Biol Invasions DOI 10.1007/s10530-014-0725-y
ORIGINAL PAPER
Modelling the risk of invasion by the red-swamp crayfish (Procambarus clarkii): incorporating local variables to better inform management decisions Francisco D. Moreira • Fernando Ascensa˜o • Ce´sar Capinha • Diana Rodrigues • Pedro Segurado Margarida Santos-Reis • Rui Rebelo
•
Received: 14 September 2013 / Accepted: 20 May 2014 Ó Springer International Publishing Switzerland 2014
Abstract The correct modelling of the potential distribution of an invasive species is crucial to define effective management and monitoring strategies. Here we compared the results of models built at different spatial scales to identify the areas at risk of invasion by the red swamp crayfish (Procambarus clarkii) in the northwest of Portugal. Firstly, we surveyed crayfish at 97 locations. Secondly, we used presence–absence data and local variables to model its current distribution (local variables model) and identified slope and river width as the best explanatory factors. Thirdly, we integrated these two local variables into a former model built for the Iberian Peninsula (regional model) increasing considerably its predictive power. Finally, we compared both models focusing on the area predicted to be invaded. The local model showed a considerably narrower extent of suitable areas for
crayfish in the study area than the regional model. These results show that the refinement of regional scale predictions through the incorporation of speciesenvironment relationships at local scales may be important for supporting management decisions. By not integrating the effects of local variability, regional bioclimatic models may overlook the potential distribution of this invader at manageable extents. Results further suggest that a wide range of native ecosystems of conservation value are probably unsuitable for this invasive species. Keywords Biological invasions Red swamp crayfish Risk assessment Spatial Scale Species distribution models
Introduction F. D. Moreira (&) F. Ascensa˜o C. Capinha D. Rodrigues Centro de Biologia Ambiental, Faculdade de Cieˆncias, Universidade de Lisboa, 1749-016 Lisbon, Portugal e-mail:
[email protected] P. Segurado Centro de Estudos Florestais, Universidade Te´cnica de Lisboa, Tapada da Ajuda, 1349-017 Lisbon, Portugal M. Santos-Reis R. Rebelo Centro de Biologia Ambiental, Departamento de Biologia Animal, Faculdade de Cieˆncias, Universidade de Lisboa, Ed. C2, Campo Grande, 1749-016 Lisbon, Portugal
Invasive species are a major threat to biodiversity worldwide (Sala et al. 2000; Vie´ et al. 2009) and an important part of current research in invasion ecology aims at predicting the areas at risk of invasion. Available predictions have been largely based on correlative approaches that compare environmental conditions in which species are observed in their native or introduced ranges and those found in the new areas (Guisan and Thuiller 2005), usually on a regional or continental scale (e.g. Ficetola et al. 2007; Capinha and Anasta´cio 2011). However, these large-scale predictions may be insufficient to forecast local-scale
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distribution (Guisan and Thuiller 2005; Loo et al. 2009; Reino et al. 2013), which is often the scale at which management options are feasible. On the other hand, using local variables alone to predict the invasibility can lead to erroneous assumptions of suitability because factors acting at coarser scales (e.g. climate) may hamper the species’ establishment. This duality emphasizes the importance of using analyses at both local and regional/continental scales when estimating the invasibility of an area. Nevertheless, local analyses have been frequently ignored despite their potential to refine information about invasibility, an essential step for defining eradication/contention strategies of invasive species or for mitigating their effects in the native ecosystems (Loo et al. 2009). The red swamp crayfish (Procambarus clarkii, Girard 1852, hereafter ‘crayfish’) is native to southeastern North America and is currently an invasive species in five continents (Gherardi 2006). In the Iberian Peninsula the species was introduced in 1973 in southwestern Spain and was first recorded in SE Portugal in 1979 (Ramos and Pereira 1981). Since then, and as a result of natural dispersion and anthropogenic transport, its distribution is expanding to the north. In fact, humans have been playing a key role in this invasion, transporting crayfish to new sites and/or altering the landscape in a way that facilitates its spread and establishment (Siesa et al. 2011; Capinha et al. 2013). The negative impacts of this invader are largely known, affecting many taxa from aquatic plants (Correia 2003; Geiger et al. 2005; Carreira et al. 2013) to invertebrates (e.g. Correia 2003; Geiger et al. 2005; Correia and Anasta´cio 2008) and amphibians (e.g. Cruz et al. 2006, 2008; Nunes et al. 2010; Ficetola et al. 2011). Conversely, there may be positive impacts on higher trophic levels as a result of the role of this crayfish as an abundant and easy prey for many mammalian and avian predators (Correia 2001; Geiger et al. 2005). Either way, its establishment disrupts the structure and function of native ecosystems by changing the relations between species; as well as the flow of matter and energy (Geiger et al. 2005; Ficetola et al. 2012). During the last 40 years P. clarkii established itself in all of Portugal’s main river basins. Capinha and Anasta´cio (2011), using climatic and physiographic predictors, suggested that one of the distribution limits for this species will be the Northwest of the Iberian
