Modelling semi-aquatic vertebrates’ distribution at the drainage basin scale: The case of the otter Lutra lutra in Italy

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Modelling semi-aquatic vertebrates’ distribution at the drainage basin scale: The case of the otter Lutra lutra in Italy D. Ottaviani a , M. Panzacchi a,∗ , G. Jona Lasinio b , P. Genovesi a , L. Boitani c a b c

INFS (Italian Wildlife Institute), Via Ca’ Fornacetta 9, I-40064 Ozzano dell’Emilia, Bologna, Italy Dip. di Statistica, Probabilità e Statistiche Applicate, Università degli Studi di Roma Sapienza, P.le Aldo Moro 5, I-00185 Rome, Italy Dip. di Biologia Animale e dell’Uomo – Università degli Studi di Roma Sapienza, Viale dell’Università 32, I-00185 Rome, Italy

a r t i c l e

i n f o

a b s t r a c t

Article history:

Modelling habitat suitability of semi-aquatic vertebrates for large scale conservation pur-

Received 1 April 2008

poses is a particularly challenging task, due to the fine-scale linearity of riverine habitats,

Received in revised form

and to the ecological continuum represented by the riparian and the aquatic ecosystems,

22 August 2008

on one side, and by a river and its tributaries, on the other.

Accepted 5 September 2008

We advocate that habitat suitability models (HSMs) should represent the fine-scale complexity of the riparian and aquatic ecosystems at the drainage basin scale, which is the most relevant spatial unit for planning conservation strategies for semi-aquatic vertebrates.

Keywords:

Hence, we developed a 3-step GIS-based modelling approach, and applied it to otter Lutra

Action plans

lutra in Italy to illustrate its potential applications for designing a nation-wide conservation

Conservation strategy

strategy.

Freshwater ecosystems Spatial scale riparian vegetation

First, we built a deductive HSM (resolution: 1:250,000, grain: 100 m × 100 m) of the riparian (100 m buffer around each river bank) and of the back-riparian (500 m buffer) areas along rivers. After, we created a synthetic index Si summarising this information within the cells of a 5 km × 5 km grid superimposed to the whole country, thus obtaining a nation-wide overview of habitat suitability. Both HSM and Si were validated by using data on otter distribution, by performing a sensitivity analysis. Finally, we scaled-up the information provided by Si to obtain a suitability index within each drainage basin (Sb ). By overlapping a layer representing the basins occupied by otters to Sb we obtained an effective tool to identify basins to be prioritised for the conservation and expansion of existing populations, for rejoining isolated sub-populations, and for habitat restoration. The methodology developed here helps filling the gap between the urgent conservation needs of semi-aquatic species, and the inadequacy of traditional modelling approaches for species inhabiting one of the world’s most endangered ecosystems. © 2008 Elsevier B.V. All rights reserved.

1.

Introduction

Inland waters cover only about 0.8% of the Earth’s surface, yet they host almost 6% of the world’s known biological diversity (Dudgeon et al., 2006). Due to overexploitation, habi-

∗

Corresponding author. Tel.: +39 0516512228; fax: +39 051 79 66 28. E-mail address: [email protected] (M. Panzacchi). 0304-3800/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.ecolmodel.2008.09.014

tat loss, flow modification and water pollution, freshwater ecosystems are among the world’s most endangered ecosystems, and several associated amphibians, reptiles, fishes, and mammals are highly threatened (Dudgeon et al., 2006). In particular, several carnivores belonging to the top-predator

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guild such as the European mink (Mustela lutreola) and several species of otters, such as the Eurasian otter (Lutra lutra), are threatened at the international and/or national level, and the IUCN strongly recommends the implementation of Action Plans for their conservation (Schreiber et al., 1989; FosterTurley et al., 1990). However, while terrestrial conservation strategies are well developed and commonly identify areas including high quality habitat to be bounded and protected, the conservation of freshwater ecosystems requires a different approach which has not received sufficient attention to date (Naiman and Latterell, 2005; Dudgeon et al., 2006). The protection of freshwater biodiversity is perhaps the ultimate conservation challenge, as the suitability of the habitat of freshwater dwelling species is influenced by that of the upstream drainage network, of the riparian zone, and of the surrounding land (Dudgeon et al., 2006). Moreover, as the species’ movements are steered by the spatial orientation of the watercourses and by the connectivity between different drainage basins, these parameters have to be taken into account when planning conservation strategies. In recent decades, GIS-based habitat suitability models (HSMs) have become a fundamental tool for species conservation plans (Barbosa et al., 2003). However, modelling habitat suitability of semi-aquatic vertebrates entails difficulties which, to our knowledge, have not been addressed to date. The first technical difficulty is related to the complexity of the otter habitat, which consists of a narrow strip of aquatic and riparian ecosystems. Even though otters and mink can stray several hundred meters away from the watercourse, most of their activity occur in its close proximity (Ruiz-Olmo et al., 1998). Hence, fine-scale modelling procedures should control for the decreasing habitat suitability as the distance to the main river increases. Besides, this would indirectly allow controlling for the ecological continuum represented by the terrestrial and aquatic habitat, as the land use progressively closer to the riparian habitat increasingly influences water quality. The second major technical problem is to obtain a useful, wide-scale picture of habitat suitability based on fine-scale habitat linearity. Previous deductive models (Boitani et al., 2002; Ottaviani, 2004) investigated habitat suitability within buffers created around all watercourses, regardless of their water regime and of the fact that the smallest streams commonly support a lower fish biomass and, thus, a lower carrying capacity for top-predators (Sjöåsen, 1997; Ruiz-Olmo et al., 2001; White et al., 2003; Kruuk, 2006). Consequently, the model output at the national scale was indistinguishable from that of terrestrial species, since the buffers covered most of the territory, thus overestimating the amount of available habitat. A different approach was adopted by Barbosa et al. (2003) which, first, built an inductive model based on proxies of water availability, productivity and human disturbance on a 10 km × 10 km grid and, after, down-scaled it to 1 km × 1 km resolution. The latter model outlined suitable watercourses but, as no characterisation of the drainage basins was provided, the whole territory appeared like a continuum of moreand less-suitable pixels without a clear indication of the actual connectivity between them. We realised that such model outputs may produce misleading conclusions regarding conservation priorities for semi-aquatic species, as the ecological

