date:20190111

Re: [R-sig-eco] standard deviation error for EcoTest.sample

2019-01-11 Thread Jari Oksanen

No, you do not need to be worried about that warning. You have rarefaction up 
to the observed community, and that is always constant, hence you get the 
warning of zero standard deviation. That should be fixed, but I’m too lazy. 
Contributions welcome in vegan at https://github.com/vegandevs/vegan/.

cheers, Jari Oksanen

On 11 Jan 2019, at 20:09, Charlotte Reemts 
mailto:cree...@tnc.org>> wrote:

I am using EcoTest.sample (rareNMtests package) to compare rarefaction curves 
for 19 vegetation plots on two soil types (alluvial and canyon). The code below 
produces the following warning (more than 50 times): "In cor(x > 0) : the 
standard deviation is zero".
The test still produces all the expected output. I tried sorting the dataset by 
SiteType, but still got the error. Curiously, sorting the data produced a 
significant difference between the site types (p around 0.3), while there was 
no difference between the unsorted data (p around 0.09).
Should I be concerned about the warnings? Why does sorting the dataset make a 
difference?

Thanks,
Charlotte

rawdata<-read.table(text="Plot  SiteTypesp1 sp2 sp3 sp4 sp5 sp6 sp7 sp8 sp9 
sp10sp11sp12sp13sp14sp15sp16sp17sp18sp19
sp20sp21sp22sp23sp24sp25sp26sp27sp28sp29
sp30sp31sp32sp33sp34sp35
2   canyon  1   0   1   0   1   1   0   1   0   0   1   0   0   0   1   0   0   
0   0   0   1   0   0   0   0   0   0   0   0   0   1   0   1   0   0
3   alluvial1   0   0   0   0   1   1   1   0   0   0   0   0   0   1   0   
0   1   0   0   0   0   0   0   0   0   0   0   0   1   0   0   1   0   0
5   alluvial1   0   0   0   0   0   0   1   1   0   0   0   0   1   1   0   
0   1   0   0   0   0   0   0   0   1   0   0   0   0   0   0   1   0   0
6   alluvial1   0   0   0   0   1   0   0   0   0   0   0   0   0   1   0   
0   0   1   0   1   1   0   0   0   1   0   0   0   0   0   0   1   0   0
7   alluvial1   0   0   1   1   0   0   0   0   0   0   0   0   1   0   0   
0   0   0   0   0   0   0   0   0   1   0   0   0   0   0   0   1   0   0
8   alluvial1   0   1   0   0   0   0   0   0   1   0   0   0   0   1   0   
0   0   0   1   0   1   0   0   0   0   0   0   0   0   1   0   1   0   0
10  alluvial1   0   1   0   0   1   0   0   0   0   0   1   0   0   0   0   
0   0   1   0   0   1   0   0   0   0   0   0   1   0   1   1   1   0   0
11  canyon  1   1   0   0   0   1   0   0   0   0   0   0   0   1   1   0   0   
0   0   0   1   0   1   0   0   0   1   0   1   0   0   0   1   0   0
12  canyon  0   0   0   0   0   0   0   0   0   0   0   0   0   1   0   0   0   
0   0   0   0   1   0   0   0   0   0   0   0   0   0   0   0   0   0
13  canyon  1   0   0   0   0   1   0   0   0   0   0   0   0   0   1   0   0   
0   0   0   1   0   0   1   0   0   0   0   0   1   0   0   0   0   0
14  canyon  1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   
0   0   0   0   1   0   0   0   0   0   0   0   0   0   0   0   0   0
15  canyon  1   0   0   0   0   1   0   0   0   0   0   0   0   0   0   0   0   
0   0   0   1   0   0   0   0   0   0   0   1   1   0   0   0   0   0
16  canyon  1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   
0   0   0   0   1   0   0   0   0   0   0   0   0   0   0   0   0   0
17  canyon  1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   
0   0   0   0   1   0   0   1   0   0   0   0   0   0   0   0   0   0
18  canyon  1   0   0   0   0   0   0   0   0   0   0   0   0   0   1   1   1   
0   0   0   0   0   0   0   0   0   0   0   0   1   0   0   1   0   0
19  canyon  1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   
0   0   0   0   1   0   1   0   0   0   0   0   0   0   0   0   1   0
20  canyon  1   0   0   0   0   1   0   0   0   0   0   0   0   1   1   0   0   
0   0   0   0   1   0   0   0   0   0   0   0   0   1   0   0   0   1
22  alluvial1   0   0   0   0   1   0   0   0   1   0   0   1   0   1   0   
0   0   0   0   0   1   0   1   1   0   0   1   0   1   0   0   1   0   0
23  alluvial1   0   0   0   0   0   0   0   0   0   0   0   1   0   0   0   
0   0   1   0   0   1   0   0   1   0   0   0   0   0   1   0   0   0   0
", header=T)

data<-rawdata[,-1]
rownames(data)<-rawdata[,1]

library(rareNMtests)
test.data<-EcoTest.sample(data[,-1], by=data$SiteType, MARGIN=1, trace=F)  
#error message and no significant difference

data2<- data[do.call(order, data),]
test.data2<-EcoTest.sample(data2[,-1], by=data2$SiteType, MARGIN=1, trace=F)  
#error message and significant difference



