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Surface-water dynamics and land use influence landscape connectivity across a major dryland region

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Landscape connectivity is important for the long-term persistence of species inhabiting dryland freshwater ecosystems, with spatiotemporal surface-water dynamics (e.g., flooding) maintaining connectivity by both creating temporary habitats and providing transient opportunities for dispersal. Improving our understanding of how landscape connectivity varies with respect to surface-water dynamics and land use is an important step to maintaining biodiversity in dynamic dryland environments. Using a newly available validated Landsat TM and ETM+ surface-water time series, we modelled landscape connectivity between dynamic surface-water habitats within Australia's 1 million km2 semi-arid Murray Darling Basin across a 25-year period (1987 to 2011). We identified key habitats that serve as well-connected 'hubs', or 'stepping-stones' that allow long-distance movements through surface-water habitat networks. We compared distributions of these habitats for short- and long-distance dispersal species during dry, average and wet seasons, and across land-use types. The distribution of stepping-stones and hubs varied both spatially and temporally, with temporal changes driven by drought and flooding dynamics. Conservation areas and natural environments contained higher than expected proportions of both stepping-stones and hubs throughout the time series; however, highly modified agricultural landscapes increased in importance during wet seasons. Irrigated landscapes contained particularly high proportions of well-connected hubs for long-distance dispersers, but remained relatively disconnected for less vagile organisms. The habitats identified by our study may serve as ideal high-priority targets for land-use specific management aimed at maintaining or improving dispersal between surface-water habitats, potentially providing benefits to biodiversity beyond the immediate site scale. Our results also highlight the importance of accounting for the influence of spatial and temporal surface-water dynamics when studying landscape connectivity within highly variable dryland environments. This article is protected by copyright. All rights reserved.
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doi: 10.1002/eap.1507
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MR. ROBBI BISHOP-TAYLOR (Orcid ID : 0000-0002-1533-2599)
Received Date : 05-Oct-2016
Revised Date : 22-Dec-2016
Accepted Date : 10-Jan-2017
Article type : Articles
Running head
Dynamic surface-water connectivity
Surface-water dynamics and land use influence landscape connectivity across a major dryland region
List of authors
Robbi Bishop-Taylor (Corresponding author1)
School of Biological, Earth & Environmental Sciences,
University of New South Wales, Sydney, NSW 2052, Australia
Mirela G Tulbure
School of Biological, Earth & Environmental Sciences,
University of New South Wales, Sydney, NSW 2052, Australia
Mark Broich
School of Biological, Earth & Environmental Sciences,
University of New South Wales, Sydney, NSW 2052, Australia
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Landscape connectivity is important for the long-term persistence of species inhabiting dryland
freshwater ecosystems, with spatiotemporal surface-water dynamics (e.g., flooding) maintaining
connectivity by both creating temporary habitats and providing transient opportunities for dispersal.
Improving our understanding of how landscape connectivity varies with respect to surface-water
dynamics and land use is an important step to maintaining biodiversity in dynamic dryland
environments. Using a newly available validated Landsat TM and ETM+ surface-water time series,
we modelled landscape connectivity between dynamic surface-water habitats within Australia’s 1
million km2 semi-arid Murray Darling Basin across a 25-year period (1987 to 2011). We identified
key habitats that serve as well-connected ‘hubs’, or ‘stepping-stones’ that allow long-distance
movements through surface-water habitat networks. We compared distributions of these habitats
for short- and long-distance dispersal species during dry, average and wet seasons, and across land-
use types. The distribution of stepping-stones and hubs varied both spatially and temporally, with
temporal changes driven by drought and flooding dynamics. Conservation areas and natural
environments contained higher than expected proportions of both stepping-stones and hubs
throughout the time series; however, highly modified agricultural landscapes increased in
importance during wet seasons. Irrigated landscapes contained particularly high proportions of well-
connected hubs for long-distance dispersers, but remained relatively disconnected for less vagile
organisms. The habitats identified by our study may serve as ideal high-priority targets for land-use
specific management aimed at maintaining or improving dispersal between surface-water habitats,
potentially providing benefits to biodiversity beyond the immediate site scale. Our results also
highlight the importance of accounting for the influence of spatial and temporal surface-water
dynamics when studying landscape connectivity within highly variable dryland environments.
Keywords: landscape connectivity, surface-water dynamics, graph theory, land use, protected areas,
wetlands, flooding, Australia, Murray-Darling Basin, Landsat
Dryland freshwater environments are among the most vulnerable ecosystems globally (Williams
1999), faced with the combined threats of land-use intensification and changes in water availability
driven by increased human consumption and a changing climate (Vörösmarty et al. 2010, Davis et al.
2015). This has resulted in extensive loss (up to 87% by area since 1700 AD) and degradation of
biodiverse wetlands due to agricultural and urban development and flow modification (Davidson
2014), isolation of remaining habitats separated by an increasingly modified landscape matrix
(Collins et al. 2014), and increasing severity of extreme drought and flooding events (Leblanc et al.
2012). The combined impacts of these changes to surrounding land use and spatiotemporal surface-
water dynamics (i.e., drought or flooding) are likely to disproportionately impact the population
persistence of dryland freshwater biota by reducing opportunities for dispersal through already
variable and fragmented surface-water habitat networks (Ward and Stanford 1995, Davis et al. 2015,
Saunders et al. 2015). Maintaining vital landscape-scale processes such as landscape connectivity in
ecosystems affected by increasingly intense human use and environmental change remains a key
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challenge facing conservation in the 21st century (Rudnick et al. 2012).
To mitigate these threats posed to freshwater ecosystems, biodiversity conservation approaches
have attempted to preserve ecologically significant surface-water habitats using protected area
strategies (Suski and Cooke 2006). Despite being effective at reducing site-scale threats such as
conversion of land for agriculture, protected area strategies can fail if they do not address
threatening processes operating at landscape, catchment or basin scales, such as changes to
hydrology or reductions in landscape connectivity (Pringle 2001, Berney and Hosking 2016). This may
be of particular concern for surface-water ecosystems, given the reliance of water-dependant biota
on water availability and variable flooding dynamics for recruitment and dispersal between habitats
(Crook et al. 2015). Identifying and managing habitats that preserve or enhance landscape
connectivity within habitat networks may provide a promising means to maintain population
persistence and biological diversity in threatened landscapes (Saunders et al. 2015). These areas
could serve as high-priority targets for the provision of limited and contested environmental water,
potentially facilitating dispersal and gene flow among isolated surface-water habitats and providing
biodiversity benefits that extend beyond protected area boundaries (Arthington 2012, Crook et al.
2015). Prioritising dynamic surface-water habitats for conservation at a network scale, however,
requires a detailed understanding of how spatial and temporal surface-water patterns influence the
potential for connectivity, particularly within heterogeneous landscapes affected by varying degrees
of modification (Watts et al. 2015).
Graph theory network analysis provides a powerful approach for studying spatiotemporal
connectivity patterns. Graph theory allows connectivity to be assessed across large spatial extents by
analysing entire landscapes in a network data structure (Minor and Urban 2007, 2008). For example,
individual surface-water habitats in a spatial surface-water habitat network can be depicted as a
series of ‘nodes’, with each node representing a potential habitat that can be connected to others by
an ‘edge’ (or link) if an ecological connection exists between them (Urban and Keitt 2001). In a
spatial habitat network, this may be the case when the distance and landscape conditions between
two neighbouring habitats allow resident water-dependent species to disperse successfully between
them. This spatially explicit analysis framework allows large habitat networks to be compared
consistently across both time and space. Graph theory analyses have provided insights into many
aspects of connectivity including how the loss or creation of habitats may impact long-distance
dispersal and gene flow (Uden et al. 2014), how migrating species may respond to a changing
climate (McIntyre et al. 2014, Dilts et al. 2016, McGuire et al. 2016) and how drought or flooding
may affect surface-water habitat network structure or metapopulation viability (Fortuna et al. 2006,
Wright 2010, Tulbure et al. 2014, Bishop-Taylor et al. 2015).
Algorithms provided by graph theory network analysis can assist in prioritising conservation
management actions by identifying individual habitats that are particularly important for facilitating
connectivity within habitat networks. Two of the most important roles a habitat can play are as
‘stepping-stones’ or ‘hubs’ (Ruiz et al. 2014, Saura et al. 2014). Stepping-stones are habitats that
allow dispersing organisms or populations to make long-distance movements through networks by
providing connections between larger groups of connected habitats. Stepping-stones can be critical
for range expansion, particularly as changing climate conditions and habitat loss forces organisms to
move long distances through increasingly modified and hostile landscapes (Kramer-Schadt et al.