continuum represented by a river and its tributaries should be carefully considered when modelling riverine species’ distribution. On the one hand, nutrients and pollutants are conveyed and reach the highest concentrations downstream (Huang et al., 2007; Lemarchand et al., 2007) and, thus, conservation plans should target the whole drainage basin. On the other, otters seem to disperse preferentially by following the watercourses (Saavedra, 2002), and recent findings indicate that steep watersheds are able to steer the direction of the colonisation processes, impede or prevent dispersal, delimit different genetic clusters, and, possibly, influence the metapopulation structure (Janssens et al., 2006, 2008). Hence, drainage basins seem to be the most appropriate spatial unit for investigating habitat suitability for semi-aquatic vertebrates, plan conservation strategies, and monitor the colonisation processes at wide spatial scales (Ruiz-Olmo et al., 2001). Here we present the case of the endangered Italian otter population in order to illustrate the potential of modelling semi-aquatic species distribution at the drainage basin scale for conservation purposes. Otters are among the most endangered species in Italy due to demographic and genetic factors, scarcity of water and of trophic resources; habitat loss can be locally a relevant factor, as well as pollution, and mortality due to persecution and road accidents (Panzacchi et al., 2007). The species recently disappeared from the northern and central-northern part of Italy, and the southern population is fragmented and divided into two main sub-populations separated by a linear distance of approximately 50 km (Boitani et al., 2002; Marcelli, 2006). Prigioni et al. (2006a,b) estimated that about 220–260 otters still live in Italy. Hence, the Italian government has assigned to a team of experts the task of compiling an Action Plan for the Conservation of Otters in Italy (Loy, 2006; Panzacchi et al., 2008), which required an appropriate modelling to identify critical areas for the conservation of existing populations and potential areas for future expansion. Our goal was to develop a methodology for building habitat suitability models useful for identifying high priority areas for otter conservation at the national scale. We developed a three-step methodology aiming at: (i) developing and validating a nation-wide Habitat Suitability Model (HSM) adequately describing the fine-scale suitability of riparian and backriparian areas; (ii) scaling up the information provided by the HSM to the drainage basins’ scale; (iii) superimpose a layer representing the species’ extent of occurrence to the suitability map of drainage basins in order to illustrate the potential of our methodology for identifying priority areas for conservation and management.

2.

Methods

2.1.

Study area

The study area comprises the whole Italian country, with special focus on the central and southern part of the peninsula including the present otter distribution range and the surrounding areas. The topography of the area is dominated by the Apennine Mountains, which extend from the southern border of the Po plain to the north-eastern part of Sicily. The

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concave shape of the Apennines determines the main features of the peninsular drainage basins. While most rivers flowing westwards towards the Tyrrhenian Sea are long with a rather complex hydrographical network, the rivers flowing eastwards towards the Adriatic Sea are generally short, linear and parallel. The climate is temperate-Mediterranean with warm and very dry summers especially in the southern regions, where several streams and smaller tributaries have a seasonal regime and may dry out during summer. In particular, the Apulia region – the heel of Italy’s boot – is characterised by scarce precipitation and very few rivers confined to its northern part.

2.2.

Habitat suitability model

The HSM was built by using a deductive approach, in which a priori information is used to produce a categorical, discrete classification of habitat suitability for the species (Rondinini et al., 2006). The HSM was built for the whole Italian country with a resolution of 1:250,000, on 100 m × 100 m cells, by using the ArcInfo software (version 9.0). Based on previous suitability models for otters (Boitani et al., 2002; Ottaviani, 2004) and on the advice of two Italian otter experts (Loy, A. and Reggiani, G.), we selected the following basic GIS layers for building the HSM: a river layer (1:250,000), a lake layer (1:250,000), a land cover map (CORINE Land Cover, III 1:100,000), a Digital Elevation Model (DEM, 1:75,000), and a road-network layer (1:250,000). All basic layers were provided by the Italian Ministry for Environment, Land and Sea. The river layer included information on the order of rivers – defined according to their increasing position in the hierarchy of tributaries (Horton, 1947) – which were used as proxies of water flow. The river layer and lake layer were adjusted to the purpose of our project (e.g. underground watercourses were discarded), and a complete hydrographical network layer was built by joining rivers and lakes with other freshwater bodies such as lagoons and wetlands, extracted from the land cover layer. The relatively coarse resolution (1:250,000) of the hydrographical network layer allowed us to avoid the smallest ditches and becks, which cannot sustain viable otter populations (Kruuk, 2006). By applying the SLOPE GRID function in ArcInfo to the DEM we obtained an acclivity layer, which indicated variations in altitude between each cell and the neighbouring ones. The acclivity layer was used in the model, as very steep river banks have been considered good indicators of inaccessible areas to humans, with possible optimal sites for otter holts and couches. Following Boitani et al. (2002), Ottaviani (2004), and the recommendation of two Italian otter experts (Loy, A. and Reggiani, G.), we classified the land cover map, the DEM, and the acclivity into different classes reflecting the species’ preferences. The CORINE was divided in classes ranging from 0 (least favourable) to 3 (most favourable), as follows: town and other human settlements scored 0, agricultural areas, pastures and grasslands scored 1, non-intensive agricultural areas interspersed by natural vegetation, shrubs, moors and sclerophyllous vegetation scored 2, broad-leaves, coniferous and mixed forests scored 3. The altitudinal range of occurrence was assumed to vary between 0 and 2000 m a.s.l. (areas >2000 m scored 0), and the preferred altitude to range between 0 and 1500 m a.s.l. (see also Cortés et al., 1998). We classify the derived acclivity values in four classes, acclivity <10% indicate

