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SEXIBAM
Spatially-EXplicit
Individual-BAsed
Metapopulation
model
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Page under construction.
The software is not yet available for download.
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Figure 1. Splash screen.
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Context
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A few packages already exist to simulate the
dynamics of populations in fragmented
landscapes. However, the movement of
individuals between populations is generally
over-simplified, whereas it is recognised as
a major driver in the distribution and
persistence of species.
For instance, we found
that juveniles of North-Island robins (Petroica
longipes) are very reluctant to cross
more than 100 m of pasture during dispersal
(see
our methodology), and the landscape
structure therefore needs to be taken into account if
one wants to model the amount of movements
between populations. Unfortunately, in
metapopulation models, movements are often
assumed to be independent of the matrix (the
part of the landscape between habitat
patches), and
only dependent on the distance between
habitat patches and on the size and/or quality
of the patches.
For my PhD, I wanted a model that could be
used to disentangle the relative effects of
habitat quality and landscape connectivity
on species persistence and distribution. To
account for landscape connectivity, I wanted
a realistic-yet-simple model of animal
movement, which could be easily manipulated,
and potentially applicable to other
species and landscapes. Moreover, I wanted a
flexible way of manipulating habitat quality
via its impact on the species vital rates
(productivity and adult/juvenile survival). |
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Description
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SEXIBAM is a program I wrote in C++ to model the dynamics of populations
in fragmented landscapes. It is individually-based, i.e. the fate of each individual is modelled (both males and females):
individuals reproduce, their juveniles disperse, settle in a territory, find a partner if possible,
and eventually die, and each stage is controlled by parameters.
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It is also spatially-explicit in the sense that patches and individuals occur in a specific landscape.
Real or created maps (as rasters) of vegetation cover for example can be used.
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Finally, it is a metapopulation model, because individuals are located in discrete habitat patches,
either provided by the user or calculated using the program using the patch recognition function.
During the simulations, the number of individuals in each patch is recorded for better insight into
the whole dynamics at the landscape level.
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Figure 2. SEXIBAM's map window,
showing the studied landscape and a simulated movement path with a 50-m gap crossing ability (in pink).
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Figure 3. SEXIBAM's parameter window,
where parameters on dispersal and vital rates (productivity, adult and juvenile survival) can be changed.
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Simulations
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| Metapopulation dynamics |
Simulation of the whole metapopulation dynamics over time, starting from specified locations
(coordinates or mouse clicks on the map), or random in the landscape or within specific patches.
The output is a csv file presenting the
metapopulation state at each simulation year: metapopulation size, the number of males and females,
the number of juveniles produced, the number and proportion of occupied patches, the patch turnover,
the number of juveniles who left the study area or died during dispersal.
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| Dispersal simulations |
Dispersal only (no mortality, no reproduction) from specified or random locations or patches.
The output is a csv file indicating for each dispersal trajectory the starting and ending locations,
the Euclidean distance, the total distance achieved, the number of visited patches and the total
area visited (sum of the areas of visited patches). A map of all dispersal paths and
of settlement locations can be also saved.
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Figure 4. Example of dispersal simulations with SEXIBAM.
Two sets of 500 dispersal simulations each were run, keeping the distribution of total dispersal distances equal,
but changing the gap crossing ability from 100 m to 150 m. All paths started from the central red
point. The frequency of visits of each cell is represented by a gradient from blue (one or few
visits) to red (many visits).
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What it can be used for...
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| Metapopulation or population viability analysis |
To follow the trajectory of the species distribution and population sizes over time. |
| Re-introduction assessment |
To estimate the viability of hypothetic re-introduced populations. |
| Sensitivity analysis |
To assess the effect of various parameter values on the distribution and persistence of
the metapopulation. |
| Connectivity analysis |
To assess the landscape or patch functional connectivity. |
Dispersal calibration
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To calibrate the distribution of dispersal distances to observed data.
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Model assumptions in the present version
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| No environmental stochasticity |
This will be changed soon. |
| No habitat selection |
Juveniles settle in habitat patches regardless of their quality. |
| Binary gap crossing behaviour |
A gap is either crossable or not, depending on the specified gap crossing ability value. |
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