2011, Saura et al. 2014). Hubs are habitats that are connected to a high number of neighbours
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relative to other habitats in the network. These habitats provide extensive opportunities for local
dispersal, and may serve as important ecological refuges into which organisms can retreat and
subsequently disperse from after periods of environmental disturbance (Davis et al. 2013, Bishop-
Taylor et al. 2015). Obtaining a better understanding of the importance of individual habitats within
habitat networks can therefore assist in maintaining landscape connectivity at scales far beyond
immediate sites.
While stepping-stone and hub habitats may represent ideal targets for habitat conservation, their
importance is unlikely to be consistent through time (Tulbure et al. 2014). In highly dynamic dryland
surface-water environments, only a small proportion of habitats may remain present from year to
year or season to season (Roshier et al. 2001). Other temporary or ephemeral habitats may play
important roles as connectivity providers only during specific environmental conditions, such as
during periods of extreme drought or flood (Zeigler and Fagan 2014). Despite the important
implications of spatiotemporal surface-water dynamics for connectivity, previous research has
focused on assessing connectivity between static or modelled sets of surface-water habitats (e.g.,
Fortuna et al. 2006, O’Farrill et al. 2014), or between waterbodies at discrete ‘snapshots’ in time
(e.g., Wright 2010, Uden et al. 2014). Little research has quantified how the distribution of important
stepping-stones and hubs varies across both space and time in the context of dynamic periods of
drought or flooding (Tulbure et al. 2014) or evaluated how these changing distributions interact with
current protected area listings or local land-use patterns (Ruiz et al. 2014, Bishop-Taylor et al. 2015).
In this study, we used graph theory network analysis to model spatially explicit patterns of important
stepping-stones and hubs using a newly available 25-year seasonally-continuous and statistically-
validated time series of potential surface-water habitats based on Landsat TM and ETM+ satellite
imagery (Tulbure et al. 2016). We focused on assessing connectivity within Australia’s Murray-
Darling Basin, a major inland river basin containing some of the world’s most ecologically significant
dryland floodplain and wetland environments (Rogers and Ralph 2010). By conducting to our
knowledge the largest spatially explicit ecological network analysis to date (a total of 5.4 million
nodes processed across 99 time-steps and two dispersal abilities), our study sought to answer the
following research questions relating to interactions among landscape connectivity, surface-water
dynamics and land-use and conservation management:
1. How does the distribution of important stepping-stones and hubs vary among dry,
average or wet conditions?
2. Are modified land-use types (e.g., grazing, dryland and irrigated agriculture) less likely to
contain stepping-stones and hubs than protected or natural areas?
3. How does dispersal ability affect relationships between land use and the distribution of
stepping-stones and hubs during dry, average or wet conditions?
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Study area
We focused on the Murray-Darling Basin (MDB), a 1 million km2 semi-arid basin in south-eastern
Australia (Figure 1). Despite being one of the driest catchments in the world by runoff (MDBA 2010),
the MDB supports some of Australia’s most biologically diverse and ecologically significant floodplain
and wetland habitats, including 16 sites listed as Wetlands of International Importance under the
Ramsar Convention (Rogers and Ralph 2010, Pittock and Finlayson 2011). Extreme variability in
water availability in the MDB drives a characteristic ‘boom and bust’ ecology, with populations of
many species fluctuating greatly in response to periods of severe drought and flooding (Ballinger and
Mac Nally 2006). Although these extremes are a natural feature of its regional climate, the MDB has
recently experienced both the most severe drought and wettest two-year period since instrumental
records began: the 1999–2010 Millennium Drought and the 2010–2011 La Niña floods (Mac Nally et
al. 2014). These extremes have combined with impacts of wetland loss, water diversions for
agriculture and flow modification to place increasing pressure on surface-water habitats (Finlayson
et al. 2011). Reduction in flows to wetlands is believed to be the major factor influencing the
ecological health of these ecosystems, with reduced landscape connectivity between habitats a key
threat to the persistence of water-dependent organisms (Finlayson et al. 2011).
Seasonal surface water
We studied the connectivity of surface-water habitats across the entire MDB between 1987–2011
using seasonally continuous observations of surface-water mapped from Landsat TM and ETM+
satellite imagery (Tulbure et al. 2016). To convert surface-water observations into potential habitat
layers, we identified pixels that contained water for greater than 50% of satellite observations within
each southern hemisphere season from 1987–2011 (i.e., autumn, winter, spring and summer
beginning on 1 March, 1 June, 1 September and 1 December, respectively). This majority threshold
ensured that resulting habitat pixels for each season were typically inundated for approximately 1.5
months, an ecologically relevant value for many aquatic invertebrates (e.g., 1–2 months from eggs to
independence for several MDB crustaceans; Jones 2010), amphibians (e.g., 2–4 month tadpole
lifespan for the majority of MDB frogs; Wassens 2010) and freshwater turtles (e.g., 60–70 day
incubation period near water for common MDB species; Thompson 1983, Kennett and Georges
We filled areas of ‘nodata’ on a per-pixel basis (Tulbure and Broich 2013) when a pixel was missing
data for an entire season (i.e. through consistent cloud cover). ‘Nodata’ pixels were assigned the
most common state (surface water or non-water) from the closest three years with data for the
respective season, commencing with the previous year (e.g., a ‘nodata’ pixel in autumn 2007 was
filled with values from autumn 2006, 2008 and 2005). To facilitate computationally intensive
connectivity analyses for the entire extensive spatial (> 1 million km2) and temporal (99 seasons)
data extent, the original 30m raster pixels were spatially aggregated to 120m pixels using a
maximum aggregation rule that considered aggregated cells as potential habitat if at least one of the
16 contributing pixels contained habitat (McRae 2008). All habitat layer manipulation was conducted
using the GDAL (GDAL Development Team 2015), NumPy and SciPy packages (Walt et al. 2011) for
Python 2.6 (Python Software Foundation 2015).
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Temporally consistent habitat IDs
To consistently compare connectivity across time and space, we assigned each flooded pixel with a
unique, temporally consistent habitat identifier (unique ID). Previous studies have defined discrete,
temporally consistent regions based on the maximum extent of flooding across an entire time series
to account for surface-water habitats growing, shrinking or fragmenting over time (Tulbure et al.
2014). In the MDB, however, rare but very large flooding events form extensive ‘mega-patches’ in
composite maximum extent datasets that can span hundreds of km2, potentially obscuring
ecologically significant seasonal surface-water dynamics. To resolve this ‘mega-patch problem’, we
used a simplified version of a method based on habitat asynchrony and graph theory community
detection previously used to identify temporally consistent subregions in remotely sensed giant kelp
forest time series (Cavanaugh et al. 2014). We initially generated an m x n matrix of n observations
(surface water or non-water for each of the 99 seasons) for each raster pixel m in the study area
(Figure 2a). We converted this matrix into a network graph by connecting surface-water pixels
(nodes) to their immediate 8 spatial neighbours, and weighted the resulting graph edges by the
pairwise Pearson correlation in temporal dynamics between each pixel (i.e., high correlation if the
two pixels consistently occurred together as either surface water or non-water throughout the time
series; Figure 2b).
We used the Igraph Python package (Csardi and Nepusz 2006) ‘community_multilevel’
implementation of the Louvain community detection algorithm to divide ‘mega-patches’ into
discrete asynchronous potential habitats (Cavanaugh et al. 2014). The Louvain algorithm efficiently
partitions large connected graphs into unique ‘communities’ by maximising the graph’s modularity, a
measure of the density of within-community links compared to between-community links (Newman
2006, Blondel et al. 2008). As the Louvain algorithm does not accept negative weights, we truncated
graph edge weights (correlations ranging from -1 to 1) to between 0 and 1 prior to analysis. The
community membership identifiers produced by the algorithm were finally converted back into a
raster containing 277,874 temporally consistent unique IDs informed by both the spatial structure
and temporal synchrony of habitats throughout the entire 1987–2011 time series (Figure 2c). These
unique IDs served as individual graph nodes in subsequent network analyses.
To test how successfully each unique ID habitat represented a spatially discrete area of surface
water across each season in the time series, we evaluated the proportion of seasons where surface
water was entirely restricted to within a habitat’s unique ID boundary (i.e., where no flooding
crossed between habitat boundaries). On average, the 277,874 habitats identified by the algorithm
had no water crossing habitat boundaries for over 90% of seasons, indicating that ‘mega-patches’
were successfully broken into unique regions which remained spatially discrete except during the
largest, most infrequent floods.