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lowlands and gentle slopes, values between 10% and 30% identify moderately sloping areas, and values >30% indicate very steep areas. Cells with acclivity >30% in the riparian areas were considered highly favourable for the otter, and scored 3. The road network has a negative impact on otter occurrence, and scored 0. The model was built within two buffers of 100 m and 500 m constructed on the external perimeter of all watercourses, which identified the riparian and the back-riparian areas, respectively (Fig. 1a). The riparian buffer represents the area where a higher otter activity is commonly registered, while the back-riparian buffer represents an area less-frequently used by otters, but whose ecological characteristics directly affect the ecological suitability of the riparian buffer. The choice of the buffer size was based on literature and on available radiotelemetry data on otters in Italy (Fusillo, 2006; Mattei et al., 2005; A. Loy, and G. Reggiani, personal communication, 2007). The suitability of riparian and back-riparian areas was assessed based on different rationales, as illustrated in Fig. 1b and c. In the riparian buffer, the CORINE layer (score 0–3) was joined to areas with acclivity >30% (score 3). The resulting layer was combined with the hydrographical network and with the altitudinal layer: optimal habitats (score 3) surrounding firstorder streams or falling outside the preferred altitudinal range were demoted to score 2 in order to correct for the lower water flow and food availability commonly characterising headwaters. Finally, cells above 2000 a.s.l. were attributed score 0. In the back-riparian buffer, the most favourable CORINE classes were demoted from score 3 to score 2 in order to account for the increasing distance from the watercourse. The roadnetwork layer (score 0) was overlaid on the CORINE layer (score 0–2) and the overall density of habitat cells (score 1–2) was counted in a neighborhood of 3 × 3 cells by using the FOCALSUM function of ArcInfo. Each cell was reclassified as follows: over 75% of cells in the neighbourhood occupied by road (score 0), between 75% and 20% (score 1), less than 20% (score 2). Also in the back-riparian buffer all areas above 2000 asl were considered unsuitable (score 0) Finally, the suitability maps for the riparian and the back-riparian areas were joined to produce a final HSM with six suitability classes, described in Table 1.

2.3.

Identification of drainage basins

Drainage basins were identified in ArcInfo through hydrologic modelling. By using a hydrologically correct elevation grid obtained from the DEM we identified the directions of the runoff and the water accumulation at selected pour points and sub-catchment boundaries, as explained by Dunn et al. (1997). The procedure was applied to the central and southern part of the peninsula including the present extent of occurrence of otters and surrounding areas, for a total of 109,296 km2 (Fig. 2). Outlets (i.e. points at which water flows out of a catchment area) were identified at the conjunction between 2nd and 3rd order rivers, 3rd and 4th order rivers, and between 4th and 5th order rivers according to the hierarchical arrangement of watercourses. The external perimeter of the drainage basins was delineated on the basis of the given outlet points. The resulting polygons represented the smallest identified drainage basin units, and were defined as “quaternary drainage basins” (Fig. 2a). By merging the quater-

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Fig. 1 – Flow chart synthesising the model design (a) and the double procedure (b and c) to develop the habitat suitability model for otters (HSM, bottom right). The model was produced by joining different GIS layers within the 100 m buffer surrounding the perimeter of water bodies (i.e. the riparian buffer, b), and within the 500 m buffer (i.e. back-riparian buffers, c). See text for further details.

nary basins according to a hierarchical system reflecting the order of the river embedded, we obtained increasingly larger drainage basins named tertiary, secondary, primary (Fig. 2b and c). Moreover, by joining those quaternary basins where otter signs have been recorded, we obtained an operational representation of the species’ extent of occurrence (Fig. 2c).

2.4. Scaling-up the HSM information to the drainage basin scale The information provided by the HSM was scaled up to the drainage basin scale with a two-step procedure. First, we

computed a suitability index Si synthesising the fine-scale suitability of the riparian and back-riparian area within the 5 km × 5 km units of a grid superimposed to the whole country. As the drainage basins differ greatly in area size, this preliminary operation was necessary in order to standardise the information at the national scale within equal-sized spatial units. After, we averaged the Si within each basin, thus obtaining a suitability index for quaternary drainage basins Sb . Si represents the proportion of different HSM classes within each 5 km × 5 km units. The index was weighed by the suitability class within each cell, and was modified by attributing

Table 1 – Definition of the six habitat suitability classes composing the otter Habitat Suitability Model (HSM, Fig. 3). Each class indicates the ecological conditions characterising the riparian and/or the back-riparian buffers built around watercourses and freshwater bodies. Suitability class 0 1 2 3 4 5

Riparian buffer (100 m)

Back-riparian buffer (500 m)

Not suitable – – Scarcely suitable (high human impact) Suitable (low human impact) Highly suitable (high degree of naturalness)

Not suitable Scarcely-suitable (high human impact) Suitable (low human impact) – – –

Core area of major lakes and fresh water bodies falling outside the model design of riparian and back-riparian buffers.

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Fig. 2 – Primary (a), secondary (b) and quaternary (c) drainage basins in the central and southern parts of the Italian peninsula. The shaded area in (c) represents the Italian otter (Lutra lutra) extent of occurrence, which we identified for operational purposes by merging those 94 quaternary basins where otter presence sites have been recorded during the years 2000–2007.

a twofold importance to riparian compared to back-riparian areas. We defined: n = (n0 , n1 , n2 ) number of cells characterised by suitability classes 0, 1, and 2, in the back-riparian buffer; m = (m0 , m3 , m4 , m5 ) number of cells characterised by suitability class 0, 3, 4 and 5, in the riparian buffer. Si was computed as follows: Si = 100

 m 5

+ 0.5

mtot

n 2

ntot

+ 0.7

m4 m3 m0 + 0.3 − mtot mtot mtot

+ 0.3

n1 n0 − ntot ntot





(1)

Si was characterised by integer and continuous values ranging from 0 to 150; these were divided into four classes as follows s1 = 0–50, s2 = 51–75, s3 = 76–100, s4 = 101–150. Note that the range of the classes s2 and s3 is half compared the range of s1 and s4 . This range choice aimed at depicting with greater detail those basins characterised by medium suitability values, which are likely to be critical for habitat restoration programmes. The arithmetic mean of Si within each 5 km × 5 km units was used to calculate the average suitability index within each quaternary drainage basin (Sb ). Those 5 km × 5 km units that did not include any HSM cells, or that included a number of HSM cells which covered less than 10% of the area of a 5 km × 5 km unit, were excluded from the average computation.