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Peninsula; however these authors also suggested that most of the Northwest is suitable for this species. Moreira (2011) conducted a bibliographical analysis of more than 2,500 articles (scientific and grey literature including news in local journals) and found that: (1) the first records of the species in Northwest Portugal date back to the year 2000; (2) four years later the species was already present in all of the main river basins of this region (Fig. 1). However, i n spite of being present for at least a decade in this area this crayfish was apparently not able to colonize all the available freshwater habitats. This absence may be due to local limiting factors, such as steep slope and high water velocity, factors that have already been identified by other authors as limitative of P. clarkii expansion in other areas of the Iberian Peninsula (Cruz and Rebelo 2007; Banha and Anasta´cio 2011; Gil-Sa´nchez and Alba-Tercedor 2002; Maceda-Veiga et al. 2013). In this study we aimed to identify the most influential factors that are conditioning P. clarkii expansion in the fringe of its distribution. We first assessed the species presence and modelled its current distribution. We then evaluated how the inclusion of local variables improved Capinha and Anasta´cio (2011) model. With this information we expected to improve the identification of the areas at most risk of being invaded.
Methods Study area The study was conducted in the northwest of Portugal, a mountainous area (maximum altitude of 1.507 m) within the Atlantic climate region, with an annual mean temperature ranging between 7.8 and 15.6 °C and an annual precipitation of 981 mm to 1,705 mm. Along a SW–NE axis this area shows a gradient of increasing elevation, as well as a change from silt to rocky soils. There are just a few large lentic water bodies; the landscape is dominated by large extensions of different sized rivers (Fig. 1). The region is one of the most biologically diverse in Portugal, with approximately 30 % of its area under some type of legal protection (Fig. 1), which may constitute an opportunity to implement management strategies concerning the invasion by P. clarkii.
The red-swamp crayfish (Procambarus clarkii) Fig. 1 Study area within the Iberian Peninsula; Grey areas are under environmental protection— Natura 2000. Black dots represent dates when crayfish was first recorded in each basin before our sampling effort in 2010. Black open square indicates the area where the sampling efficiency was tested. Adapted from Moreira (2011)
Field survey To assess crayfish presence we sampled 97 river stretches spread throughout the study area, covering all of the main river basins (Fig. 1). Crayfish distribution data was collected from July to October 2010, matching the time of year when the species is more active (Gherardi et al. 2000). Sampling sites correspond to different sized rivers. Within this paper, ‘‘small rivers’’ refers to rivers with strahler levels of 1–2 (that is, with either one or no tributaries of their own) and which are \10 m wide; ‘‘medium sized rivers’’ are those with strahler 2–5, and which usually are between 10 and 30 m wide; and ‘‘main rivers’’ are those that flow directly to the sea and are more than 30 m wide. At each sampling site we performed a 100 m transect along the river bank and searched for signs of crayfish presence (live specimens or pieces of recently predated individuals). When no signs of crayfish were found, we installed five funnel traps (5 L containers with a 43 mm diameter opening) baited
with fish-tasted cat food. Traps were set until the presence of crayfish was confirmed or for a maximum of three consecutive nights. For each basin, the sampling was carried upstream, starting from the vicinity of the river mouth. Environmental variables To characterize each 100 m river stretch we used ten local environmental variables (for the complete list and methodological details see Table 1) plus the occurrence probability of P. clarkii according to the Capinha and Anasta´cio (2011) model (hereafter referred to as ‘‘regional model’’) which included the following variables compiled at 1 km resolution: annual mean temperature; mean temperature of warmest quarter; mean temperature of coldest quarter; annual precipitation; precipitation of wettest quarter and precipitation of driest quarter; altitude; slope; distance to ocean and a compound topographical index.
