Adder Dispersal Distances

Actually the KLIS website doesn't look like it shows all of the different habitat
breakdowns. I believe more detailed information is available on their GIS - and I
aim to get a copy of this.

The following screenshot illustrates what we should be able to do once we
overlay estimated occupancy areas on top of the habitat data.



This example is based on individual adder records, but I aim to take a more
landscape level approach to the analysis.

I should also point out that there are nearly 2000 gcn records on the KRAG
db. However, several hundred of these are either terrestrial observations or
do not specify whether the observation was on land or in water. For the map
above, I was interested in plotting records that were confirmed as
originating in a waterbody. There would be quite a few more red dots if I
was less demanding !

Not yet addressed some of Vicar's points.

Vicar wrote: Distribution is possible, dispersal...is going to have to be pretty wooly imo. As you say, there will be sites where the animals cannot disperse, due to habitat restrictions.

What I am suggesting is that we can use maximum likely dispersal as a
proxy for predicting distribution from confirmed locations. I completely
agree that the prediction is woolly. However, I am less concerned about
this at the moment, because it is a prediction that will become less
woolly as more records are collected.

In areas where animals cannot disperse we may expect less records. I am
therefore minded to use the count of nearest neighbour(s) as a proxy for
estimating the prevalence of dispersal barriers ('dispersal potential').

Of course, this all relies on a good data set. But I would hope that as
more records are collected the prediction would become more refined.
The estimate is also likely to be less woolly for species with larger
potential dispersal distances. The smaller the dispersal distance, the
more records that are needed to adequately identify distribution
boundaries.

Grass snake may have been an easier species to start with, but since
adders have such a well defined range in Kent they are perhaps a more
interesting target.

Vicar wrote: How will you know that a 'summer' sighting is not from an undetected hibernaculum area?

We wouldn't. However, a site with only summer sightings can be flagged
within the db for early spring survey work.

If I chose to use nearest neighbour as a proxy for dispersal potential, it
may not be necessary to control for season. Of course the beauty of a
relational database is that this information can still be stored and turned
on at some point when the dataset is considered sufficiently large.

Vicar wrote: I think a useful goal would be to produce a very simple Vb HSI, using only data that is already available at county level.

Yep. My aim is to correlate the predicted presence of a species in a
specified habitat type against (1) the area that that habitat covers across
the county (potential range) and (2) habitat within the polygons defined
by available records and maximum likely dispersal (predicted range).

Maximum likely dispersal is a variable that can be defined by species,
lifestage, observation date and dispersal potential.

Sorry, this all probably sounds horribly complicated to other folks reading
the thread! It isn't really - honest. Both Vicar and I are aiming to achieve
the same ends, the difference at the moment is in the methods that we
may use to estimate overall distribution.

Vicar, if I remember correctly you use a statistical routine to predict
presence based on nearest neighbour. As I understand it, your method
would involve dividing the area of interest into a number of blocks
(defined by 6 figure grid references?) and then predicting the likely
presence of each species in that block using statistical analysis of the
nearest neighbour dataset? Presumably you would then compare
predicted presence against HSI to further refine the occupancy estimate?
Or perhaps only run the calculation on those squares already flagged by
the HSI?

I guess what I am suggesting is to incorporate maximum likely dispersal
into the calculation as a way of helping to further refine the estimate.

A last comment - honest! We should also remember that presence of a
species on a site may well be a consequence of historical dispersal. Just
because a site is now isolated doesn't mean that is wasn't colonised at an
earlier time.

calumma wrote: Using the squared distance is relatively straightforward to incorporate into my nearest neighbour script so I will updating things after work.

Have modified the relevant script and decreased processing time by 63% If I
cleaned up some other elements within the table I would probably see
further gains as well

calumma wrote: For interest, 75% (upper quartile) of Kent adder records are situated within 0.86 km of each other. This seems a reasonable figure to use for the upper limit.

And when I control for possibly spurious outliers by excluding unconfirmed
records, the value drops to 0.64 km. I'll post a graphic showing what this
looks like.