Circuit theory analysis
We calculated effective distances between unique habitats using circuit theory, a modelling
approach based on random walks that takes into account all possible movement pathways through a
heterogeneous landscape matrix separating two habitats (McRae et al. 2008). Landscape moisture
has been shown to strongly affect movement and connectivity for aquatic invertebrates (Morán-
Ordóñez et al. 2015), amphibians (Murphy et al. 2010, Watts et al. 2015), and turtles (Patrick et al.
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2012). To account for landscape resistance to movement for a broad range of water-dependent
organisms inhabiting the MDB, we used a generalised resistance layer based on landscape moisture
(Goldberg and Waits 2010). This resistance layer was generated by classifying Australian Dynamic
Land Cover Dataset v1.0 classes (Lymburner et al. 2010) and potentially significant movement
barriers or corridors such as roads, streams and urban areas from Geoscience Australia (2006)
topographic mapping into six exponentially increasing resistance classes based on assumed ground
level moisture content, and combining this with areas that received any inundation during each of
the 99 seasons (Table 1).
Pairwise circuit theory resistances (effective distances that account for landscape resistance to
movement) between neighbouring habitats were calculated for each season using a moving window
approach. To allow connectivity between windows and minimise circuit theory edge effects by
providing a large buffered area relative to the longest investigated inter-patch distance (Pelletier et
al. 2014), we divided the entire study area into 12 x 12 km ‘windows’ (100 x 100 raster pixels) that
overlapped by 50% (McIntyre and Strauss 2013). Pairwise resistance distances for each window were
calculated using the Circuitscape v4.04 software package (Shah and McRae 2008) with seasonal
habitat and resistance layers as inputs (connecting raster pixels to their eight immediate
neighbours). When pairs of habitats were analysed multiple times due to overlapping windows, the
lowest resistance distance was selected to eliminate artificially high resistance values associated
with circuit theory edge effects.
Graph theory network modelling
We generated potential surface-water habitat networks for each of the 99 seasonal time-steps in the
1987–2011 time series using graph theory network modelling, with the matrix of Circuitscape
resistance distances between all neighbouring habitat pairs as an input. We treated unique habitats
as graph nodes, and compared connectivity for two maximum circuit theory resistance distances
converted from Euclidean distance values. These resistance distances (0.53 and 1.61) were identified
by calculating the 95th percentile of circuit theory movement costs associated with maximum
Euclidean dispersal distances of 1000 m and 5000 m (Bishop-Taylor et al. 2015). Distances of 1000 m
and 5000 m were selected to encompass the dispersal abilities of a range of common water-
dependent organisms within the MDB, including aquatic invertebrates (e.g., 99% of movements
below ~1 km for a widespread MDB freshwater crustacean; O’Connor 1986), amphibians (e.g., mean
maximum dispersal distance of ~2 km globally; Smith and Green 2005) and freshwater turtles (e.g.,
dispersal up to ~5 km for a common south-eastern Australian freshwater turtle; Roe et al. 2009).
Many studies have highlighted the importance of habitats with high network centrality for
maintaining and enhancing landscape connectivity (Urban and Keitt 2001, Estrada and Bodin 2008).
We used local graph centrality metrics to evaluate the potential importance of each unique habitat
(node) across each of the 99 seasonal time-steps. Degree centrality (DC) measures the numbers of
edges (connections) between a node and its immediate neighbours. In a habitat network, habitats
with particularly high DC values (e.g. in the top 1% of values) may serve as ‘hub’ habitats whose
location and spatial structure provide abundant opportunities for local dispersal (Estrada and Bodin
2008). Betweenness centrality (BC) quantifies the number of times a node occurs on the shortest
path between any two nodes in a network, with high values (e.g. top 1%) indicating potential
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‘stepping-stone’ habitats vital for enabling long-distance movements through a habitat network. We
calculated DC and BC on networks restricted to each of the two dispersal distances using the Igraph
Python module (Csardi and Nepusz 2006), using resistance distances as edge weights to ensure
shortest paths for BC ran through areas of least resistance to movement.
To identify potential stepping-stones and hubs, we mapped the top 1% of values for each metric and
seasonal time-step by ranking habitats from 0 (low importance) to 1 (high importance). This top 1%
cutoff was selected to identify a potentially manageable subset of important habitats given the large
number of total habitats identified in our study area (i.e. 277,874), and to approximate cutoffs used
to identify stepping-stones and hubs in previous dynamic connectivity studies (e.g. the top 0.7% or
20 out of a maximum of 2955 wetlands by DC and BC used to identify hubs and stepping stones by
Ruiz et al. 2014). Habitats were mapped separately for the driest 25%, average 25–75%, and wettest
25% of seasons by surface water area across the entire MDB to compare how the distribution of top
1% habitats varied spatially and in context of drought and flood. To evaluate whether stepping-
stones and hubs were under- or over-represented within various land-use types, we extracted data
for seven land-use categories based on the 2010–2011 Catchment Scale Land Use of Australia
(CLUM) dataset’s ‘Primary’ categories (ABARES 2014; Table 2) and identified the majority land-use
category for each unique ID habitat using the Extract to Table tool in ArcMap 10.3 (ESRI 2016).
Assuming that 1% of habitats within each land-use type would be in the top 1% of habitats by BC and
DC basin-wide if these important habitats were randomly distributed with respect to land use, we
then calculated the proportion of top 1% basin-wide habitats compared to all habitats within each
land use (i.e., a proportion greater than 1% indicated a higher than expected proportion of stepping-
stones and hubs within a specific land use, whereas a proportion less than 1% indicated stepping-
stones or hubs were underrepresented). This proportion was calculated separately for each seasonal
time-step, and plotted by driest to wettest seasons to evaluate how the representation of stepping-
stones or hubs per land-use category varied along a spectrum from dry to wet conditions.
Spatial distribution of stepping-stones and hubs
The distribution of important habitats for connectivity varied considerably both between dry,
average and wet seasons and by dispersal ability. During dry seasons, stepping-stones (habitats in
the top 1% of BC values) were largely restricted to permanent riverine corridors for both short and
long dispersal distances (i.e. ~1000 m and 5000 m, respectively; Figure 3). These regions included the
floodplains of the perennial River Murray and, to a lesser extent, the Murrumbidgee and Darling
where persistent waterbodies and wetlands supported long-distance connectivity even during dry
conditions. For both dispersal abilities, persistently important stepping-stones occurred along an
~650 km stretch of the Murray between the Ramsar-listed Coorong, Lower Lakes and Murray Mouth
lake and wetland complex (top 1% stepping-stones for up to 94 out of the total 99 seasonal time-
steps) to the confluence of the Murray-Murrumbidgee Rivers (up to 97 seasons), and within and
upstream of the Barmah-Millewa Ramsar site and wetlands of River Murray Reserve (up to 95
seasons). Although most dry-season stepping-stones for long-distance dispersers followed the main
channels of the Murray, Murrumbidgee and Darling Rivers, other important stepping-stones
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occurred within the upper reaches of the Gwydir, Namoi, Border Rivers and Condamine catchments
to the north-east of the MDB, and in highly ephemeral surface-water habitat within the Paroo River
catchment and the Greater Darling Anabranch (Figure 3).
During average and wet seasons, top 1% stepping-stone habitats expanded along the
Murrumbidgee, Darling and other smaller ephemeral river systems and agricultural regions in the
less modified northern MDB (Figure 3). Some of the largest increases in stepping-stone prevalence
for short-distance dispersing organisms occurred within the highly ephemeral and unregulated
Paroo basin. Although a small number of permanent habitats in the Paroo remained connected via
stepping-stones for long-distance dispersal organisms during averagely wet periods, these habitats
were highly disconnected for short-distance dispersers until large increases in connectivity during
wet conditions. Other ephemeral catchments including the Lachlan and Condamine-Balonne saw
large increases in connectivity during only the wettest seasons in the time series for both dispersal
distances but more pronounced for long-distance dispersal organisms. New stepping-stones during
wet periods also occurred away from main river channels within irrigated agricultural regions, such
as within the Riverina region where stepping-stones provided shortcuts for movement between the
floodplains of the upper Murray and Murrumbidgee Rivers (Figure 3). These regions typically served
as stepping-stones only for long-distance dispersers, with stepping-stones for less vagile organisms
being located in closer proximity to river channels.
Hub habitats were less consistent across time and displayed a more dispersed distribution compared
to stepping-stones (Figure 3). During dry seasons, habitats along the Murray and Darling Rivers
frequently served as both stepping-stones and hubs, particularly for short-dispersal organisms.
