaGE 2) can do 1D, 2D, or 3D Ripley's K with this sort of permutation scheme, but not network K.
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Hello!
Yes, you can do network functions with linearK() for one dimension. And LinearKcross() for two dimensional Ripley.  Now, in order to calculate envelopes in a way that the geographic position is fixed, but the labels (sick, not-sick, or male, Female) are randomly shifted among individuals you need to include an option in the envelope command:
envelope(..., simulate=expression(rlabel(X)))  
where X is the ppp object (which has labels).  In this expression rlabel() randomly assigns one of the labels to the points.  That way, the position of the points is kept but the labels are shifted. I think this is what you want.
Hope this help
eric,
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I explored species association using survey plot data in Tibetan Plateau, the highest plateau in world, where very unique herbaceous communities occurred. However, I found the number of associated pairs of species fluctuated based on a four-year interval data. Could I assume that the slow-growth community remain stable or equilibrium so that their association keeps stable too?
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thanks for the quick feedback, I will start from a simple model first.  although there are some papers in recent ecology letters in this regard, ideas from early papers is much easy to understand in this case.
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I need suggestions / help to the identify suitable model for spatial patterns and trends of forest succession. I have 5 land cover layers with an increasing trend of forest. Which Spatial – Temporal model is suitable? I want to use at least 4 layers for model input. I am confused by which one is better, GEOMOD, CA-Markov, ANN or another. Please help.
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Take a look at the following article. I think that methods based on full mapping of individuals could better suit your case.
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Does anyone have a good source for correcting for spatial auto correlation when comparing a species assemblage (site X species matrix) to a geographic distance matrix? I know a mantel test will tell me how correlated the variables are but how do you correct for this effect in subsequent analyses? 
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Hi Israel,
To control for the effects of spatial autocorrelation, you can use spatial eigenvector mapping (SEVM). This approach removes all significant autocorrelation from the residuals of your model. Have a look at Griffith & Peres-Neto (2006) Spatial modeling in ecology: the flexibility of eigenfunction spatial analyses. Ecology 87, 2603–2613.
Good luck
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Given only animal relocation data from a telemetry study and no corresponding mark release recapture records is it possible to derive a density estimate using a mark release recapture model.  I suspect there is a way to do this treating telemetry encounters as resighting events but I am not certain so I thought I would ask.  If some of you can point me toward the relevant papers it would be greatly appreciated.
PAZ
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I don't think you can do it, unless you know what is the proportion of animals you have telemetry data on. Otherwise, there is no information on resights of a sample of tagged animals on the proportion of untagged animals, and hence you can't get density/abundance. At best, if you have a large and representative sample of animals for which you have telemetry, you can construct a RELATIVE density surface, which essentially would represent habitat use (assuming you get locations  independently of habitat, which might or not be true!). But you have no information on the telemetry data alone to inform about the height of said function. I believe the references provided by @Rishi Kumar Sharma are only useful if you have BOTH telemetry data and additional information (e.g. captures and recaptures of both telemetry and non telemetry animals), which is clear form the wording in the title ("....radiotelemetry and replicated mark-resight techniques..." and "....radiotelemetry and replicated mark-resight techniques...") 
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I'm working with MAXENT and GARP in species distribution modeling (plant species). I've converted my raster files (environmental variables) to ascii ( I work with Arcmap 9.3 for converting) and then used them in MAXENT but now I don't know how convert them for GARP.
Thanks
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Hello Ali, I´m glad to hear you that your analysis works fine.
The outputs represents every run in your GARP analysis, a simple and robust way to get a better (and a few representative results) result, is better turn on the "best subset options".  When you have only the best subset results (for example 15 results of 100 runs) you can make a map algebra and make an average map for the potential distribution of the specie. Several authors state that OpenModeller outputs are different of the DesktopGarp software because OpenModeller don´t allow you to modify all the relevant options in the algorithm, this is commonly called as "Tailored software", please use the OpenModeller platform only if you can´t do it in DesktopGARP.