2.5.

(Tyre et al., 2003). As the presence sites recorded during the period 2000–2007 were often spatially clustered, we standardised the data by overlaying a 5 km × 5 km grid, and identifying only those 5 km × 5 km units (n = 177) where otter occurrence had been recorded (i.e. otter sites). Both HSM and Si were validated as described below by using the statistical package R 2.7.1 for windows (R Development Core Team, 2006).

Model validation

In order to validate the HSM, we obtained UTMs of all sites (N = 324) where otter occurrence had been recorded in Italy between 2000 and 2007. The data were collected by all research groups monitoring the distribution of otters in Italy by following the standard survey method described by Reuther et al. (2000; see also Fusillo et al., 2007, Prigioni et al., 2007). All research groups allowed us to use presence-only data in order to validate the HSM. For this purpose, we discarded those sites where otter presence was not recorded, and considered only sites where otter signs have been documented, as they were likely to include “false negatives”

2.5.1.

Validation of the habitat suitability model

The HSM was validated by adapting the compositional procedure described in Ottaviani et al. (2004) to the otter case study. A composition was defined as the proportion of 100 m × 100 m cells with different suitability classes recorded within each 5 km × 5 km unit. The aim of the procedure was to verify that the suitability characterising the compositions of those cells where otter presence was recorded (V1 ) was higher compared to that of a random sample of cells extracted from the area outside the species’ extent of occurrence (V2 ). Let i (i = 0, . . ., 5) be the number of 100 m × 100 m HSM cells belonging to each suitability class within each 5 km × 5 km unit, and n the total number of HSM cells. We defined the compositional data as pi = ni /n. We transformed the data by computing ri = pi /p1 , and by calculating the natural logarithm of ri (see Aitchinson, 1986). We first tested for differences in mean and covariance between the compositions of V1 and V2 . Once differences were established, we tested the hypothesis that the suitability of the compositions in V1 was higher than that in V2 . This was done by establishing a correspondence one-to-many between each cell in V1 and all cells in V2 , and by computing the differences (ri1 − ri2 ). This vector of differences was defined as “configuration” (C). Four favourable configurations (C1 , . . ., C4 ) fulfilled the conditions for a higher suitability both in the riparian and back-riparian areas (see Table 2). The most favourable configuration (C1 ) required a greater proportion of cells belonging to class 4 and 5 in the riparian buffer, and a greater proportion of suitable cells in the back-riparian areas (class 2). C2 required the fulfilment of all conditions required for C1 , except that it allowed for a

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Table 2 – Definition of “favourable configurations” for the validation of the habitat suitability model (HSM). A configuration (C) is a vector of differences between the proportions of the different suitability classes, see Table 1 recorded within each 5 km × 5 km unit where otter presence was recorded (V1 ) and in a random sample of units extracted from the area outside the extent of occurrence (V2 ). Favourable configurations occur when a higher suitability is recorded in V1 compared to V2 . Label “+” indicates a higher proportion of cells belonging to a given suitability class in V1 compared to V2 ; Label “−” indicates no differences or a lower proportion of cells in V1 . Favourable configuration

C1 C2 C3 C4

Difference between V1 and V2 in the proportions of cells belonging to Suitability class 1

Suitability class 2

Suitability class 3

Suitability class 4

− − − −

+ + + +

− + − −

+ + − +

relatively greater proportion of scarcely suitable cells in V1 compared to V2 . C3 and C4 required only class 4 or 5 to be more frequent in V1 than in V2 , but they both still required the occurrence favourable conditions in the back-riparian areas (class 2). Due to the high number of comparisons between V1 and V2 , the binomial distribution was approximated with a Gaussian distribution. The model was validated if the total number of favourable configurations (C1 , . . ., C4 ) exceeded the 95% of a normal distribution at ˛ = 0.01. A sensitivity analysis was performed to verify that: (i) the difference in mean and covariance (i.e. population structure) between the compositions in V1 and V2 was higher than expected by chance; (ii) the number of favourable configurations characterising V1 was higher than expected by chance compared to V2 . The analyses were carried out twice by keeping the 177 otter sites in V1 fixed, and sampling randomly 177 cells within V2 with 1000 replications. In the first series of 1000 runs we tested for structural population differences (i.e. differences in mean and covariance), while in the second series we tested for differences in the number of favourable configurations.

2.5.2.

Validation of the suitability index (Si )

The Si was validated using a multinomial parametric bootstrap procedure (Ottaviani et al., 2004) which, through a generalised maximum likelihood type test, compared the proportions of otter sites falling into different suitability classes on the basis of the theoretical multinomial distribution that we expected to find if the model was validated. Given s = (s1 , s2 , s3 , s4 ) the suitability classes of Si , p = (p1 , p2 , p3 , p4 ) the probability of otter occurrence in each s, and l = (l1 , l2 , l3 , l4 ) the number of otter’s presence sites falling in each s, we assumed l to be distributed as a multinomial distribution with parameters p. We considered Si to be validated (H0 ) if the probability of occurrence of otter sites increased with the suitability class (s), i.e. if the following conditions were verified:

4

1. p = 1; 0 ≤ pi ≤ 1; i=0 i 2. p1 < p2 < p3 < p4

i = 0, . . . , 4

The test statistic (t) presented in Eq. (2) was given by the ratio of the unconstrained maximum of the likelihood function (numerator) obtained in the observed proportions l while the denominator was given by the likelihood function max-

Suitability class 5 + + + −

imised under conditions 1 and 2.

 t = −2 log

supH0 Multinomial(p)



sup Multinomial(p)

(2)

In order to calculate t we performed a multinomial-based parametric bootstrap procedure (Efron and Tibshirani, 1986). The model was validated if t was smaller than the 95% percentile of the bootstrap distribution.

3.

Results

3.1.