These habitats included the nationally significant Riverland Wetland Complex and Wallpolla Island
floodplain wetlands along the central Murray (top 1% hubs for 86 and 97 seasons, respectively) and
wetlands to the south of the Ramsar-listed Gunbower-Koondrook-Perricoota Forest group (75
seasons). During wet seasons, hubs for long-distance dispersers were more likely to occur across
extensive floodplain regions away from the immediate river channel, or within irrigated agricultural
regions (e.g., the Lachlan). The prevalence of hubs within irrigated agricultural areas also increased
during average or wet conditions, with some of the largest increases occurring in irrigated areas
within the Gwydir, Namoi, Border Rivers and Condamine catchments in the north-eastern MDB.
Connectivity in natural and modified landscapes
Conservation and natural environment land uses exhibited significantly higher than expected
proportions of stepping-stone habitats for both short- and long-distance dispersal organisms. During
dry seasons, conservation areas contained up to 9.5 times more top 1% BC habitats than expected
(i.e., 9.5% vs 1% expected), while natural environments contained up to 7.1 times (Figure 4).
Although these values decreased during increasingly wet seasons, conservation and natural
environments retained the highest proportion of stepping-stones of any land-use type across the
entire time series. Hub habitats were also consistently overrepresented within conservation areas
(up to 7.6 times more than expected), with no clear trend apparent with increasing inundated
habitat area aside from a gradual decrease for short-distance dispersal organisms across dry (~3.7%),
average (~1.7%) and wet conditions (~1.2%). Although natural environments also saw a steady
decrease in the proportion of stepping-stones during increasingly wet seasons, this proportion never
dropped significantly below the expected 1% of habitats.
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Artificial and modified land-use types typically contained fewer than expected stepping-stone or hub
habitats (Figure 4) despite hosting a far greater proportion of potential surface-water habitat (~87%
by area). Several land-use categories consistently showed significantly lower than expected
proportions of stepping-stones across the time series. This included dryland agriculture (including
cropping and grazing on modified pastures), artificial water (e.g., reservoirs and dams) and intensive
land uses (e.g., urban infrastructure and roads), which exhibited extremely small proportions of
stepping-stones for short-distance dispersers across dry, average and wet seasons (~0–0.5%
compared to 1% expected). Stepping-stone proportions improved marginally for long-distance
dispersers during increasingly wet seasons but remained significantly lower than expected (~0.3–
0.6%). Hub habitats were also underrepresented in these land-use types, although with greater
differences between dispersal distances (higher representation for long-distance dispersal) and with
higher and more variable proportions overall (i.e., typical values between 0 to 0.7% with occasional
seasons up to 2%).
The proportions of stepping-stones and hubs within production from natural environments (e.g.,
forestry and grazing on native vegetation) and irrigated agriculture were higher and showed
stronger relationships with inundated area compared to other modified land uses (Figure 4).
Although both land uses showed lower than expected proportions of stepping-stones and hubs
during dry seasons, increasingly wet conditions saw both stepping-stone and hub proportions
increase to above expected values. Although the increase in stepping-stone and hub proportions for
production from natural environments was modest (increases to 1.1–1.4% and 1.1–1.8% for
stepping-stones and hubs, respectively), the prevalence of this land use across much of the north
and western MDB was sufficient to account for the majority of decreases in proportions observed in
other land-use types during wet seasons (e.g., conservation and natural environments). Increases in
the importance of irrigated agricultural areas with flooding were far more extreme, with several
seasons distributed across the spectrum from average to wet showing extremely high proportions of
top hub habitats (up to 10.1% for long-distance and 3.8% for short-distance dispersal). Despite also
containing a disproportionately high number of stepping-stone habitats for long-distance dispersal
organisms during wet seasons (up to 3%), irrigated agricultural areas contained fewer than expected
stepping-stones and hubs for short-distance dispersers across the majority of the time series.
Spatial distribution of stepping-stones and hubs
Landscape connectivity plays a key role in maintaining biodiversity within dynamic dryland
freshwater ecosystems (Davis et al. 2015, Murphy et al. 2015). Conservation and management
approaches aimed at maximising connectivity are likely to grow in importance as habitat loss and a
changing climate require water-dependent organisms to move long distances in search of suitable
habitat or ecological refuges (Nuñez et al. 2013, Davis et al. 2015). However, changing land-use
patterns, intensifying water resource demands and increasingly extreme surface-water dynamics
require approaches to maintaining or enhancing connectivity that account for and embrace rapid
environmental change and temporal variability (Bond et al. 2008). In this study, we used a
comprehensive and spatiotemporally consistent graph theory network analysis approach to model
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landscape connectivity between potential surface-water habitats within Australia’s MDB, a highly
modified semi-arid region containing some of Australia’s most biologically significant wetland
ecosystems (Rogers and Ralph 2010, Pittock and Finlayson 2011). We processed over 5.4 million
nodes across 99 seasonal time-steps and two dispersal abilities, making our study to our knowledge
the largest spatially explicit ecological network analyses yet conducted.
We identified a subset of potential habitats that facilitate high levels of connectivity both at local
(hubs) and at regional or network scales (stepping-stones). Prioritising surface-water habitats for
conservation based on their potential for improving connectivity through habitat networks may
provide an opportunity for maximising benefits from limited conservation funding and reducing
conflict in regions like the MDB faced with changing land-use patterns and increasing demands for
water resources. In the MDB, the importance of surface-water habitats as connectivity providers
depended on both their spatial position in a habitat network and their persistence and dynamics
over time. Large, persistent hub habitats such as those within the Coorong, Lower Lakes and Murray
Mouth lake and wetland complex may serve as important ecological refuges, allowing organisms to
survive periods of extreme environmental conditions, including drought. However, landscape
connectivity and processes of dispersal into and out of habitats during and after disturbances are
critical to refuge function (Chester and Robson 2013). Persistent habitats with highly suitable local-
scale habitat attributes (e.g., dense aquatic or riparian vegetation and a lack of predators) may be
unable to serve as refuges if they are functionally isolated from other habitats either by geographic
distance or by anthropogenic barriers (Sheldon et al. 2010, Davis et al. 2013). Conversely, hub
habitats may be less likely to serve as functional refuges if they are present in the landscape for a
short period, or if they are highly degraded or otherwise unsuitable for organisms to establish
populations or reproduce. Our results therefore do not reduce the importance of collecting site-
scale ecological data, but can complement field studies with insights on habitat importance informed
by a habitat’s context within its larger-scale habitat network.
Stepping-stone habitats that support long-distance dispersal though habitat networks will perform
different ecological roles depending on their permanence and relationship with flooding (Bishop-
Taylor et al. 2015). Many stepping-stone habitats in the MDB were located within riverine
floodplains, where a matrix of permanent and temporary habitats facilitated longitudinal
connectivity during even periods of severe drought. By providing connections between clusters of
habitats during dry seasons, these stepping-stones may enhance the ability of locally well-connected
hubs to serve as functional ecological refuges. This was particularly apparent along the lower Murray
River, where perennial stepping-stone habitats provided continuous opportunities for movement
between persistent hub habitats within the Lower Lakes and the floodplains of the central Murray.
However, other more ephemeral stepping-stones appeared during major flooding across extensive
natural floodplains of the northern MDB including within the Condamine-Balonne and Paroo
catchments. By creating both new temporary surface-water habitat and stepping-stones connecting
existing isolated habitats, transient flooding events in these regions are likely to play a key role in
maintaining regional-scale landscape connectivity.
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Connectivity in conservation and natural environments
Conservation areas and natural environments in the MDB contained significantly higher than
expected proportions of important stepping-stones and hubs throughout almost all seasonal time-
steps in our 25-year time series. Many of these regions contain surface-water habitats already
regarded as significant at a national (e.g., Directory of Important Wetlands in Australia) or global
(i.e., Ramsar Wetlands of International Importance) scale, of which some have served as previous
targets for the allocation of environmental water aimed at maintaining or improving ecological
health of habitats stressed by drought. Along the Murray River, several of the stepping-stones and
hubs identified by this study (e.g., within the Lower Lakes, Coorong and Murray Mouth, Gunbower-
Koondrook-Perricoota Forests, Lindsay-Wallpolla-Mulcra Islands and along the River Murray
Channel) have been allocated proportions of over 500 gigaliters of environmental water under The
Living Murray program, in part to improve connectivity between floodplain and waterways and
maintain ecological refuges during the worst of the 1999–2010 Millennium Drought (MDBC 2005).
The provision of strategic environmental flows to high-priority stepping-stone and hub habitats will
be important to maintain ecological connections between both local and distant clusters of habitats,
and may assist in greatly reducing the adverse impacts of severe drought on water-dependent
species (Arthington 2012).