If you have any trouble please don´t hesitate to ask, best regards from Colombia.
Luís Enrique.
PD: In order to get good results in your potential distribution modeling, remember that you need a good quantity of points (about 80, there are no a magical number) for the best subset analysis because you use a dataset of points to create and test the GARP models, and another dataset of points for select the best subset models. 
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I have mean-variance plots of annual NDVI from different data sets. I would like to compare the trajectories of these plots, but not sure what would be the best way to do it. Any suggestions are welcome.
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Hallo Yolandi,
There are many ways of doing this. It would be fun working through them to see what they revealed !
One is to calculate the euclidian distance between the pair of points for each year. That is, take the point for, say, 2005 in plot a and that for 2005 in plot b.  Then calculate the distance beween these - by basic trigonometry.  If the trajectories were identical then you'd expect the distances to all be zero.  In practice they'd be a distribution of values around a mean. You can then test whether the mean obtained is significantly different from zero. (I would do this by repeatedly randomizing the plot b values against a stationary column of the a values and each time calculate the distances and their mean.)
Another possibility is to calculate similarity values between all pairs of points (that is beween the point for year 2005 in plot a and that for the same year in plot b). You'd use the mean and variance values for each year as the basic data. (I would suggest that you might also include kurtosis and skew of the distributions of NDVI values. All these are essentially multivariate measures of the characteristics of the NDVI values for each year in the two localities.
Once you have the similarity values you can try techniques such as clustering to see whether the values for plot a cluster separately from those for plot b.  If there's no separation the trajectories are the same.
There's many more possibilities !  best wishes, Andrew
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Currents and other hydrodynamics / hydrological phenomenon can induce spatial expansion of a bloom (such as visible in upwelling areas).
I would like to know if abundance of cells can also induce a spatial expansion? As though phytoplankton needed "more space" when cells are more numerous?
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Short answer is yes. It's simple physics of diffusion. When cell abundance continues to increase through growth, the bloom will spread out. This will happen with or without water movement, and with or without cell motility. You can do a simple experiment to demonsrate that: Add an aliquot of dense phytoplankton very gently to a container of water without any stirring. Observe over time how that patch of phytoplankton spreads out.
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The model I'm referring to utilizes the Markovian dependence of animal sign detection. Any opinions? Any pros or cons? Perhaps an alternative model?
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Sorry, I didn't finished my answer. For ussing occupancy analysis you can use several programs
Or you can use library "unmarked" within R or bayesian approaches with Bugs or jags also within R.
You just need to see which kind of data you have and which constrains you need, and then see if it is implemented in Presence or unmarked or try to implement it with Bugs or jags
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Any tips on the best way to get sub-meter GPS coordinates while on the ground in Bolivia?
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Thanks. Your tip for establishing a base station and post-processing is a good one. I'll do that.
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We are planning a study on roe deer migrations and habitat utilization and we think that GPS tracking of animals could be a part of this study. Since we have no experience in this field, could you please give recommendations on the set of equipment necessary to organize this work, and maybe any comments on the companies that sell such equipment?
Or maybe you know somebody who wants to sell used collars? Or somebody who owns the collars and would like to do research in western Siberia?
Any comments are appreciated! Thanks advance!
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Hello!
I did a monitoring of roe deer in Switzerland and we also wanted to know how they use the territory. We divided the area in equal squares and we used cam traps that we placed on deer tracks in each square (1 perpendiculary to the path, one facing it). We got pictures of the animals and we have been able to identify all the males. Then, we wanted to use a model to know how big the pop was according to the number of males, but we don't know how to use CAPTURE...!
Thanks to the pictures, we have been anyway able to tell in front of how many cam one individual was observed, so to determine its homerange and the frequency of the encounters.
If I can be of any other help, just ask!