Drainage basins and otter’s extent of occurrence

From digital elevation hydrological modelling we obtained a layer composed by 865 polygons representing all quaternary drainage basins in the north-central and southern part of the Italian peninsula (Fig. 2c). By merging these into progressively wider basins according to the hierarchical order of the main rivers we obtained 470 tertiary drainage basins, 171 secondary basins (Fig. 2b), and 127 primary basins (Fig. 2a). Their shapes reflect the characteristics of their main rivers: long and with many tributaries westwards, short and linear eastwards. By merging the 94 quaternary basins where otter sites have been recorded, we identified the otter extent of occurrence, which was highly fragmented and divided in a northern and southern area, approximately 50 km distant (Fig. 2c).

3.2.

Habitat suitability model

The nation-wide HSM described the fine-scale suitability of the riparian and the back-riparian areas. As it would not be possible to display correctly the large amount of information provided by the HSM for the whole country, in Fig. 3 we illustrated a few examples of the ecological content provided by the HSM. The entire HSM is accessible as supplementary material of this publication. The HSM has been successfully validated, as the compositions within otter sites (V1 ) were different by mean (2 = 188.45, p < 0.001) and covariance (2 = 123.44, p < 0.001) from those randomly sampled outside the extent of occurrence (V2 ). The sensitivity analysis confirmed these differences in 100% of the replications (N = 1000). Also, the suitability in V1 was higher than that in V2 , as the number of favourable configurations (N = 18,089) exceeded the 95% quan-

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Fig. 3 – Details of the otter habitat suitability model (HSM) describing different ecological settings within the 100 m and 500 m buffers (i.e. riparian and back-riparian areas, respectively). The suitability classes composing the HSM are described in Table 1. (a) The suitability characterising first-order streams is lower compared to that of higher order rivers, since the former are likely to have a lower water flow, more marked seasonal regime and lower food availability compared to the latter. (b) Areas occupied by cities and towns, and areas crossed by roads were considered unsuitable, and the suitability commonly increased with the distance from human-dominated areas. (c) Example of “soft edges”: the high suitability in the riparian area is enhanced by the contiguity to suitable back-riparian areas (d) Example of “hard edges”: the high suitability in the riparian buffer is contrasted by the contiguity to a highly human dominated back-riparian area.

tile of the Gaussian distribution (17,375.79). The sensitivity analysis demonstrated (˛ = 0.05) that the compositions in V1 were more favourable than those in V2 in 63.4% of the replications (N = 1000).

3.3.

Suitability index (Si )

The Si provided a fine-scale overview of suitability in the whole country (Fig. 4a). The distribution of the most suitable areas closely traced that of hilly and low mountain areas which, in Italy, are usually characterised by a higher proportion of natural habitats, with lower human disturbance compared to lowlands. In particular, high suitability values were recorded on the eastern part of the Alps and along the Apennine chain, with a major interruption of suitability in the central-southern part of the country in correspondence to the gap between the

northern and southern sub-areas characterising the extent of occurrence. Si has been successfully validated, as the probability of occurrence of otter sites increased with the suitability class (p < 0.001; 95% percentile of the t = 7.79; Fig. 5). The sensitivity analysis confirmed the validation of Si in 86% of the cases (N = 1000). Hence, the analyses demonstrated the existence of a strong agreement between the geographic distribution of otter sites and the higher Si suitability classes.

3.4.

Suitability index within drainage basins (Sb )

The Sb provided a wide-scale overview of the suitability of the north-central and southern part of Italy (Fig. 4b). Within the current extent of occurrence, 43 basins were characterised by very high average suitability (Sb : 101–150), 32 basins and 18 basins had respectively a high (Sb : 76–100) and medium (Sb :

Fig. 4 – Otter habitat suitability index Si in Italy (a). The index summarises the information provided by the habitat suitability model (HSM) within 5 km × 5 km units. In (b) we present the otter habitat suitability index within the drainage basins (Sb ) for the central-southern part of Italy. Sb was calculated as the average value of the habitat suitability index Si within each quaternary drainage basin (Fig. 2c).

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Fig. 5 – Output of the multinomial validation procedure for the suitability index Si . The figure shows the test statistic distribution with its 95% percentile (solid line), and the observed test statistic (dashed line). The observed statistic value is considerably smaller then the critical 95% value, indicating that the model is validated with a great confidence interval. 51–75) suitability, and 1 basin had an average low suitability (Sb : 26–50).

4.

Discussion

We developed a procedure for modelling the distribution of semi-aquatic vertebrates for conservation purposes by synthesising the fine-scale suitability of the continuum represented by the terrestrial and the aquatic ecosystems to the drainage basin scale. The novelty of this procedure is threefold. First, it assesses the fine-scale habitat suitability for semi-aquatic species by taking into account the continuum represented by the aquatic, the riparian and the backriparian area, in decreasing order of importance. Secondly, our approach evaluates habitat suitability for semi-aquatic species at the drainage basin scale. This method meets the criticism that several authors rose against the use of the “fortress approach” (i.e. bound and protect areas of high habitat quality) for the conservation of freshwater dwelling species, as this policy has been developed specifically for the conservation of terrestrial ecosystems (see Dudgeon et al., 2006). Indeed, the protection of river segments or lakes embedded in unprotected drainage basins is likely to fail (Boon, 2000) or to be counterproductive (Dunn, 2003). Finally, our procedure allows for identifying drainage basins that are, or that are likely to become, critical for the establishment or the maintenance of a meta-population structure. The identification of these key-basins should be based on the suitability of their ecosystems, on their position within higher-ranking drainage basins – as these may be critical for the dispersal – and by their location with respect to the species’ extent of occurrence. Hypothetically, a drainage basin linking two isolated parts of the extent of occurrence should generally be prioritised for conservation programmes notwithstanding its habitat suitability, while the actions to be taken should conform to the habitat suitability level, i.e. highly suitable basins