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F. D. Moreira et al. Table 1 Explanatory variables for modelling Variable
Type
Unit
Description
Total range and range of values used for modelling
Regional model
Continuous
–
Probability according to Capinha and Anasta´cio (2011)
0–0.82 0.56–0.81
Altitude
Continuous
m
GPS
4–369
Slope
Continuous
%
Steepness of the river segment
4–250 0–26.9 0–7.7 Width
Continuous
m
Distance between the two margins (wet region)
2–315 3–315
Depth
Continuous
cm
Water depth at each sampling site
Human impact
Ordinal
–
Anthropogenic disturbance 1) no disturbance;
20–130 30–120
2) small impacts (such as for leisure activities);
1–3 1–3
3) high human impact and river bed modification (e.g. channelling) Surrounding area
Ordinal
–
Land cover in the first 10 m of margin 1) natural vegetation;
1–3
2) afforestation or traditional small-scale agriculture;
1–3
3) human structures (houses, paved roads, etc.). Margin soil
Categorical
–
Soil type at the margin according to granulometry, from 1 (softest) to 4 (hardest)
1–4
River bed soil
Categorical
–
Soil type at the river bed according to granulometry, from 1 (softest) to 4 (hardest)
1–4
Margin vegetation
Categorical
–
Vegetation type at the margin classified in a landscape with increasingly more human impact
River bed vegetation
Categorical
–
Vegetation type at the river bed classified by the percentage of cover
Slope was extracted for each river segment from the Catchment Characterisation and Modelling database (Vogt et al. 2007) as a surrogate of water current velocity which has been pointed by several authors as having a negative impact on this species (Gil-Sa´nchez and Alba-Tercedor 2002; Cruz and Rebelo 2007). This slope is actually quite different from the one used by Capinha and Anasta´cio (2011) as it refers to the slope of the riverbed and not to the average slope of a one square kilometre cell. Additionally, we measured width and depth of the river on site since they can also influence current velocity. The variable Human impacts assessed the level of anthropological disturbance as it has been shown to positively influence the distribution of crayfish (Siesa et al. 2011; Capinha et al. 2013). The dominant habitat
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1–3 1–3 1–4 1–4 1–4 1–4
surrounding each site was characterized within a 10 m buffer along the river margin, since the surrounding habitat can influence river characteristics (Allan 2004). Soil and vegetation of the river margins and bed were characterized by identifying soil and vegetation types in ten points spaced out by 10 cm in three 1-m transects distributed randomly along each sampling point; counts of each type of soil and vegetation were then converted into percentages. Soil was characterized according to its granulometry as the crayfish has been referred to prefer silt surfaces because of its burrowing behaviour (Correia and Ferreira 1995). Margin vegetation was also characterized on the basis of its cover using an increasing magnitude of human intervention: from natural riparian forest to clear river
The red-swamp crayfish (Procambarus clarkii)
banks. High cover of margin vegetation can influence microclimate and deprive riverbed vegetation of light (Allan 2004). The same procedure was applied to river bed vegetation, one of the most important components of crayfish’s diet (Gutie´rrez-Yurrita et al. 1998). Although water flow has been found to influence crayfish distribution (Gil-Sa´nchez and Alba-Tercedor 2002; Cruz and Rebelo 2007; Banha and Anasta´cio 2011), it was not considered in our models because frequent and unpredictable discharges from dams located upstream in all the main rivers of NW Portugal cause a high temporal variability. Data analysis Sampling efficiency Before further analysis, we assessed the accuracy of our method in detecting the real absence of crayfish in sampled sites, by calculating the probability of not finding crayfish where the species is known to be present (Type II error). This test was performed in a small protected area (‘‘Paisagem Protegida da Lagoa de Bertiandos e S. Pedro d’Arcos’’) and nearby rivers (Fig. 1), in five different sites where crayfish is present, but with contrasting abundances—two sites with abundant populations (89 % occupied traps; 2.46 individuals caught per unit effort—CPUE) and three sites where crayfish is captured only occasionally (16 % occupied traps; 