Emilie
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A coworker is working on a biodiversity habitat model with a minimum grid size of 30 x 30m. Basically, I am curious to know what the minimum habitat size a coot needs is and if the habitat is suitable to successfully reproduce since I am unable locate a definite number within the literature.
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Larger open lakes will still be used, certainly by the Red-knob Coot, but only if they have some shorelines with suitable vegetation. Rather than setting a min or max size or trying to use P/A ratios, it would be better to just take out the central pixels (e.g., reclassify all LC pixels classified as water for which all 8 nearest neighbours are also water). The only challenge here would be that they would still use small vegetated islands. As suggested previously, for min size I would think they would use any body of water that shows up as water in a LCC map at 30x30m resolution. Also, you might have a look at some of the work on modelling mosquito habitat with remote sensing - it's a very similar problem - there are other tools and freely available RS data sets that you could use to get a better estimate than the GAP data are capable of.
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I'm doing research using shape metrics of islands and I'm trying to measure their length. However, some islands have rather odd shapes and I'm not sure what their length mights be.
Here is a link, just for example, on a map with three of the researched islands
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Hi!
I had to measure the diameter of dolines for my thesis and I think for measuring any areal landform one could use the same methods. So I recommend this paper: Bondesan, A., Meneghel, M., Sauro, U., 1992: Morphometric Analysis of Dolines, International Journal of Speleology 21, 1-55 (http://scholarcommons.usf.edu/ijs/vol21/iss1/1/).
It provides the guiding principles for measuring length, diameter, perimeter, area etc. of various shapes including V shape (of Molat island).
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I have a spreadsheet with the number of presence records of my study specie, sampled in different quadrants, of which I have the geographic coordinates of latitude and longitude. Whats the most appropriate way to analyze this data?
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This looks like a question that can be answered with spatial autocorrelation calculation. This can be done in ArcGIS, you can find detailed help here: http://resources.arcgis.com/en/help/main/10.1/index.html#/How_Spatial_Autocorrelation_Global_Moran_s_I_works/005p0000000t000000/
Best regards
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Does anyone know about courses of spatial ecology, landscape genetics, seascape ecology scheduled for this year? With application period still open.
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Did you see, the uicn courses?
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I couldn't find any clear facts about the disambiguation between these two fields.
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Dear Neven, you can read
Landscape Ecology: The Effect of Pattern on Process
Monica Goigel Turner
Page 171 of 171-197
Annual review of ecology and systematics, 1989
or one from 2005
Landscape ecology: what is the state of the science?
MG Turner -
Annual Review of Ecology, Evolution, and Systematics, 2005
And
Spatial ecology and conservation of seabirds facing global climate change: a review
D Grémillet, T Boulinier
Marine Ecology Progress Series, 2009
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Hump shape distribution of biodiversity is the most common in the mountains. The mid-domain effect (only relatively simple model which can produce such a pattern) seems not as a driver of that pattern as it has no biological meaning. What about other hypothesis?
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Levan,
I'd say that without exploring a number of factors, including sampling, geometric constraints, species area relationships, biogeographical history, and both phylogenetic and functional diversity, you cannot really have an understanding of the mechanisms underlying the richness pattern in your data. Just looking at species richness is insufficient, in my opinion, to say very much about the relative importance of competition, environmental filtering, convergent evolution, or niche conservatism has on the patterns in richness, though there is much research out there that tries to do just that. Christie McCain's meta-analysis "Global analysis of bird elevational diversity" (2009) describes four of the most commonly occurring trends in richness along a gradient (for birds): 1. decreasing, 2. low plateau 3. low plateau with mid peak, and 4. mid peak. Within her paper are numerous citations of papers that have found these different richness patterns. For a paper that addresses species richness, functional diversity, and phylogenetic diversity along a gradient, check out Devictor et al. 2010. Spatial mismatch and congruence between taxonomic, phylogenetic and functional diversity: the need for integrative conservation strategies in a changing world.