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[R-sig-eco] standard deviation error for EcoTest.sample

2019-01-11 Thread Charlotte Reemts

I am using EcoTest.sample (rareNMtests package) to compare rarefaction curves 
for 19 vegetation plots on two soil types (alluvial and canyon). The code below 
produces the following warning (more than 50 times): "In cor(x > 0) : the 
standard deviation is zero".
The test still produces all the expected output. I tried sorting the dataset by 
SiteType, but still got the error. Curiously, sorting the data produced a 
significant difference between the site types (p around 0.3), while there was 
no difference between the unsorted data (p around 0.09).
Should I be concerned about the warnings? Why does sorting the dataset make a 
difference?

Thanks,
Charlotte

rawdata<-read.table(text="Plot  SiteTypesp1 sp2 sp3 sp4 sp5 sp6 sp7 sp8 sp9 
sp10sp11sp12sp13sp14sp15sp16sp17sp18sp19
sp20sp21sp22sp23sp24sp25sp26sp27sp28sp29
sp30sp31sp32sp33sp34sp35
2   canyon  1   0   1   0   1   1   0   1   0   0   1   0   0   0   1   0   0   
0   0   0   1   0   0   0   0   0   0   0   0   0   1   0   1   0   0
3   alluvial1   0   0   0   0   1   1   1   0   0   0   0   0   0   1   0   
0   1   0   0   0   0   0   0   0   0   0   0   0   1   0   0   1   0   0
5   alluvial1   0   0   0   0   0   0   1   1   0   0   0   0   1   1   0   
0   1   0   0   0   0   0   0   0   1   0   0   0   0   0   0   1   0   0
6   alluvial1   0   0   0   0   1   0   0   0   0   0   0   0   0   1   0   
0   0   1   0   1   1   0   0   0   1   0   0   0   0   0   0   1   0   0
7   alluvial1   0   0   1   1   0   0   0   0   0   0   0   0   1   0   0   
0   0   0   0   0   0   0   0   0   1   0   0   0   0   0   0   1   0   0
8   alluvial1   0   1   0   0   0   0   0   0   1   0   0   0   0   1   0   
0   0   0   1   0   1   0   0   0   0   0   0   0   0   1   0   1   0   0
10  alluvial1   0   1   0   0   1   0   0   0   0   0   1   0   0   0   0   
0   0   1   0   0   1   0   0   0   0   0   0   1   0   1   1   1   0   0
11  canyon  1   1   0   0   0   1   0   0   0   0   0   0   0   1   1   0   0   
0   0   0   1   0   1   0   0   0   1   0   1   0   0   0   1   0   0
12  canyon  0   0   0   0   0   0   0   0   0   0   0   0   0   1   0   0   0   
0   0   0   0   1   0   0   0   0   0   0   0   0   0   0   0   0   0
13  canyon  1   0   0   0   0   1   0   0   0   0   0   0   0   0   1   0   0   
0   0   0   1   0   0   1   0   0   0   0   0   1   0   0   0   0   0
14  canyon  1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   
0   0   0   0   1   0   0   0   0   0   0   0   0   0   0   0   0   0
15  canyon  1   0   0   0   0   1   0   0   0   0   0   0   0   0   0   0   0   
0   0   0   1   0   0   0   0   0   0   0   1   1   0   0   0   0   0
16  canyon  1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   
0   0   0   0   1   0   0   0   0   0   0   0   0   0   0   0   0   0
17  canyon  1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   
0   0   0   0   1   0   0   1   0   0   0   0   0   0   0   0   0   0
18  canyon  1   0   0   0   0   0   0   0   0   0   0   0   0   0   1   1   1   
0   0   0   0   0   0   0   0   0   0   0   0   1   0   0   1   0   0
19  canyon  1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   
0   0   0   0   1   0   1   0   0   0   0   0   0   0   0   0   1   0
20  canyon  1   0   0   0   0   1   0   0   0   0   0   0   0   1   1   0   0   
0   0   0   0   1   0   0   0   0   0   0   0   0   1   0   0   0   1
22  alluvial1   0   0   0   0   1   0   0   0   1   0   0   1   0   1   0   
0   0   0   0   0   1   0   1   1   0   0   1   0   1   0   0   1   0   0
23  alluvial1   0   0   0   0   0   0   0   0   0   0   0   1   0   0   0   
0   0   1   0   0   1   0   0   1   0   0   0   0   0   1   0   0   0   0
", header=T)

data<-rawdata[,-1]
rownames(data)<-rawdata[,1]

library(rareNMtests)
test.data<-EcoTest.sample(data[,-1], by=data$SiteType, MARGIN=1, trace=F)  
#error message and no significant difference

data2<- data[do.call(order, data),]
test.data2<-EcoTest.sample(data2[,-1], by=data2$SiteType, MARGIN=1, trace=F)  
#error message and significant difference



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[R-sig-eco] Functional Response Modelling in R

2019-01-11 Thread Emma De Roy

I'm having trouble modelling predator-dependent functional responses in R
that incorporate non-random movement of predators and prey (i.e. prey
aggregation and thus an aggregative response), and habitat complexity.