0.17 CPUE) (Rodrigues, unpublished data). We used 15 traps per site that were set for five consecutive nights and checked daily for crayfish presence. We built occupancy models (MacKenzie et al. 2002) separately for each type of site, using PRESENCE software (Hines 2006). We randomly selected five traps (out of the 15 that were used each night) to simulate one sampling unit, and results were then transformed to presence/absence. This random procedure of choosing five out of the 15 traps was repeated 1,000 times for each of the 5 days of sampling. We then estimated the detectability of the species using one to five sampling days, in both low and high abundance sites. Modelling procedures To investigate the relative influence of environmental variables in the crayfish distribution and predict the
potential area of expansion we fitted logistic regression models to our data. A logistic regression model was used to evaluate relationships between presence/ absence in our sampling sites and the set of environmental predictors depicted in Table 1. Presence/ absence data were defined as follows: for each main river basin, the invaded area was considered to encompass the entire basin starting from the most upstream site with confirmed presence. From this area, we selected all presences (n = 46) for model building. Absences were also chosen within the invaded area, based on whether or not the site was located nearby a site known to have crayfish; only those sites where crayfish was not recorded and were distanced\10 km from a known ‘presence’ were considered ‘absences’ (n = 16) (see Fig. 3). This is an important step to insure that the equilibrium assumption is met (Guisan and Zimmermann 2000). Before model building we evaluated the collinearity between the variables, using the Variation Inflation Factor, which shows how much the regression coefficient is increased due to collinearity. Variables with a Variation Inflation factor above five were removed from the subsequent analysis (Craney and Surles 2002). This procedure removed Altitude and Depth from further analysis. We constrained our models to include the variable representing the probabilities of species occurrence projected by the regional model for each sampling site, while the other remaining eight variables were combined in all possibilities in a total of 255 models (hereafter referred to as ‘combined models’). Model selection procedure was based on the Akaike Information Criteria adjusted for small sample sizes (AICc, Burnham and Anderson 2004). In order to identify those models with larger support, Akaike weights (wi) were computed for each candidate model (dAICc \ 4, Richards 2005). A Moran’s I test was performed to check for spatial autocorrelation in the residuals of the best fitted model. For that purpose a distance matrix among all sites was computed based on distances along the river network. Dispersion between river basins was considered to be negligible. We assessed the classification accuracy of the different models using a leave-one-out cross-validation to compute two commonly used measures (Allouche et al. 2006): area under the ROC (AUC) and true skill statistics (TSS). For TSS computation
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F. D. Moreira et al. Fig. 2 Species distribution model by Capinha and Anasta´cio (2011) and current distribution of crayfish. Black to white gradient represents a decrease in suitability of the area. Black and white dots represent crayfish presence and absence sites, respectively. Dashed line represents the separation of the invaded (left-side) and non-invaded area (rightside) (see ‘‘Methods’’ for details)
and presence/absence predictions, we used species prevalence as the cut-off probability. To map the probability of colonization of the river stretches that were not directly sampled, we first divided the best combined model into two simpler models: (1) a ‘Regional model’ represents the probability of distribution according to Capinha and Anasta´cio (2011), as explained previously; and (2), a ‘local model’ that was built with the two local variables included in the combined model. This local model does not represent the result of the modelling selection procedure. Using the partial responses of river width, we could determine, to any given slope, the critical value for the model to change its prediction from absence to presence, based on the cut-off probability.