Best,
Kevin
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I need some articles related to this topic and some suggestions to analyze these data together.
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Hi Patric. Thanks so much!
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Suppose we want to assess the effect of a given predictor variable on the given response variable. In case observations are dispersed in space it is recommended to check for spatial autocorrelation. But positive autocorrelation between points normally means that the values of the predictor are similar, and lack of correlation will be related to significant variation in predictor variables. Then, it is worth searching mainly for cases of negative spatial autocorrelation, when we fail to observe the effect of the predictor. Is this correct?
Can anybody give an example of empirical studies illustrating the effect of positive autocorrelation not related to variation in predictor variable?
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I think what your are getting at is that you can have spatial autocorrelation for some variables and not for others, depending on the spatial scale in question. So, for example, imagine your response was tree height and one of your predictors was the slope of the terrain where the trees were growing. If you sampled in an area that is relatively flat with regularly spaced ridges, you might see a pattern of spatial autocorrelation between the slope predictor at the scale of the distance between ridges. Similarly, you may see spatial autocorrelation between the response, tree height, in the flat areas at a smaller spatial scale. You could resample your data to remove the spatial autocorrelation based on a semivariogram. Semivariiograms can be easily calculated in R using the "variog" commend in the "geoR" package.
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I've done some looking on line, so far without success. For big or repetitive processing tasks that need spatial stats, it would be really useful...
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The attached figure is a mark correlation function produced by the spatstat package in R. The summary returned from the object used to produce this figure is as follows:
r theo trans iso
Min. : 0.00 Min. :1 Min. :0.1663 Min. :0.1678
1st Qu.:15.27 1st Qu.:1 1st Qu.:0.1759 1st Qu.:0.1765
Median :30.53 Median :1 Median :0.5172 Median :0.4858
Mean :30.53 Mean :1 Mean :0.5695 Mean :0.5652
3rd Qu.:45.80 3rd Qu.:1 3rd Qu.:0.9435 3rd Qu.:0.9286
Max. :61.06 Max. :1 Max. :1.1325 Max. :1.2666
When I enter the name of the object on its own, I get the following:
lo k[mm][lo](r) lower pointwise envelope of k[mm](r) from simulations
hi k[mm][hi](r) upper pointwise envelope of k[mm](r) from simulations
I would like to return the actual values, as in the former output, for the simulations labelled "lo" and "hi" in the latter output.
In other words, I would like to be able to obtain a range of values at which the observed (black) line falls outside the grey envelope.
Is this possible?
I get the impression it may have something to do with the argument "savefuns", but I am at a loss to know how to implement it and return the values I need.
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hi Tom,
I hope the following code might be of some help to you.
###R-code###
library(spatstat)
X <- simdat
Xenv<-envelope(X, Kest)
plot(Xenv)
min(Xenv$lo)
max(Xenv$hi)
range(Xenv$lo)
range(Xenv$hi)
###END R-code###
best wishes
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When analysing relationships between two spatially autocorrelated variables using linear regression (with ordinary least squares), one has to be aware of the inflated Type I error - you are more likely to get significant results even if you should not (e.g. Legendre 1993). On the other side, it seems that regression coefficients are not biased, so to estimate the slope of regression is safe. But how is it with explained variation in this regression, i.e. R2? Is it also inflated, similarly to Type I error rate? I read the paper of Lennon (2000) in Ecography, pointing out that R2 will increase with increasing spatial autocorrelation of one of the variables. But I can’t find other references dealing with this issue (there are plenty dealing with Type I error rate and shifts in regression or correlation coefficients, but I am not aware of other one dealing with R2). Can somebody please help me with that?
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Hi David,
Firstly thanks for this question since I have been trying to deal with this problem myslef. I have to agree with Luis and the only way to deal with this is to use a geographicically weighted regression model. I found that the easiest way to get a handle on this was to download the freeware Spatial Analysis for Macroecology (SAM) - it is really user friendly, contains many different types spatial anlayses, explorative and diagnostic tools and an easy to follow manual. I ended up using generalised least squares, which is like OLS but the spatial structure of the data are described by a spatial autocorrelation function.
