1. drifts and an increased chance of inbreeding

1. Introduction

1.1 Isolated Populations

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threatened species have their populations isolated due to habitat
fragmentation, which can have serious implications with regards to their long
term success (Morrogh-Bernard et al. 2003). The fragmentation of habitat is
usually a result of human disturbance, for example many animals will not pass
across roads as it leaves them exposed meaning the development of a highway can
often lead to one large population becoming a number of small isolated
populations (Mader, 1984). This
fragmentation reduces a habitats carrying capacity which is the number of
individuals a habitat can support and is limited by factors such as space and


all species are effected in some way by both environmental and demographic
stochasticity, these isolated populations are at a greater risk of extinction (Frankham, 1998).
Environmental stochasticity describes natural hazards such as changing weather
or catastrophes, the Pagan reed-warbler (Acrocephalus
yamashinae) is an example of this as a volcanic eruption on Pagan in 1981
likely caused the small population to go extinct (Reichel et al.,
1992). Demographic
stochasticity describes the effects of births and deaths on the age and sex
ratios for example the dusky seaside sparrow (Ammospiza maritima nigrescens) is now extinct having only males
left in the population (Sykes, 1980).


populations are also at increased risk from genetic drifts and an increased
chance of inbreeding between family members, all of which can lead to genetic
defects such as lower fertility rates or smaller litter sizes (Keyghobadi et al. 2005). Large species are
often the most vulnerable as environmental extinction risks rise sharply in
species with a larger than 3 kg body mass (Cardillo et al. 2005).

that reproduce slowly are also particularly susceptible to extinction on a
local basis as they are often slow to recover from reduced population in sizes
(Marshall, et al., 2009).

survive these stochastic and genetic risks, species have a minimum viable
population (PVA) which varies depending on the particular species (Soulé, 1987).
As habitat fragmentation currently threatens a wide variety of species as such,
integrating sub-populations has become a key conservation tool (Vitousek, 1990).


1.2 Connectivity and Translocation

more research is being conducted into understanding the relationships between
meta-populations and meta-ecosystems, as the links between habitat patches and
species are crucial to predicting and managing resource dynamics (Loreau et al., 2003).


the exchange of individuals between geographically separated subpopulations in
a meta-population (Le Corre, et al., 2012), is one of the key characteristics
affecting these dynamics. Due to its broad definition, connectivity is used in
a wide range of fields such as habitat management (Schumaker, 1996). As a habitat management tool connectivity is
used to introduce new genomes into a population and is often referred to as
either structural or functional connectivity. Structural connectivity usually
refers to the direct links between fragments, such as the density and
complexity of corridors (Uezu et al., 2005). Whist functional connectivity is
the way a wider landscape interacts with organisms by either impeding or
facilitating movement and can vary between species. Both forms of connectivity
are important when trying to connect fragmented habitats together, as a corridor
for one species may be a barrier for another (Uezu et al., 2005).


is another management technique that can be potentially used as a complement to
connectivity. Translocation is often used to supplement a population by
introducing new genomes to relive the effects of the allee effect (Lindenmayer et al., 2000).  Whilst a useful tool, it is only effective if
a population is below the habitats carrying capacity. Adding individuals to a
population at its carrying capacity will have little to no effect and in some
cases can increase mortality as individual compete for sparse resources (Lubow, 1996).
However, connecting habitat fragments often increases the overall carrying
capacity of habitat allowing for a greater number of individuals.


1.3 P.V.A

viability analysis (PVA) is a means of estimating a population’s extinction
risk through assessing threats to survival in models (Boyce, 1992).
Simulations of these models are used by conservationists to assess different
management decisions and the viability of small populations by taking into
account deterministic forces and stochastic events. Though there some
scepticism as to the effectiveness of PVA’s they are regularly used to inform
management decisions.


1.4 Orangutan

are currently only found in parts of Borneo and Sumatra where both species (Pongo
pygmaeus, and the Sumatran orangutan, P. abelii) are currently at
risk of extinction (Meijaard et al.,
2010). Largely this is a result of deforestation and habitat
degradation, leading to increasingly smaller, more fragmented and isolated
populations (Wich et al., 2008).
However, individual populations have shown that where the regeneration of
woodland occurs they can recover. For example in the Sebangau swamp forests,
illegal deforestation caused orang-utans to leave the area leading to
overcrowding of surrounding areas which lead to increased mortality, however,
once the forest had begun to regenerate they returned (Miles, 2007).

