Suggestion for trail camera software for photo management and sharing with students?

Hello,

I have a dataset in which we were measuring vegetation height near some Burrowing Owl burrows. Many of the sites were surrounded by bare ground, so our vegetation heights were zero. This has skewed our data and I have tried normalizing them using square root and log(x+1) transformations, with little success (i.e., still not normally distributed). Is there really anything I can do to data such as these to make them normally distributed? Thanks everyone for your input, in advance.

When model fits are ranked according to their AIC values, the model with the lowest AIC value being considered the ‘best’. Models in which the difference in AIC relative to AICmin is < 2 can be considered also to have substantial support (Burnham, 2002; Burnham and Anderson, 1998).

To my knowledge it is common to seek the most parsimonious model by selecting the model with fewest predictor variables among the AIC ranked models. Hence, a variable qualifies to be included only if the model is improved by more than 2.0 (AIC relative to AICmin is > 2).

What do others prefer to do?

1. Present all models in which the difference in AIC relative to AICmin is < 2 (parameter estimates or graphically).

2. Only present the model with lowest AIC value.

3. Take into account the number of predictor variables and select the one with fewest predictor variables among the AIC ranked models.

4. Take into account the number of predictor variables and select the one with fewest predictor variables among the AIC ranked models using the following criteria that a variable qualifies to be included only if the model is improved by more than 2.0 (AIC relative to AICmin is > 2).

5. Looking at p-values of the predictors in the ranked models in addition to the AIC value (e.g. sometimes the predictors are non-significant in the top ranked model, while the predictors in a lower ranked model could be significant).

Regards,

Ronny

Hi everyone,

We are about to start processing photos from a camera trapping study. There are many many thousands of photos and I cant help but think there must be some neat programs out there to speed up or automate the process. So far, 'Camera Base' (CamBase) appears to be the wildlife ecologists' tool of choice. Anyone out there have any other recommendations?

UPDATE: The identification of species will be done by eye (human). What I am looking for is  suggestions of programs that would facilitate the process of going through many photos and that would generate a database based on our identification.

Thanks a lot!

Chris

They seem to be similar, especially in Chinese.

I was recently involved in a debate in Journal of Applied Ecology about whether apex predators preferentially use tracks/trails while mesopredators avoid them. I've accumulated collaborations of camera trap studies on and off trail from Australia, South Africa, Serengeti, Brazil and India to investigate this, but I'd like to. Make it mor e detailed. Hence, I wonder if anyone with such camera trap data is interested in joining our collaboration? If so, please message me and we can discuss.

Smithsonian institute started tiger ecology project in Asia in early 1970s. The radio collared tigers and tracked them. The first tiger was radio-collared in Nepal which I know but I want a literature which mentions this.

Tyto alba nesting sites

We will start soon a 1 year (at least) camera trap project, where the main target will be mesocarnivores. Unfortunately, some animals like mongooses don´t have spots or truly individual marks and many pictures might be blurred, especially at night.

The idea is to use tags (e.g. hear tags, hair cuts, etc.) that will increase the chance of individual identification, even in worst conditions!

At the same time, we will be conducting box trapping sessions to capture several individuals.

Can someone suggest any easy and durable method to mark animals with the lowest impact possible?

Thanks a lot in advance

Best Regards

Filipe Carvalho