The high importance of conservation and natural environments corroborates previous static
connectivity modelling in the Murray-Darling that found individual habitats located within protected
areas displayed higher BC and DC values than unprotected habitats, especially during large modelled
flooding scenarios (Bishop-Taylor et al. 2015). However, our dynamic study showed that while
individual habitats within conservation areas may be important for maintaining connectivity during
flooding, they represent an increasingly small proportion of total stepping-stones and hubs across
the entire MDB during wet seasons. This is likely to reflect two major underlying processes: a basin-
wide shift in important habitats towards the less-modified northern MDB during wet periods, and a
local shift away from immediate river channels and into more extensive floodplain environments.
Conservation areas cover a relatively small proportion of the MDB (~7.5% by area), with protected
rivers and wetlands exhibiting a strong bias towards the southern MDB (Bino et al. 2016). Although
this spatial distribution provides disproportionally high protection to persistent stepping-stones and
hubs located along the floodplains of the Murray River, conservation areas provide less protection
for more transiently important habitats that appear across the northern MDB during major
inundation events.
Increasing the regional- or continental-scale effectiveness of the Australian protected area network
for preserving freshwater biodiversity is likely to require an increase in the representation of
stepping-stones and hubs that are important for facilitating landscape connectivity across the
northern MDB. Many of these surface-water habitats are located in relatively natural environments
used predominantly for grazing and other forms of low intensity production, and so may still provide
local-scale habitat attributes required to support populations of water-dependent species, provided
they are protected from future land-use intensification (Chester and Robson 2013). Where protected
area listings are not compatible with increasingly intensive land uses, increased management may be
required to ensure that potential stepping-stones and hubs can support long-distance movement
through ecological networks, or serve as ecologically functional refuges. A range of management
techniques have shown promise in improving the suitability of artificial surface water to serve as
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ecological refuges or stepping-stones. These include promoting riparian and aquatic plant growth by
reducing the steepness of dam or canal edges (e.g., Robson and Clay 2005, Hamer et al. 2012),
fencing to minimise livestock erosion (e.g., Markwell and Fellows 2008, Canals et al. 2011) and
attempts to reduce predation by removing introduced fish species (e.g., Vredenburg 2004). The
stepping-stones and hubs identified by this study may provide priority targets for management,
allowing limited conservation funding to be allocated to habitats that serve as the most effective
connectivity providers.
Connectivity in modified environments
Surface water within irrigated agricultural areas in the MDB served as important stepping-stones and
hubs during average and wet seasons, but predominantly for organisms with long-distance dispersal
abilities. These landscapes were typically characterised by a shifting mosaic of uniformly distributed,
temporarily flooded fields and irrigation canals. Although this habitat structure did not prevent inter-
patch movement for long-distance dispersers, potential irrigated agricultural habitats remained
disconnected for short-distance dispersal organisms less able to move between the spatially
dispersed areas of surface water present at any moment in time. Other less transient artificial water
sources (i.e., farm dams and reservoirs) also displayed significantly lower than expected connectivity
across the majority of the time series, particularly for short dispersal distances (Figure 4). A similar
result was observed by Uden et al. (2014) in a graph theory study of amphibian habitat networks in
the Rainwater Basin (central USA), where smaller, shorter-distance dispersers displayed sharply
reduced connectivity in agricultural landscapes dominated by uniformly distributed irrigation water
storage pits.
These findings suggest that the regular, dispersed spatial structure of surface-water habitats in
highly modified agricultural landscapes disproportionately affect connectivity for short-distance
dispersers compared to the tightly clustered matrix of ephemeral and permanent floodplain and
wetland habitats they have replaced. Although organisms with long-distance dispersal abilities (i.e.,
dispersal distances of ~5000 m and above) may be able to successfully move through these
landscapes, less vagile organisms may suffer increased genetic isolation and heightened risks of local
extinction events due to reduced opportunities for dispersal (Johst et al. 2002). Hydrologic
management practices (i.e., maintaining a subset of fields flooded during fallow phases) aimed at
mimicking natural mosaics of temporary and permanent habitats have shown success in maximising
aquatic macroinvertebrate biodiversity (e.g., Stenert et al. 2009), and may be required to enhance
the persistence of short-distance dispersal species in these modified landscapes. In addition to
enhancing connectivity, managing artificial surface-water hydroperiods may also provide ecological
benefits at site scales, including ensuring that during even dry periods aquatic organisms such as
amphibians or invertebrates have continuous access to relatively persistent surface water in which
to complete larval or juvenile stages of their lifecycles (Wassens et al. 2008, Canals et al. 2011).
Limitations and future work
By allowing us to model spatiotemporal connectivity dynamics consistently across space and over a
~25-year period, our application of remotely sensed surface-water datasets advances previous graph
theory network analyses that relied on modelled flooding scenarios (e.g., Bishop-Taylor et al. 2015)
or historical and hypothetical future wetland maps (e.g., Uden et al. 2014). Nevertheless, some
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important caveats in our approach should be noted. As our work was based on remote sensing
datasets, our findings are conditional on both the accuracy and availability of input data. This is
particularly significant for graph theory network analyses that can be highly sensitive to variation in
the number and distribution of nodes (i.e., habitats) used to model connectivity (Butts 2009). The
surface-water time series we used to define potential habitats through time was subject to a
statistically rigorous accuracy assessment (Tulbure et al. 2016), with a high resulting accuracy for
surface water during both wet and dry years (producer's accuracy of 87% ± 3% SE). In addition,
temporally aggregating surface water by ensuring that a cell was flooded for at least 50% of each
seasonal time-step is likely to have significantly reduced errors of commission by eliminating noisy
pixels that may have only occurred once throughout the entire time series.
Where minimal satellite coverage or extended presence of clouds resulted in areas with no data that
persisted throughout an entire season, we used a conservative gap-filling method in which cells
were filled based on the nearest seasons with data. As reported by Tulbure et al. (2016), large areas
of no data in the MDB surface-water time series were concentrated in a minority of (mostly winter)
seasons towards both ends of the time series, minimising their influence on the analysis. The
resulting surface-water layers are expected to be far more accurate than previous static layers used
to model connectivity in the MDB, such as the Geoscience Australia (GA 2006) topographic
waterbody features used by Bishop-Taylor et al. (2015), which have coarse resolution (1:250,000), a
highly variable provenance (1965–2004 in a single static dataset) and inconsistent attribution (Bino
et al., 2016). Our use of freely available satellite data with global coverage (i.e., Landsat TM and
ETM+) also ensures our approach is generalizable to other regions, allowing our findings to be
compared and assessed consistently against other dynamic surface-water environments.
To focus on identifying habitats whose location within surface-water habitat networks was
important for facilitating landscape connectivity, we limited this study to using centrality metrics (DC
and BC) that quantified only network topology or structure. However, our flexible graph theory
framework supports future approaches that could make use of advanced metrics which additionally
incorporate habitat quantity, quality or the relative probabilities of movement between two
habitats. In particular, metrics based on the concept of habitat availability or reachability including
the Integral Index of Connectivity and Probability of Connectivity have been empirically shown to
provide information on the importance of habitats for connectivity that complements and improves
upon centrality methods (Saura and Pascual-Hortal 2007, Saura et al. 2011). Combining dynamic
surface-water data with these advanced methods would allow interactions between flooded area
and regional-scale connectivity to be compared holistically across time and space, potentially
providing valuable insights into the structure and resilience of natural and artificial surface-water
habitat networks during periods of extreme hydroclimatic variability.
This work was funded through an Australian Research Council Discovery Early Career Researcher
Award (DE140101608) to Tulbure. We thank B McRae for his timely assistance with the Circuitscape
software package.