1.5 Aims

study will investigate the effects of fragmentation and habitat loss may have
on genetic diversity and survivorship of the 11 individual orangutan
populations (shown in figure 1) located within Lower Kinabatangan Wildlife
Sanctuary (LKWS) designated as primary sampling units (PSU). The populations
will be tested against two catastrophes in adult mortality due to crop raids
and severe drought. This will help to show the populations ability to withstand
both demographic and environmental stochastic processes. Three different
management plans will be tested in an attempt to improve the genetic diversity
and gene flow of the orang-utan populations this will include: connectivity,
translocation and connectivity and translocation.


2.1 Study Populations

Bornean orangutan is listed under Appendix 1 of the Convention of International
Trade of Endangered Species (CITES) and is fully protected in Indonesia and
Malaysia (Curran et al., 2004).
In 2005 Sabahs state government declared that 26,000ha of the Lower
Kinabatangan flood plains as the Lower Kinabatangan Wildlife Sanctuary per the
states Wildlife Conservation Enactment of 1997. Despite this 20% of orangutan
habitat in Sabah is unprotected (Ancrenaz et al., 2004). 


lies in the floodplain of the Kinabatangan River on the isle of Borneo and
contains a well-studied population of orangutans (Lackman-Ancrenaz
et al., 2001). Despite high levels of fragmentation
and degradation, largely as a result of oil plantations, the flood plains
continue to be an important biodiversity hotspot for Malaysia. 41,000 Ha of
LKWS are considered to be suitable for orangutans and contains approximately
1125 of the species. However, due to the fragmentation of the forests, Ancrenaz
et al., (2004) considers the orangutans to be split into 11 individual
populations. This study will focus on the 11 PSUs and the best means of their
continued survival as well as investigating the benefits of connecting the
populations to promote gene flow.


2.2 Software

variety of simulations were ran through the stochastic simulation software
VORTEX 10.2.7 which has been used by a large number of wildlife agencies (Lacy,
1993). The program was originally developed to model avian and mammalian
populations through a series of events that describe deterministic forces and
genetic, environmental and demographic stochasticity (Lacy, 1993). This makes
it appropriate for modelling the orangutan populations of the Lower
Kinabatangan Wildlife Sanctuary (Bruford et
al., 2010).


2.3 The Baseline

standard protocol of 1000 simulations were carried out over 100 years, which is
an equivalent to 4-12 orangutan generations, per Ancrenaz et al., (2004), Bruford et
al., (2010) and Singleton et al.,
(2004). All of these papers recommended 1000 simulations for increased accuracy
and used the 100 year time frame although Ancrenaz et al., (2004) did also use a 25 year and 200 year simulation.
These papers all studied the same groups of orangutans allowing results to be
compared to other similar papers.


and reproduction rates were both equally applied across all of the PSU’s and
included El Nino weather events and human conflict, the only parameters that
were specific to each population were carrying capacity from Ancrenaz et al., (2004) and population numbers
which come from Bruford et al., (2010),
Ancrenaz et al., (2004) and Singleton
et al., (2004). These studies used
both ground and helicopter surveying to estimate the population sizes of all 11
populations. Mortality rates were developed over a 10 year period of
observation by Ancrenaz et al.,
(2010). Due to the very high density of orangutans in some PSUs density
dependant reproduction was used for all models using the following formula for
density dependence: P(N)=(P(0)-(P(0)-P(K))(N/K)B)N/N+A the
variables of which are shown in table 1.

2.4 Simulations

2.4.1 No intervention

baseline simulation was ran both with and without inbreeding depression across
all eleven of the populations to show the state of the populations without any
intervention (Lacy, 1993).


2.4.2 Translocations

second model tested the translocation of two females from PSU 2 and three
females from both PSU 1 and 5 to the other populations, as these hold the
largest numbers of orangutans. This is has been modelled at every 10, 20 and 50
years to see the effect it has on the genetic diversity of the overall
populations. Females were chosen to be translocated as they are less likely to
genetically dominate a population (Utami et al. 2002).