To my knowledge, frair is the only R package available to fit and compare
functional response models, but this merely models traditional
prey-dependent Holling curves.
I did notice that there was once documentation for the Beddington-DeAngelis
predator-dependent FR, but these models are now deprecated.

Does anyone know of reference materials or resources that may lend
themselves to fitting and comparing functional response models in R that
incorporate the aforementioned parameters?


Thanks in advance,

Emma

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[R-sig-eco] FINAL CALL - Aquatic Acoustic Telemetry Data Analysis

2019-01-11 Thread Oliver Hooker

Aquatic Acoustic Telemetry Data Analysis (ATDA01)

https://www.prstatistics.com/course/aquatic-acoustic-telemetry-data-analysis-atda01/

This course will be delivered by VEMCO (a leading company in the field of
aquatic acoustic telemetry) in Glasgow City Centre form the  28th January -
1 February 2019.

Course Overview:
Acoustic telemetry has become increasingly popular as a method of choice
for monitoring the movements and behaviour of aquatic animals globally.
Increasingly smaller tags along with improvements in battery technology
have allowed for tagging a wide variety of species and life stages,
enabling monitoring of individuals as small as salmon smolts and as large
as whale sharks for periods from 30 days to 10 years.  Receiver technology
also continues to evolve and increasingly allows for data to be collected
at finer spatial and temporal scales than ever before.  In addition, with
more and more acoustic diagnostics information available telemetry datasets
are becoming richer, allowing more detailed analyses of system performance
over time and more robust interpretation of animal detection data.

In this course you will learn about the different types of Vemco acoustic
telemetry technology and their applicability for use in different study
environments and in answering a variety of research questions.  Advantages
and limitations of the different types of equipment will be discussed,
along with resulting study design considerations such as hardware selection
and tag programming considerations.

The planning and testing phases of any acoustic telemetry study are
critical to success, and you will learn about the importance of testing the
suitability of your environment for acoustic telemetry, testing your array
once it is deployed, and monitoring your system performance throughout the
duration of your study.  We will analyze an example range test dataset and
discuss the implications of range test results on array design.  You will
also learn how to test your study deployment in the field and what metrics
are best used to determine whether the array is operating as planned.
Finally, because interpretation of acoustic telemetry data and inferring
animal behaviour from these data is often confounded by array performance
questions, this course will teach you techniques for assessing system
performance to aid in the correct interpretation of animal detection data.

Since telemetry datasets are growing larger all of the time, data
management is becoming increasingly challenging.  During this course you
will learn to perform basic quality assurance on your data (removal of
false detections, time correction of detection data) and basic filtering
techniques (identifying residency events, removing duplicate detections)
for large datasets.

Finally, this course will include extensive discussion of the proper design
and logistics of fine-scale positioning studies and will provide an
overview of the different types of analyses that are commonly performed
with positional data.  You will also have the opportunity to discuss your
own telemetry studies with the experts during a Q session on the final
day of the course.  Bring your own data and your questions!

Course Programme
Monday 28th – Classes from 09:30 to 17:30
Lecture 1
1) Introduction to Acoustic Telemetry
Acoustic vs radio, satellite, PIT tags
History of acoustic telemetry
Comparison of acoustic technologies from different manufacturers
2) How Vemco acoustics work
Continuous vs coded vs HR (High Residence) transmission systems
Pinger tags vs sensor tags
3) Vemco Equipment Overview
Applications of 69 kHz line
Applications of 180 kHz line
Applications of HR (High Residence) technology
4) Designing a Successful Study
Hardware Selection
Tag Programming Considerations
Range Testing
Using Sentinel Tags
5) Vemco Equipment Demo
PPM vs HR coding systems
Surgery techniques

Tuesday 29th – Classes from 09:30 to 17:30
Lecture 2
1) Preparing for Deployment
Preparing your receivers
Initialization tips
Setting up built-in transmitters
2) System performance considerations
Quiet vs noisy locations
Thermoclines
River flow considerations
Mooring design and receiver attachment
3) Testing your deployment
Using transponding data (includes transponding demonstration)
Using offloaded test data/VUE (includes demonstration)
Important considerations during testing
Practical 1
4) Case Study 1 – River migration study (traditional PPM coding example)
Study Design
Range test analysis example
Determining receiver spacing
Tag Programming

Wednesday 30th – Classes from 09:30 to 17:30
1) Practical 2
Data Management (using Case Study 1 data or other example datasets
Assigning stations in VUE
Time Correction
Identifying false detections
Using VUE’s FDA tool
Marking questionable detections in your exported data
2) Filtering your data
Building filters in VUE
Identifying residency events
What to do with duplicate detections
Sensor Tag Data
Bringing in sensor metadata (.VXM) files
How to handle

Re: [R-sig-eco] standard deviation error for EcoTest.sample

[R-sig-eco] standard deviation error for EcoTest.sample

[R-sig-eco] Functional Response Modelling in R

[R-sig-eco] FINAL CALL - Aquatic Acoustic Telemetry Data Analysis

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