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Data Availability
Data associated with this paper have been deposited in the Dryad digital repository
Table 1 Resistance to movement values used to develop resistance surfaces (based on GA
topographic features and Australian Dynamic Land Cover Dataset v1.0 classes; GA 2006, Lymburner
et al. 2010)
Value Resistance Example DLCD land cover or GA topographic features
1 Very low
Surface water (including seasonal maximum flooding
extent), aquatic vegetation
2 Low Closed natural vegetation, irrigated cropping and pasture
4 Moderate Open natural vegetation, dryland pasture
8 Moderate-high Sparse natural vegetation, dryland cropping, minor roads
16 High Non-vegetated areas, major roads
32 Very high Urban infrastructure and highways, saline waterbodies
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Table 2 Land-use categories used to compare distributions of stepping-stones and hubs, based on
the 2010–2011 Catchment Scale Land Use Map (CLUM) classes (ABARES 2014)
Land-use category CLUM codes Example CLUM land uses
Conservation 1.1.1–1.1.7 Protected areas
Natural environments 1.2.0–1.3.4, 6.0.0–6.1.3,
6.3.0–6.3.3, 6.5.0–6.6.3
Non-protected but natural
vegetation or surface water
Production from natural
environments 2.1.0–2.2.2 Grazing on relatively natural
vegetation, forestry
Dryland agriculture 3.0.0–3.6.4 Dryland cropping, modified
Irrigated agriculture 4.0.0–4.6.3 Irrigated cropping and horticulture
Intensive uses 5.0.0–5.9.5 Urban areas, mining
Artificial water 6.2.0–6.2.3, 6.4.0–6.4.3 Reservoirs, farm dams
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Figure 1 Murray-Darling Basin study area, showing the distribution and frequency of potential
surface-water habitats across the entire 99 season time series (dark blue indicates persistent
habitats; Tulbure et al. 2016). Numbered annotations highlight significant regions of the MDB
discussed in this study: (1) Lower Lakes, Coorong and Murray Mouth; (2) the River Murray Channel
including Riverland Wetland Complex and Lindsay-Wallpolla-Mulcra Islands; (3) Gunbower-
Koondrook-Perricoota and Barmah-Millewa Forests; (4) Greater Darling Anabranch; (5) the Riverina
agricultural region; (6) irrigated agriculture and floodplains of the Condamine, Gwydir, Namoi and
Border Rivers catchments; and (7) floodplains of the Condamine-Balonne catchment
Figure 2 Example of process followed to break potential habitat areas into discrete ‘communities’ of
raster pixels (unique ID regions or graph nodes) that consistently occurred together across time. For
all neighbouring pairs of pixels within the study area, we assigned weights depending on their
asynchrony across the entire 1987–2011 time series (A). These weights (B; yellow to red indicating
pixel pairs that rarely occurred together in a single time-step) were then used to identify (C)
potential unique ID regions (graph nodes) that typically occurred as discrete areas of habitat except
during the largest flooding events.
Figure 3 Distribution of top 1% stepping-stones and hubs across the MDB. Important habitats are
shown separately for two dispersal abilities (short-distance, ~1000 m; long-distance, ~ 5000 m) and
the driest 25%, average (25–75%) and the wettest 25% of seasons by inundated habitat area.
Figure 4 Top 1% stepping-stones and hubs basin-wide as a proportion of all habitats within each
land-use category in the MDB. Proportions are plotted against inundated habitat area (percent rank)
for two dispersal abilities (short-distance in orange, ~1000 m; long-distance in blue, ~ 5000 m), and
compared against the expected proportion of 1% (values above 1% indicate more stepping-stones or
hubs than expected within a land-use category).
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... In recent years, graph metrics have been used to assess the connectivity of a habitat network and the important role habitat patches play in the entire landscape network [34,35]. For example, the node degree (Dg) and betweenness centrality (BC) metrics have been used at the local level to identify hubs and potential stepping stone nodes for the entire landscape network [34][35][36]. ...
... In recent years, graph metrics have been used to assess the connectivity of a habitat network and the important role habitat patches play in the entire landscape network [34,35]. For example, the node degree (Dg) and betweenness centrality (BC) metrics have been used at the local level to identify hubs and potential stepping stone nodes for the entire landscape network [34][35][36]. The integral index of connectivity (IIC) is an improved probability of connectivity (PC) metric that was proposed for and has been computed at the global and component levels to assess the level of connectivity within an entire habitat network and compare different component connectivity values [37]. ...
... The cost value (Table 4) was generated by the multi-factor weighted overlay analysis method including the landscape moisture of different land covers [46], ease of conversion from different land use to surface water body during the study period, and the topographic characteristics (slope and elevation) of this region. Each factor was mapped into six exponentially increasing resistance classes [34]. The weight value of different factors was assigned according to the influence of topography and land cover on the development of the surface water body. ...
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An understanding of the scientific layout of surface water space is crucial for the sustainable development of human society and the ecological environment. The objective of this study was to use land-use/land-cover data to identify the spatiotemporal dynamic change processes and the influencing factors over the past three decades in Henan Province, central China. Multidisciplinary theories (landscape ecology and graph theory) and methods (GIS spatial analysis and SPSS correlation analysis) were used to quantify the dynamic changes in surface water pattern and connectivity. Our results revealed that the water area decreased significantly during the periods of 1990–2000 and 2010–2018 due to a decrease in tidal flats and linear waters, but increased significantly in 2000–2010 due to an increase in patchy waters. Human construction activities, socioeconomic development and topography were the key factors driving the dynamics of water pattern and connectivity. The use of graph metrics (node degree, betweenness centrality, and delta probability of connectivity) in combination with landscape metrics (Euclidean nearest-neighbor distance) can help establish the parameters of threshold distance between connected habitats, identify hubs and stepping stones, and determine the relatively important water patches that require priority protection or development.
... Surface water extent over broad spatial scales is usually derived from satellite imagery (Hermoso, Ward & Kennard, 2013;Bishop-Taylor, Tulbure & Broich, 2017b), but this has several limitations for conservation prioritization in dendritic stream networks. The relatively coarse spatial resolution of satellite imagery (e.g. 30 m for Landsat TM/ETM+ imagery) and the often dense riparian vegetation cover over stream channels mean that detection of surface water and identification of aquatic refuges may be difficult in some stream networks. ...
... The relative position of stream segments within dendritic stream networks is ecologically important for the successful dispersal of organisms from refuges and the recolonization of stream networks (Er} os, Schmera & Schick, 2011). Topological indices derived from graph theory, such as 'betweenness centrality' (BC) (Jordán, Liu & Davis, 2006) and the integral index of connectivity (Pascual-Hortal & Saura, 2006), have been proposed to measure the positional importance of a given segment in a stream network from the viewpoint of connectivity and show promising performance in informing priority stream segments for riverscape conservation (Segurado, Branco & Ferreira, 2013;Bishop-Taylor, Tulbure & Broich, 2017b). ...
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1. The hydrological variability of intermittent streams means that the spatial distribution of dry-season aquatic refuges within river networks and the temporal dynamics of hydrological connectivity between them are critical for the persistence of aquatic biodiversity. Here, a new approach is demonstrated to identify surface water bodies as priority refuges for efficient conservation management of freshwater biodiversity in intermittent stream networks. 2. Recently developed models of surface water extent and daily streamflow were used to represent spatio-temporal variations in hydrological connectivity and surface water persistence within river networks of eastern Australia over a 107-yr period. Using this information, systematic conservation planning was applied to prioritize aquatic areas for conservation of 25 fish species under two scenarios. One scenario identified priority refuges to complement those already occurring in protected areas, whereas the other did not consider protected area status. 3. The priority networks identified concentrated on the main stems of river catchments where surface water was more likely to be persistent and aquatic refuges were more likely to be connected, but also included headwaters for rare fish species. All three set conservation targets for the 25 fish species can be met in the best solution of priority networks. Although the second scenario achieved the targets with a smaller size of priority network overall, it required more new aquatic refuges and was thus less efficient than the first scenario. 4. The newly developed datasets are useful for freshwater conservation prioritization because they account for hydrological variability of intermittent streams. The systematic prioritization approach applied is transferable to other regions and freshwater taxa to identify aquatic refuges for biodiversity conservation within intermittent stream systems.
... An understanding of pool surface-water availability at the reach scale is important because river reaches and their associated sub-catchments are usually the management unit that catchment managers use when making management prioritisation decisions, such as for conservation management (Hermoso et al., 2013). While surface water extent can be identified by methods including satellite imaging (Bishop-Taylor et al., 2017), statistical modelling (Yu et al., 2019), and on-ground mapping (Moidu et al., 2021), the application of these methods can be constrained due to a number of issues. The application of satellite imaging is limited to wide river channels with little vegetation canopy, statistical modelling often fails to reflect the continuous decrease of surface water extent within rivers in the absence of rainfall, and on-ground mapping is difficult to scale up to a large area. ...
Persistent surface water pools within non-perennial streams provide critical refuges for freshwater species by facilitating their survival during extended dry periods. However, our understanding of how pool water levels recede is limited, hindering our ability to quantify and predict aquatic refuge persistence. We characterise variations in water-level recession rates based on one year of water-level measurements in five non-perennial streams. Most of the observations show that the water level recedes at a constant rate after streamflow ceases in our study streams, a rate often significantly higher than that during low-flow periods. However, water-level recession rates varied considerably amongst cease-to-flow periods within pools in the same non-perennial stream and between nearby streams. The constancy of water-level recession during a cease-to-flow period is applicable to other non-perennial streams, but a detailed understanding of the factors influencing recession rates is needed for prediction in ungauged streams and identifying persistent aquatic refuges across river networks.