should be protected, and moderately suitable ones should be restored. In key-basins of national importance for otter conservation, further investigations would be required in order to identify and eliminate the local causes of extinction and increase the probability of a successful conservation strategy. For this purpose, detailed extracts of the suitability of the keybasins highlighted by the model should be delivered to a team of local experts, which would refine the suitability assessment in light of locally available information such as water pollution, food availability, critical road mortality areas, critical poaching areas, critical barriers for otters’ movements, etc. This would contribute to better define local conservation plans aimed at enforcing the provisions of the national Action Plan. As it would be unrealistic to assume that wide-scale suitability models can be fully representative of local peculiarities, we suggest that the combination between theoretical modelling and expert-based evaluation of the outputs is a sound approach that deserves further attention in the development of wildlife conservation strategies. Our first aim was to identify the most relevant spatial scale to represent the fine-grain habitat suitability for otters, and to build a HSM tightly fitting to the spatial arrangement of the hydrographical network. Even though most of otters’ feeding and resting activities take place in close proximity to the watercourse (Mason and Macdonald, 1986; Kruuk, 2006), the surrounding few hundreds of meters play a relevant role in the species’ ecology, as they include additional holts, hoovers, and hunting areas for non-aquatic prey items (Liles, 2003; Mattei et al., 2005; Fusillo, 2006). Hence, first we defined a riparian and a back-riparian buffer around rivers and freshwater bodies and, after, we developed a methodology to assess their suitability by using different criteria according to their increasing distance to the watercourse (Fig. 1). The resulting HSM provided high-resolution information on the suitability of the riparian and of the back-riparian zones of all inland waters in Italy (Fig. 3). The agreement between the HSM and data on otter occurrence indicated a high model performance and confirmed the appropriateness of the ecological assumptions of the methodology, as the species was more frequently associated to overgrown riparian habitats with low-human dominated back-riparian areas. Researchers are often required to develop urgent and sound conservation plans for endangered species based on incomplete information. In the case of otters, relevant information such as fish abundance, water flow and water pollution are rarely available or reliable at wide spatial scales, as these parameters are highly fluctuating in time and space, and their surveys techniques are rarely standardised. Hence, the use of proxies is often unavoidable. Our HSM was mainly constrained by the lack of direct data on fish abundance and water-related parameters. As water availability is a critical factor for otters in Mediterranean countries (Prenda et al., 2001), we attempted to compensate for the lack of information on periodic droughts by downgrading habitat suitability around the smallest streams, which are prone to dry up during summer. In order to strengthen the importance of the medium-low course of the rivers, which is commonly associated with the highest fish biomass (Macdonald and Mason, 1983), we also downgraded the suitability of areas located above the preferred altitude range for the species. In addi-

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tion, as natural and well structured riparian vegetation is commonly correlated to high water quality, high primary productivity, high fish biomass, and high availability of alternative prey species (Mason and Macdonald, 1989; Guégan et al., 1998), areas with a high degree of naturalness in the riparian belt and a low human impact in the back-riparian area are likely to support a high trophic availability for otters. The good correspondence between otter sites and the most suitable HSM classes supports this hypothesis. However, we would like to clearly highlight the limitations of our large-scale modelling approach. Due to lack of direct data on food availability, water pollution and other factors affecting otter occurrence at the national scale, we suggest the model outputs to be used for planning large scale conservation strategies, or for identifying key drainage basins for which additional information on the causes of local extinction and on the current threats and limiting factors should be collected. The detailed HSM output is valuable for the development of local conservation plans. However, it is also useful to have a more synthetic overview of habitat suitability to be used for planning wide-scale conservation strategies. Thus, we changed the model grain and created an index (Si ) synthesising the HSM into 5 km × 5 km units, thus providing a nation-wide overview of habitat suitability (Fig. 4a). The multinomial validation procedure confirmed a high correspondence between the locations of otter sites and the more suitable Si classes. The Si identified the most remote parts of the Apennines and the lowest mountains of the Alpine chain as the most suitable areas for the otter, and the human-dominated Po valley and the water-deficient Apulia region as the least suitable. Based on literature, we would expect lowlands to represent highly suitable otter habitats, as the rivers’ primary productivity increases downstream and prey biodiversity and biomass typically reaches a peak in the medium-low course of rivers (Macdonald and Mason, 1983; Ruiz-Olmo et al., 2001; Marcelli, 2006). However, as Italian lowlands are frequently characterised by a high concentration of urban areas, infrastructures and intensive agriculture especially in the central and southern party of Italy, rivers in lowlands often have a lower availability of riparian cover, more eutrophic waters, and lower overall suitability to otters compared to hilly areas (Macdonald and Mason, 1983). Similar altitudinal patterns in otter distribution, largely attributed to human population density, were also recorded in Spain (Cortés et al., 1998; Prenda et al., 2001; Barbosa et al., 2003), Portugal (Barbosa et al., 2003), Yugoslavia and France (Macdonald and Mason, 1983). Although Si provided a solid, nation-wide overview of habitat suitability, in order to identify priority areas for otter conservation it should be complement with information on the connectivity of the drainage basin network. In fact, the contiguity of highly suitable 5 km × 5 km units in Si (Fig. 4a) should not be considered a-priori a potential area of connection to be prioritised for otter conservation, as these units may relate to different river stretches belonging to separate drainage basins divided by impervious watersheds. As steep ridges can act as physical barriers hampering inter-catchment otter movements (Cortés et al., 1998; Janssens et al., 2006, 2008), it may be advisable to explore alternative connection areas along the watercourses. In order to correctly identify priority areas for conservation at a wide spatial scale, we scaled