Hope this helps.
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We want to select a fixed number of deer relocation, coming from collared animals, to sample the vegetation.
1- Do the sampled locations need to be spatially independent? Temporally independent? Spatio-temporally independent?
2- If so, how do you test for the independance. We were currently exploring autocorrelation indices (Moran's I) or spatio-temporal indices (Griffith's).
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In their paper Kaczensky et al. (2008, Resource selection by sympatric wild equids in the Monglian Gobi) dealt with temporal autocorrelation of GPS data in a different way: all locations separated by a time interval lower than 72h were given a weight less than 1 based on a specific power law. You may want to have a look at this. Good luck!
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Among the methods available to select spatial filters (PCNM) which take spatial autocorrelation into account, which one is the most effective? Is there a consensus on this subject?
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There was some discussion related to this topic on the following thread
I don't think there is consensus since spatial autocorrelation can take many forms (eg. different scales simultaneously). Maybe you could clarify your question with specific examples of the dataset?
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The general "nursery habitat" paradigm describes habitats that are distinct to juveniles (because they provide protection from predation, have ample food for lower trophic levels, and suitable environmental conditions). These habitats are used until individuals attain a certain size/age, and then movement to sub-adult/adult habitats occur.
It appears, however, that a single "nursery habitat" definition for a species is appropriate, as species-habitat relationships can change with ontogeny, multiple times even within the first year of life for an estuarine-utilizing fish.
Furey, N.B., Rooker, J.R., 2013. Spatial and temporal shifts in suitable habitat of juvenile southern flounder (Paralichthys lethostigma). Journal of Sea Research 76, 161–169.
What factors about a species life history or its environment mediate or influence how quickly species-habitat changes occur? Would we expect these changes to occur at specific stages, or more across a gradient (gradually)? And if species-habitat relationships are dynamic or even fluid when an organism is a juvenile, how does this impact how we manage habitats? Is this another case of focusing on landscape-based processes rather than habitat-scale? How does this impact the notion of Essential Fish Habitat?
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Hi Nathan,
Presumably, life-history strategy is going to play the biggest role in how fast juveniles leave the nursery areas. If your lifespan is 3 years, then you want to grow up, get out and start breeding asap! For long-lived species, time is not such an issue, so staying safe and taking advantage of a good food supply would be beneficial. There might be a publication or two on lemon shark use of nursery areas in Bimini, Bahamas which could help you out. Seasonality might also play a role in determining nursery area use. If juvenile fish use the weedy edges of ponds to stay safe from predators, but these edges freeze over in winter, then they are forced into deeper water and out of the nursery area. Density-dependence could also play a role. If many juveniles are in a specific nursery habitat, interference competition could limit the usefulness of over-staying.
Essential fish habitat SHOULD account for these factors, but in general, it does not and speaks to the need for buffer zones to mitigate behavioural changes associated with ontogenetic shifts.
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e.g. when different scales for estimating plant cover have been used to assess plant species abundance in different experiments in the field, but then for reasons of wanting to compare across experiments, one would like to transform one scale to another (eg. modified Braun Blanquet to modified Londo)?
The different scales were used in different experiments since the main questions being asked required slightly different levels of precision, but now it would still be nice to be able to compare cover values across experiments.
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Hi Vicky! I agree with Flavia in transforming the cover classes into percentage values. Best option to do statistics! Additionally, to ensure comparability I would transform all cover values into percentage values of the least best resolution. As I understand, in your case that would mean that you would also have to transform the Londo cover values into Braun-Blanquet "percentage values".
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If I want to execute null model analysis by using R statistical software to understand deterministic community assembly pattern, then from which package could I get best outcome?
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