... By using graph theoretic metrics, several studies have evaluated the fragmentation of habitats, caused either by climate variability or by anthropogenic pressures, and their associated ecological implications (Bishop-Taylor et al., 2018;McIntyre et al., 2014;Søndergaard & Jeppesen, 2007;Wright, 2010). Combined with remote sensing, graph theoretic metrics have also facilitated the measurement of floodplain habitat networks over larger spatial extents (Bishop-Taylor et al., 2017;Ruiz et al., 2014;Wright, 2010). While graph theoretic metrics provide a way to quantify potential impacts of disrupted surface water dynamics in connectivity, developing new approaches that also account for ecological processes may help estimate the potential impact of such disruptions on broader ecosystem functioning. ...
1. Floodplain wetlands provide an important subsidy for riverine food webs as sites of high algal production. However, this subsidy depends on the degree of landscape connectivity during flood pulses, which provides the opportunity for movement of higher order consumers between rivers and floodplains to access these productive habitats. Changes in floodplain inundation extent and dura-tion, due to variable wet season flows or water resource development (WRD), can impact landscape connectivity and ultimately the magnitude of the food web subsidy. 2. We explored landscape connectivity using graph theory and derived four new metrics to measure how different flow scenarios can affect connectivity and algal production. We considered a historic scenario with the present level of water resource development in our study area, the Mitchell River, and a WRD scenario with the inclusion of three new dams in the catchment. We generated 240 unique daily spatial graphs, using surface water inundation maps across 40- day flood events to compare a dry year (2006), an average flow year (2001) and a wet year (2009) with and without a WRD scenario. 3. Drier years and WRD scenario resulted in floodplain fragmentation, potentially constraining the movement of higher order consumers. Changes in connectivity due to WRD resulted in predicted reductions of up to 26% of algal production on the floodplain that was otherwise connected to the main river channels. 4. Synthesis and applications. The approach developed in this study provides new metrics to identify how changes in floodplain surface water extent due to water resource development and climatic variation may impact ecosystem function such as connectivity that facilitates access of higher order consumers to primary production in floodplain wetlands. With a direct link to river flow alteration, these metrics can inform catchment planning and management to ensure that the conservation of floodplain ecosystem functions is adequately considered in water resource management decisions.
... Ecological sources are defined as habitats that support species survival and outward dispersal (i.e., sources vs sinks) [17]. We referred to the research carried out by Bishop-Taylor et al., who investigated landscape connectivity for aquatic habitats in the Murray-Darling Basin [14,30]. The ecological source in their study was "water source", with the area of surface water patches used for water source identification. ...
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The sustainability of wetlands is threatened by the past and present land use practices. Hydrological connectivity is one of the most important aspects to consider for wetland rehabilitation planning purposes. Circuit theory and connectivity indices can be used to model and assess hydrological connectivity. The aim of this study was to assess spatiotemporal variation in the hydrological connectivity of the Zoigê area from 2000–2019 using both methods. The study area contains a Ramsar wetland of international importance, namely the Sichuan Ruoergai Wetland National Nature Reserve. We used a global surface water observation product as the major input for both methods, and then analyzed the temporal and spatial characteristics, in terms of important components and patches. We found that the overall connectivity has increased slightly in the last 20 years, while the probability of connection between patches of surface water has increased significantly. Important components and patches represent steppingstone habitat for the dispersal of organisms in the landscape. The main determinants of hydrological connectivity are mostly human oriented, predominantly a decrease in large livestock population size and population increase.
... Here, the distance between patches is the cost-weighted distance derived from the least-cost modelling [17], to consider the spatial heterogeneity of impedance of the landscape (see Supplementary Table S2 for the cost of each land-use type). Some studies noticed that the landscape impedance may vary with time [44,45], hence such variations were also taken into account. Given that detailed information about when land-use changes occur during t 1 and t 2 is unavailable, we obtained the spatiotemporal cost surface by taking the average between the cost surfaces in t 1 and t 2 . ...
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Landscape connectivity is important for all organisms as it directly affects population dynamics. Yet, rapid urbanization has caused serious landscape fragmentation, which is the primary contributor of species extinctions worldwide. Previous studies have mostly used spatial snap-shots to evaluate the impact of urban expansion on landscape connectivity. However, the interactions among habitats over time in dynamic landscapes have been largely ignored. Here, we demonstrated that overlooking temporal connectivity can lead to the overestimation of the impact of urban expansion. How much greater the overestimation is depends on the amount of net habitat loss. Moreover, we showed that landscape connectivity may have a delayed response to urban expansion. Our analysis shifts the way to understand the ecological consequences of urban expansion. Our framework can guide sustainable urban development and can be inspiring to conservation practices under other contexts (e.g., climate change).
The development of road networks over the years has caused serious damage to biodiversity. However, few studies have explored the impact of different road grades on ecological network connectivity, especially at multiple levels and at different dispersal distances. Here, we propose an analytical framework based on the integrated graph theory and the circuit theory method, in order to model the ecological network of virtual species, to evaluate connectivity at the landscape, patch, and corridor levels, and to identify the key patches and key corridors that contribute the most to the maintenance of connectivity. The empirical analysis in this study was performed on six scenarios, which were designed by successively integrating different road grades into the landscape. On this basis, the impact of different road grades on the connectivity, key patches, and key corridors in Wuhan, China, were explored. The results showed that: (1) High-grade roads have a significant impact on landscape-patch-corridor connectivity, while medium-grade roads have a similar degree of impact on patch-level connectivity as high-grade roads do. (2) Species with long dispersal ability (25 km) are susceptible to roads at the landscape and corridor levels; species with low and medium dispersal abilities (10, 15 and 20 km) are vulnerable to roads at the patch levels. (3) The importance of key patches and the resistance of key corridors are significantly increased by the influence of roads, while their spatial distribution changes slightly. This integrated framework contributes to an evaluation of the impacts of different grades road on ecological processes, so as to better provide targeted suggestions for biodiversity conservation and transportation planning.
Maintaining and enhancing landscape connectivity reduces biodiversity declines due to habitat fragmentation. Uncertainty remains, however, about the effectiveness of conservation for enhancing connectivity for multiple species on dynamic landscapes, especially over long time horizons. Focusing on central North Carolina, we forecast connectivity under four common conservation strategies‐ acquiring the lowest cost land, acquiring land clustered around already established conservation areas, acquiring land with high geodiversity characteristics, and acquiring land opportunistically‐ on a dynamic landscape incorporating forest growth and succession, disturbance, and management from 2020 to 2100. We used graph theoretic metrics to quantify landscape connectivity across these four strategies, evaluating connectivity for four ecologically relevant species guilds, representing endpoints along a spectrum of vagility and habitat specificity: long‐ vs. short‐distance dispersal ability and habitat specialists vs. generalists. Our results indicate that landscape connectivity will improve for specialist species under any conservation strategy employed, although these increases were highly variable across strategies. For generalist species, connectivity improvements were negligible. Overall, clustering the development of new protected areas around land already designated for conservation yielded the largest improvements in connectivity, with increases of several orders of magnitude beyond current landscape connectivity for both long‐ and short‐distance dispersing specialist species. Conserving the lowest cost land showed the smallest contributions to connectivity. Our approach provides insight into the connectivity contributions of a suite of conservation alternatives prior to on‐the‐ground implementation, and therefore can inform connectivity planning to maximize conservation benefit. This article is protected by copyright. All rights reserved
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Spatiotemporal quantification of surface water and flooding is essential given that floods are among the largest natural hazards. Effective disaster response management requires near real-time information on flood extent. Satellite remote sensing is the only way of monitoring these dynamics across vast areas and over time. Previous water and flood mapping efforts have relied on optical time series, despite cloud contamination. This reliance on optical data is due to the availability of systematically acquired and easily accessible optical data globally for over 40 years. Prior research used either MODIS or Landsat data, trading either high temporal density but lower spatial resolution or lower cadence but higher spatial resolution. Both MODIS and Landsat pose limitations as Landsat can miss ephemeral floods, whereas MODIS misses small floods and inaccurately delineates flood edges. Leveraging high temporal frequency of 3–4 days of the existing Landsat-8 (L8) and two Sentinel-2 (S2) satellites combined, in this research, we assessed whether the increased temporal frequency of the three sensors improves our ability to detect surface water and flooding extent compared to a single sensor (L8 alone). Our study area was Australia’s Murray-Darling Basin, one of the world’s largest dryland basins that experiences ephemeral floods. We applied machine learning to NASA’s Harmonized Landsat Sentinel-2 (HLS) Surface Reflectance Product, which combines L8 and S2 observations, to map surface water and flooding dynamics. Our overall accuracy, estimated from a stratified random sample, was 99%. Our user’s and producer’s accuracy for the water class was 80% (±3.6%, standard error) and 76% (±5.8%). We focused on 2019, one of the most recent years when all three HLS sensors operated at full capacity. Our results show that water area (permanent and flooding) identified with the HLS was greater than that identified by L8, and some short-lived flooding events were detected only by the HLS. Comparison with high resolution (3 m) PlanetScope data identified extensive mixed pixels at the 30 m HLS resolution, highlighting the need for improved spatial resolution in future work. The HLS has been able to detect floods in cases when one sensor (L8) alone was not, despite 2019 being one of the driest years in the area, with few flooding events. The dense optical time-series offered by the HLS data is thus critical for capturing temporally dynamic phenomena (i.e., ephemeral floods in drylands), highlighting the importance of harmonized data such as the HLS.