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up the information provided by the Si , and created an index (Sb ) synthesising the suitability of riparian and back-riparian habitats within each drainage basin. In order to illustrate a potential application of our method, we overlaid a layer representing the species’ extent of occurrence to Sb (Fig. 4b). As drainage basins are the most appropriate spatial unit for planning conservation strategies of freshwater-dwelling species, we created an ad hoc representation of the species’ extent of occurrence by merging those basins occupied by the species (Fig. 2c). For our purposes, this method has several advantages compared to those traditionally used for the definition of the extent of occurrence, such as the identification of cells occupied by the species (or drawing a blotch around the sites occupied by the species (Gaston, 1991; IUCN, 2001). Firstly, both otter movements and colonisation processes occur preferentially along the main drainage basin (Cortés et al., 1998; Saavedra, 2002), and steep watersheds may hamper gene flow (Janssens et al., 2008). The extent of occurrence represented in Fig. 2c may therefore provide clues concerning the anatomy of the metapopulation structure, which would be of great importance for conservation. Secondly, Fig. 2c reveals the existence of gaps in the distribution range due to vacant basins, and highlights those key basins that, due to their length, size, geographic position, and connectivity to other basins could be critical for the exchange of individuals between isolated populations and for their future expansion. For example, Fig. 4b show that a range expansion towards the south-eastern part of the Italian peninsula is prevented by scarcity of water and by the presence of scarcely suitable basins. Also, Fig. 4b shows a group of suitable basins, whose rivers flow in the direction NW–SE, which may allow for a connection between the northern and the southern part of the extent of occurrence, and may be used as corridors for dispersal. Finally, Fig. 4b suggests that the future expansion of the northern population will most likely occur in the direction NW and W from the upper part of the northern sub-area. We developed a modelling procedure that contributes to fill the gap between the urgent need to conserve semi-aquatic top-predators and the inadequacy of the current methodological approaches. As suggested by Wiens (2002), we started “taking landscape ecology into the water”. We recommend future studies to scale up the fine-grain information regarding habitat suitability along the watercourses to the drainage basin scale, and that these should be the basic management unit for conservation of semi-aquatic species. Future efforts should focus on the study of the permeability of watersheds to semi-aquatic species, and on the influence of terrestrial land use strategies on aquatic ecosystems within the drainage basins, taking into account the directional accumulation of nutrients and pollutants downstream.

Acknowledgements funded by the Italian Ministry of Environment. We are particularly grateful to A. Loy and G. Reggiani for providing useful advice for the model development, and to all the Italian research groups who provided their survey data to validate the model: University of Rome Sapienza University of Molise, Gruppo Lontra Molise, University of Pavia, Cilento and Vallo di

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Diano National Park, Pollino National Park, Centro Studi Naturalistici ONLUS, Oasi WWF delle Cascate del Rio Verde, and Apulia Region.

Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.ecolmodel.2008.09.014.

references

Aitchinson, J., 1986. The Statistical Analysis of Compositional Data. Chapman & Hall, London. Barbosa, A.M., Real, R., Olivero, J., Vargas, J.M., 2003. Otter (Lutra lutra) distribution modelling at two resolution scales suited to conservation planning in the Iberian Peninsula. Biol. Cons. 114, 377–387. Boitani, L., Corsi, F., Falcucci, A., Maiorano, L., Marzetti, I., Masi, M., Montemaggiori, A., Ottaviani, D., Reggiani G., Rondinini, C., 2002. Rete Ecologica Nazionale. Un approccio alla conservazione dei vertebrati italiani. Sapienza University of Rome Italian Ministry for Environment Territory and Sea, Institute of Applied Ecology Rome. http://www.gisbau.uniroma1.it/REN. Boon, P.J., 2000. The development of integrated methods for assessing river conservation value. Hydrobiologia 422/423, 413–428. Cortés, Y., Fernandez-Salvador, R., Garcia, F.J., Virgòs, E., Llorente, M., 1998. Changes in otter Lutra lutra distribution in Central Spain in the 1964–1995 period. Biol. Cons. 86, 179–183. Dudgeon, D., Arthington, A.H., Gessner, M.O., Kawabata, Z.I., Knowler, D.J., Leveque, C., Naiman, R.J., Prieur-Richard, A.H., Soto, D., Stiassny, M.L.J., Sullivan, C.A., 2006. Freshwater biodiversity: importance, threats, status and conservation challenges. Biol. Rev. 81, 163–182. Dunn, H., 2003. Can conservation assessment criteria developed for terrestrial systems be applied to riverine systems? Aquat. Ecosyst. Health Manage. 6, 81–95. Dunn, S.M., McAlister, E., Ferrier, R.C., 1997. Development and application of a distributed catchment scale hydrological model for the river Ythan, NE Scotland. Hydrol. Process. 12, 401–416. Efron, B., Tibshirani, R.J., 1986. Bootstrap methods for standard errors, confidence intervals and other measures of statistical accuracy. Stat. Sci. 1, 54–77. Foster-Turley, P., Macdonald, S., Mason, C. (Eds.), 1990. Otters, An Action Plan for their Conservation. IUCN, Gland, Switzerland, p. 126. Fusillo, R., Marcelli, M., Boitani, L., 2007. Survey of an otter Lutra lutra population in southern Italy: site occupacy and influence of sampling season on species detection. Acta Theriol. 52, 251–260. Fusillo R., 2006. Risorse trofiche e habitat della lontra (Lutra lutra L.) in Italia meridionale. Fattori di variazione ed analisi di selezione. Ph.D. Thesis. Sapienza University of Rome, 192 pp. (in Italian). Gaston, K.L., 1991. How large is a species geographic range? Oikos 61, 434–438. Guégan, J.F., Lek, S., Oberdorff, T., 1998. Energy availability and habitat heterogeneity predictions of global riverine fish diversity. Nature 391, 381–384. Huang, S., Budd, W.W., Chan, S., Lin, Y., 2007. Stream order, hierarchy, energy convergence of land use. Ecol. Mod. 205, 255–264.