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River width was calculated with the aerial images from the software Google Earth (version 7.1.2.2041) to any given site. We used this function to classify all the river stretches of the study area as susceptible of being invaded, or protected from invasion. This separation between presence and absence sites was clear for most stream sections, either with steep slopes (where the species would require wide rivers to be present) or moderate slopes (where the species would still be able to establish in narrow rivers).We then overlapped the map resulting from this approach with the regional model in order to trace the species potential distribution boundaries in each basin. All statistical analyses were performed in R software version 2.12 (R Development Core Team 2012).
The red-swamp crayfish (Procambarus clarkii) Table 2 Candidate combined models and the best possible model with local variables AICc
dAICc
Akaike weight
TSS
AUC
Regional (?); Slope (-); Width (?)
32.68
0
0.16
0.64
0.94
Regional (?); Slope (-); Width (?); Riverbed veg. (-) Regional (?); Slope (-); Width (?); Human imp (?)
34.61 34.70
1.93 2.02
0.06 0.06
0.64 0.64
0.94 0.93
Regional (?); Slope (-); Width (?); Margin veg. (-)
34.78
2.10
0.06
0.64
0.94
Regional (?); Slope (-); Width (?); Margin soil (-)
34.85
2.17
0.05
0.64
0.94
Regional (?); Slope (-); Width (?); Surround. area (?)
34.98
2.30
0.05
0.64
0.94
Regional (?); Slope (-); Width (?); Riverbed soil (-)
34.99
2.31
0.05
0.64
0.94
Regional (?); Slope (-); Width (?); Human imp (?); Margin veg. (-)
36.45
3.77
0.02
0.66
0.93
Regional (?)
62.44
29.76
0.00
0.21
0.72
Combined models
AICc and wi were computed using the R package MuMIn (Barton´ 2009). Moran’ I values and tests were computed with the R package spdep (Bivand 2007).
Results Sampling efficiency Sampling efficiency was very high in sites with high crayfish abundance, with 100 % probability of detection within the first day with any given set of five traps. At low abundance sites the probability of detecting crayfish within 1 day of sampling was also high (78.6 %) and after 3 days of sampling it increased to 97.4 %. On the basis of these results, we set 3 days as the minimum number of days necessary to confirm crayfish presence at any given site. Field survey We found crayfish in 46 out of 97 sampling sites. The species is mostly present at lower altitudes and is apparently more widespread in the southern part of the study area, where it has supposedly arrived earlier (Fig. 2). Combined suitability models The overlap of the current distribution of crayfish with the regional model highlighted many river segments located in the non-invaded area with high environmental
suitability for the species, and conversely absences in river segments showing high suitability index values in the invaded area (Fig. 2). When considering only the combined models, eight models best fitted our data (Table 2). All of these represent a considerable increase in predictive power when compared with the regional model. Furthermore, all the models include a common set of three variables (regional, width and slope) and therefore we considered the simpler nested model as the final model (Richards et al. 2011). Slope and river width were the most important variables being included in all of the best combined models. We used these variables to represent a probability of colonization of a given site. Plotting the results of width versus slope, two types of sites emerged: wide rivers in flatlands (crayfish presence) and narrow rivers and streams in moderate to steep slopes (crayfish absence). The results from our model were then mapped for the whole study area, indicating that crayfish will likely invade most of the extent of the main rivers, as well as some of the medium sized rivers. Interestingly, most of the small rivers seem to have a lower risk of invasion (Fig. 3). Both the local model and the regional model agreed in 61.9 % of the river segments, with 28.2 % agreement in predicted presences and 33.7 % in predicted absences. The remaining corresponded almost exclusively (37.4 %) to segments with a high suitability index according to the regional model and a low suitability index according to the local model, with \1 % for the opposite condition (Fig. 4).