The inundation state is one of the main factors affecting the development of river floodplain wetlands, and the water inundate frequency (WIF) is a commonly used characterisation tool. This study combines remote sensing data with ground hydrological observation data to propose the water inundate guarantee rate (WIGR) as a new tool to characterise the inundation probability in river floodplain areas. It further conducts a comparative study between WIGR and WIF in the Yimin River, located in China's Inner Mongolia Autonomous Region. The results show that the inundation state maps of the study area are generally consistent using WIF and WIGR. However, there are observed differences in specific regions. In regions with very low assurance rates, it covers a larger area of 2.90 km² using WIGR relative to WIF. Meanwhile, The area of other low, medium, high and very high areas is relatively lower. The reason for the difference is that the selected 52 phase remote sensing data corresponds to a low flow guarantee rate, which is generally medium and high. Further, the flow guarantee rate introduced in the WIGR analysis process plays a corrective role in the final analysis results. Compared with WIF, the WIGR algorithm is systematic, continuous, and has certain statistical significance, which has higher scientific credibility and accuracy. Specifically, the simple operation of the WIGR algorithm shows great potential to be applied to other related studies. Nevertheless, further improvement of the WIGR algorithm should focus on the influence of the atmospheric environment on remote sensing data and the acquisition of hydrological data. The WIGR proposed in this study can provide technical support for the conservation of river floodplain wetlands and open new ideas for studying river floodplain wetland ecosystems.
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The contiguous United States contains a disconnected patchwork of natural lands. This fragmentation by human activities limits species’ ability to track suitable climates as they rapidly shift. However, most models that project species movement needs have not examined where fragmentation will limit those movements. Here, we quantify climate connectivity, the capacity of landscape configuration to allow species movement in the face of dynamically shifting climate. Using this metric, we assess to what extent habitat fragmentation will limit species movements in response to climate change. We then evaluate how creating corridors to promote climate connectivity could potentially mitigate these restrictions, and we assess where strategies to increase connectivity will be most beneficial. By analyzing fragmentation patterns across the contiguous United States, we demonstrate that only 41% of natural land area retains enough connectivity to allow plants and animals to maintain climatic parity as the climate warms. In the eastern United States, less than 2% of natural area is sufficiently connected. Introducing corridors to facilitate movement through human-dominated regions increases the percentage of climatically connected natural area to 65%, with the most impactful gains in low-elevation regions, particularly in the southeastern United States. These climate connectivity analyses allow ecologists and conservation practitioners to determine the most effective regions for increasing connectivity. More importantly, our findings demonstrate that increasing climate connectivity is critical for allowing species to track rapidly changing climates, reconfiguring habitats to promote access to suitable climates.
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Australia has diverse wetlands with multiple threats. We reviewed knowledge about the extent of wetlands, representativeness, impacts and threats to integrity and options for effective conservation. Natural Australian wetlands cover an estimated 33 266 245 ha (4.4%), with 55% palustrine (floodplains and swamps), followed by 31% lakes, 10% estuarine systems, and 5% rivers and creeks. The Lake Eyre (1.1%), Murray–Darling (0.73%), Tanami–Timor Sea Coast (0.71%) and the Carpentaria Coast (0.55%) drainage divisions have more wetlands, also reflected in the distributions among states and territories. Ramsar sites and wetlands in protected areas were generally biased towards the southern continent. Overall representation of mapped wetlands was good for lacustrine (40.6%) and estuarine (34.4%), fair for riverine (16.8%), but inadequate for palustrine (10.8%) wetlands. Within drainage divisions, representation varied considerably, with shortfalls from the Aichi target of 17%. Agriculture, urbanisation, poll
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Conservation planning and biodiversity management require information on landscape connectivity across a range of spatial scales from individual home ranges to large regions. Reduction in landscape connectivity due changes in land use or development is expected to act synergistically with alterations to habitat mosaic configuration arising from climate change. We illustrate a multiscale connectivity framework to aid habitat conservation prioritization in the context of changing land use and climate. Our approach, which builds upon the strengths of multiple landscape connectivity methods, including graph theory, circuit theory, and least-cost path analysis, is here applied to the conservation planning requirements of the Mohave ground squirrel. The distribution of this threatened Californian species, as for numerous other desert species, overlaps with the proposed placement of several utility-scale renewable energy developments in the American southwest. Our approach uses information derived at three spatial scales to forecast potential changes in habitat connectivity under various scenarios of energy development and climate change. By disentangling the potential effects of habitat loss and fragmentation across multiple scales, we identify priority conservation areas for both core habitat and critical corridor or stepping stone habitats. This approach is a first step toward applying graph theory to analyze habitat connectivity for species with continuously distributed habitat and should be applicable across a broad range of taxa.
• In dryland environments, freshwater ecosystems often suffer extensive degradation through habitat modification and water regime changes. The Macquarie Marshes are located in the Murray–Darling Basin in south‐eastern Australia. They are an example of an ecosystem that has experienced significant degradation in recent decades owing to upstream water diversions. Recent reforms of water management in the Murray–Darling Basin have attempted to balance environmental needs against consumptive uses of water. • This paper examines the role a protected area has played in conserving the Macquarie Marshes freshwater ecosystem and discusses how Murray–Darling Basin water reform, together with social expectations of public land managers, have had impacts on the management of the Macquarie Marshes Nature Reserve. • Protected areas may limit adverse impacts on habitat associated with local‐scale agricultural production but struggle without formal water management arrangements to protect water‐dependent ecosystems from threats operating at a catchment scale. Protected area managers balance demands from stakeholders to address local issues, such as fire management and geomorphic changes, against contributing to the achievement of environmental goals in a water planning system that operates at the catchment scale. • European settlement and water resource development has marginalized local Aboriginal people from any role in land and water management activities. The water reforms have given them more opportunities for involvement in decision‐making processes, but opportunities for increased access to water for cultural and spiritual purposes remain limited. Protected areas offer a means of providing greater access for Aboriginal people to their ancestral lands. • The strategies and processes developed to maintain and enhance the Macquarie Marshes exemplify the evolution in understanding of freshwater ecosystem protection in Australia, and are relevant globally to water resource management in complex social‐ecological systems. Copyright © 2016 John Wiley & Sons, Ltd.
Human activities are altering the processes that connect organisms within and among habitats and populations in marine and freshwater (aquatic) ecosystems. Connectivity can be quantified using graph theory, where habitats or populations are represented by 'nodes' and dispersal is represented by 'links'. This approach spans discipline and systemic divides, facilitating identification of generalities in human impacts. We conducted a review of studies that have used graph theory to quantify spatial functional connectivity in aquatic ecosystems. The search identified 42 studies published in 2000-14. We assessed whether each study quantified the impacts of (1) habitat alteration (loss, alteration to links, and gain), (2) human movements causing species introductions, (3) overharvesting and (4) climate change (warming temperatures, altered circulation or hydrology, sea-level rise) and ocean acidification. In freshwater systems habitat alteration was the most commonly studied stressor, whereas in marine systems overharvesting, in terms of larval dispersal among protected areas, was most commonly addressed. Few studies have directly assessed effects of climate change, suggesting an important area of future research. Graph representations of connectivity revealed similarities across different impacts and systems, suggesting common strategies for conservation management. We suggest future research directions for studies of aquatic connectivity to inform conservation management of aquatic ecosystems. Journal compilation