2.4.3 Connectivity

third model gradually increased carrying capacity to simulate the effects of
implementing forest corridors to connect each PSU to its nearest neighbouring
population. Connectivity between populations can only take part a long each
side of the river as it is very rare for orangutans to cross (Arora, et al., 2010).
The increase in capacity is dependent on the size of the population area,
distance to nearest population and other factors such as rivers and roads. The
following data set was be used:


Mixed Approach

final model uses both translocation of one female every 20 years and the
introduction of corridors between sites used in the connectivity simulations.


3. Results

2 shows the baseline model for all 11 PSUs of the LKWS and whilst the larger
populations look likely to survive many of the smaller populations are clearly
at risk.

3, 4 and 5 show the translocation of orang-utans every 10, 20 and 50 years
respectively. The 10 year translocation model shows improvements for the small
populations but overall the meta-population actually ends up lower than it
would with no management (599.78 – 597.46). Both the 20 and 50 year
translocations show similar improvements over the baseline model (607.33 &
608.88 final meta-populations) however the 20 year model showed greater
improvements in the smaller populations. 

Figure 6 shows connectivity as a
management tool and shows a marked improvement over the baseline and
translocation models. The overall meta-population numbers are at 681.01 and
improving in each population and the survivorship chance of smaller populations
for example PSU 10 goes from a 15% chance of survivorship over 100 years to a

these results the final management proposal of connectivity and translocation
was used, shown in figure 7. This showed the greatest improvement with the
final meta-population number at 710.98 and improving, genetic diversity up to
0.99 compared to the baselines 0.92 and the survivorship chance of the smallest
PSU (10) up to 83%.

4. Discussion

4.1 Management

large decrease in numbers and the increased extinction risk shown in all but
the three biggest populations (PSUs 1, 2 and 5) would suggest that no
intervention is an unsuitable conservation method for the species. The model
did not include the continued degradation of woodland which would likely lead
to smaller carrying capacities and increased pressure on the small
populations.  However, any management
plans put in place would need to be ecologically, financially and logistically
viable (Kiss, 1990).


translocation model is likely the most financially and logistically attractive
and given the long lifespan of the orangutan, infrequent translocations could
significantly improve the demographic and genetic stability of the smaller
populations (Bruford, et al., 2010). Previous studies have shown that limited
introductions of migrant genomes to a sub-population can drastically improve
genetic diversity, limiting the effects of inbreeding and genetic drift
(Saccheri & Brakefield, 2002). However, the effects on social structure,
animal health of both the source and sink populations must be taken into
account and any translocations must adhere to IUCNs guidelines.


translocation of females every 50 years did not prevent high levels of
inbreeding in the smaller PSUs. However, both the 10 and 20 year models were
more successful, although neither model is likely to save PSU 10 and both
models show a large decline in population number for the source PSUs. This
could be alleviated by sourcing individuals from other populations such as
animals rescued from nearby plantations and nearby rehabilitation centres (Singleton
et al., 2004).  The biggest issue with translocation is that
it does not offer a solution to ecological issues such as habitat degradation
which is likely to continue (Ancrenaz et
al., 2004).


the second simulation aims to combat the ecological issues that affect the
orangutans of the wildlife sanctuary through the restoration of habitat and the
creation of habitat. The aim of the LKWS management plan is to eventually
reconnect all of the PSUs (Estes et al.,
2012). Both the demographic and genetic benefits of doing so can be clearly
seen in the simulation, particularly of the smaller populations, although PSU
10 still has a significant chance of extinction. Despite the use of realistic
scenarios, the efficiency of this model is dependent on the speed at which the
connectivity can be implemented (Bruford et
al., 2010). Many factors can influence this from financial, as more land
will need to be acquired, to the rate at which the forests recolonise the areas
and connections across human habitats and roads. Therefore, any estimates need
to be taken with caution due to the difficulty of the implementing the


the inability of both the connectivity and translocation models to deal with
issues surrounding the orangutans at the sanctuary, the mix approach may be
most suitable. The combination of the 20 year translocation method, which
provided the best proportional change in results across all PSUs, and the
connectivity method, which provided the largest mean population size and lowest
coefficient. This is likely the most realistic method for solving the issues
facing the orangutans within the sanctuary. 


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