Linear habitat and secr
Posted: Fri Aug 09, 2019 7:27 pm
Hello -
I am struggling with how to salvage an analysis!
I am trying to get a baseline density estimate of sitatunga. My study animal is a wetland specialist, that seldom forage on dry land habitats adjacent to the wetlands. I expect their activity centres to lie within wetlands, thus have created a habitat mask for available habitat. There is no information about their movement rates, thus I included in the mask a 5km extension from the ends of the trapping array to hopefully close the state space (negligible chance to capture those outside of the area). However, these are riverine wetlands, and thus are linear in nature (our camera trapping array was about 10 km long x 0.5 km wide) surrounded by dry lands. In order to maximize detection, the trapping array we used was oriented along the river, which I understand now can bias density and sigma estimates.
I have fit a null SECR model, which (according to plotting the predictDsurface) shows no variation in density in the habitat mask, and a very high density estimate of 16 / km^2. As I understand it, if the (mostly)linear trapping array and the home ranges are elongated in the same direction, then we risk severe bias in the parameter estimates. Now I am wondering what the next step should be. Is there a way to check if the trapping array is two dimensional "enough" to use isotropic detection models? Since sitatunga can use non-wetland habitat, should I use a buffer around the traps instead of the strict habitat mask? Are anisotropic detection models worth pursuing, since the bias may not be removed? Is there another option?
Thanks in advance for any advice!
**EDITED to add (in case it matters): I ran a model using a buffer of 3500 instead of using the mask, and the density estimate is much more reasonable (4.5 / km^2). The suggest.buffer command on both models is very similar (3319 for the version with the habitat mask, and 3371 for the buffered version).
I am struggling with how to salvage an analysis!
I am trying to get a baseline density estimate of sitatunga. My study animal is a wetland specialist, that seldom forage on dry land habitats adjacent to the wetlands. I expect their activity centres to lie within wetlands, thus have created a habitat mask for available habitat. There is no information about their movement rates, thus I included in the mask a 5km extension from the ends of the trapping array to hopefully close the state space (negligible chance to capture those outside of the area). However, these are riverine wetlands, and thus are linear in nature (our camera trapping array was about 10 km long x 0.5 km wide) surrounded by dry lands. In order to maximize detection, the trapping array we used was oriented along the river, which I understand now can bias density and sigma estimates.
I have fit a null SECR model, which (according to plotting the predictDsurface) shows no variation in density in the habitat mask, and a very high density estimate of 16 / km^2. As I understand it, if the (mostly)linear trapping array and the home ranges are elongated in the same direction, then we risk severe bias in the parameter estimates. Now I am wondering what the next step should be. Is there a way to check if the trapping array is two dimensional "enough" to use isotropic detection models? Since sitatunga can use non-wetland habitat, should I use a buffer around the traps instead of the strict habitat mask? Are anisotropic detection models worth pursuing, since the bias may not be removed? Is there another option?
Thanks in advance for any advice!
**EDITED to add (in case it matters): I ran a model using a buffer of 3500 instead of using the mask, and the density estimate is much more reasonable (4.5 / km^2). The suggest.buffer command on both models is very similar (3319 for the version with the habitat mask, and 3371 for the buffered version).