Horton, R.E., 1947. Erosional development of streams and their drainage basins: hydrophysical approach to quantitative morphology. Geol. Soc. Am. Bull. 56, 275–370. IUCN, 2001. IUCN Red List Categories and Criteria: Version 3.1. IUCN Species Survival Commission. IUCN, Gland, Switzerland and Cambridge, 30 pp. Janssens, X., Defourny, P., Kermabon, J., Baret, P., 2006. The recovery of the otter in the Cevennes (France): a GIS-based model. Hystrix It. J. Mamm. 17, 5–14. Janssens, X., Fontaine, M.I.C., Michaux, J.R., Libois, R., Kermabon, J., Defourny, P., Baret, P.V., 2008. Genetic pattern of the recent recovery of European otters in southern France. Ecography 31, 176–186. Kruuk, H., 2006. Otters, Ecology, Behaviour and Conservation. Oxford University Press Inc., New York, 260 pp. Lemarchand, C., Amblard, C., Souchon, Y., Berny, P., 2007. Organochlorine compounds (pesticides and PCBs) in scats of the European Otter (Lutra lutra) from an actual expanding population in Central France. Water Air Soil Pollut. 186, 55–62. Liles, G., 2003. Otter Breeding Sites. Conservation e Management. Conserving Natura 2000 Sites Conservation Techniques Series N◦ 5. English Nature, Peterborough, 39 pp. Loy, A., 2006. An Italian action plan for the Eurasian otter (Lutra lutra): preliminary contents. IUCN Otter Spec. Group Bull. 23, 26–27. Macdonald, S.M., Mason, C.F., 1983. The otter Lutra lutra in Southern Italy. Biol. Cons 25, 95–101. Marcelli M., 2006. Spatial structure and ecological determinants of otter (Lutra lutra L.) in Italy. Development of predictive models for ecological inference and conservation. Ph.D. Thesis. Sapienza University of Rome (in Italian with English summary). Mason, C.F., Macdonald, S.M., 1986. Otters. Ecology and Conservation. Cambridge University Press, Cambridge, 236 pp. Mason, C.F., Macdonald, S.M., 1989. Acidification and Otter (Lutra lutra) Distribution in Scotland. Water Air Soil Poll. 43, 365–374. Mattei, L., Antonucci, A., Di Marzio, M., Ronci, D., Biondi, M., 2005. Otter experimental release in Aterno-Pescara basin (Abruzzo): home range and space use. In: European Otter Workshop, Padula (SA, Italy), 20–23 October, 2005. Naiman, R.J., Latterell, J.J., 2005. Principles for linking fish habitat to fisheries management and conservation. J. Fish Biol. 67, 166–185. Ottaviani, D., 2004. I modelli di distribuzione delle specie animali in Italia: opportunità e limiti della loro utilizzazione per la conservazione della biodiversità. Ph.D. Thesis, Sapienza University of Rome (in Italian with English summary), 240 pp. Ottaviani, D., Jona Lasinio, G., Boitani, L., 2004. Two statistical methods to validate habitat suitability models using presence-only data. Ecol. Mod. 179, 417–443. Panzacchi, M., Genovesi, P., Loy, A., 2008. National Action Plan for the Conservation of Otter Lutra lutra in Italy. Italian Ministry for the Environment and the Protection of Land and Sea, Italian Wildlife Institute (Eds.), 175 pp. (in preparation). Panzacchi, M., Genovesi, P., Loy, A., 2007. An action plan for the conservation of Otter in Italy. In: Abstracts of the 25th Mustelid Colloquium, Trebon, Czech Republic, 4–7 October. Prenda, J., Lopez-Nieves, P., Bravo, R., 2001. Conservation of otter (Lutra lutra) in a Mediterranean area: the importance of habitat quality and temporal variation in water availability. Aq. Cons. Marine Freshwater Ecosyst. 11, 343–355. Prigioni, C., Remonti, L., Balestrieri, A., Sgrosso, S., Priore, G., 2006a. How many otters are there in Italy? Hystrix, It. J. Mamm. 17, 29–36. Prigioni, C., Remonti, L., Balestrieri, A., Sgrosso, S., Priore, G., Mucci, N., Randi, E., 2006b. Estimation of otter (Lutra lutra) population size by fecal DNA typing in Southern Italy. Hystrix, It. J. Mamm. 87, 855–858.

e c o l o g i c a l m o d e l l i n g 2 2 0 ( 2 0 0 9 ) 111–121

Prigioni, C., Balestrieri, A., Remonti, L., 2007. Decline and recovery in otter Lutra lutra populations in Italy. Mammal Rev. 37, 71–79. R Development Core Team, 2006. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, http://www.R-project.org. Reuther, C., Dolch, D., Green, R., Jahrl, J., Jefferies, D., Krekemeyer, A., Kucerova, M., Bo Madsen, A., Romanowsky, J., Roche, K., Ruiz-Olmo, J., Teubner, J., Trindade, A., 2000. Surveying e monitoring distribution e population trends of the Eurasian Otter (Lutra lutra). Habitat 12, 148. Rondinini, C., Wilson, K.A., Boitani, L., Grantham, H., Possingham, H.P., 2006. Tradeoffs of different types of species occurrence data for use in systematic conservation planning. Ecology Letters 9, 1136–1145. Ruiz-Olmo, J., Delibes, M., Zapata, S.C., 1998. External morphometry, demography and mortality of the otter Lutra lutra (Linneo, 1758) in the Iberian Peninsula. Galemys 10, 239–251. Ruiz-Olmo, J., Lopez-Martin, J.M., Palazon, S., 2001. The influence of fish abundance on the otter (Lutra lutra) populations in Iberian Mediterranean habitats. J. Zool. 254, 325–336.

121

Saavedra, B., 2002. Reintroduction of the Eurasian otter (Lutra lutra) in Muga and Fluvià basins (north-eastern Spain): viability, development, monitoring and trends of the new population. Ph.D. Thesis. University of Girona, Spain, 218 pp. Schreiber, A., Wirth, R., Riffel, M., Rompaey, H., 1989. Weasels, Civets, Mongooses and their relatives. In: IUCN (Ed.), An Action Plan for the Conservation of Mustelids and Viverrids. IUCN, Gland, p. 99. Sjöåsen, T., 1997. Movements e establishment of reintroduced European otters Lutra lutra. J. Appl. Ecol. 34, 1070–1080. Tyre, A.J., Tenhumberg, B., Field, S.A., Niejalke, D., Parris, K., 2003. Improving precision and reducing bias in biological surveys: estimating false-negative error rates. Ecol. Appl. 13, 1790–1801. White, P.C.L., McClean, C.J., Woodroffe, G.L., 2003. Factors affecting the success of an otter (Lutra lutra) reinforcement programme, as identified by post-translocation monitoring. Biol. Cons. 112, 363–371. Wiens, J.A., 2002. Riverine landscapes: taking landscape ecology into water. Freshwater Biol. 47, 501–515.