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F. D. Moreira et al. Fig. 3 Species distribution model in the study area. Grey areas under environmental protection— Natura 2000. Dashed line represents the edge of invasion (see ‘‘Methods’’ for details). Black lines are segments of river predicted as presence and the grey ones predicted as absences. Black and white dots indicate presence and absence sites, respectively, while the bigger white dots indicate the absences that were used for modelling
Discussion The current distribution of P. clarkii in the NW of Portugal is clearly the result of the interplay between regional and local variables. In fact, there was a significant improvement in predictive power by combining the local variables with the regional model. The combined model suggests that some invaded rivers apparently maintain large stretches that are not likely to be invaded. The same applies to the non-invaded area where it seems that the risk of invasion is lower than predicted when considering only the regional scale. Most agreements depicted in Fig. 3 were restricted to the primary rivers (in predicting presence) and to small rivers at high altitudes (in predicting
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absence). On the other hand, disagreement between models was mostly associated with medium sized rivers at intermediate to low altitudes. Bioclimatic models usually predict the potential distribution of a species at coarse to medium scales but cannot incorporate local variability that plays a major role in the distribution of a species in a given area (Loo et al. 2009; Reino et al. 2013; Wearne et al. 2013). Thus, the inclusion of local variables is expected to refine such predictions at finer scales, a critical step when assessing future routes of invasion, identifying areas where to concentrate conservation efforts and support management decisions (Fig. 4). The local variables that help predict the distribution of crayfish in NW Portugal were generally similar with
The red-swamp crayfish (Procambarus clarkii) Fig. 4 Comparison between the species distribution models of Capinha and Anasta´cio (2011) and of the present study. Black both the regional and local models show high suitability index; Green both the regional and local models show low suitability; Blue the regional model indicates a high suitability and the local model a low suitability; Red the regional model indicates a high suitability and the local model a low suitability. Grey areas are under environmental protection—Natura 2000. Dashed line represents the edge of invasion
variables included in other published models (GilSa´nchez and Alba-Tercedor 2002; Cruz and Rebelo 2007; Banha and Anasta´cio 2011). River width and slope were chosen for their positive and negative relationship, respectively, on the potential distribution of the crayfish. This result was expected since crayfish preference for wide and slow streams is well documented (e.g. Gil-Sa´nchez and Alba-Tercedor 2002; Cruz and Rebelo 2007). It should be noted that not all the variability range of these and other variables was used for calibrating models, because some the sites that were not exposed to the crayfish invasion could not be considered as true absences. This may have cause some problems when extrapolating the model for the whole study area. However, we think the range
of conditions that was omitted in model calibration is the most unsuitable for the species (e.g. very steep sites) and therefore will have little implications on model transferability. In contrast with the results of Siesa et al. (2011), we found no significant association between crayfish presence and human dominated areas. This may result from a lower degree of human impact in NW Portugal than in Lombardy in Italy, as well as from the type of aquatic habitat considered which is mainly lentic in Italy and entirely lotic in this study. Mapping the predictions of two models, one based on width and slope (local variables) and another based on macroecological factors (regional model) revealed a similar prediction for 62 % of the river stretches
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considered, while the remaining ones seemed to almost exclusively show a high suitability according to the regional model and a low suitability according to the local model. This is the result of a weak prediction ability of the regional model for medium size tributaries. Since temperature was the most important variable for the regional model (Capinha and Anasta´cio 2011), we assume that these habitats may have an adequate temperature for crayfish, but nevertheless are steep (and water flow will probably be strong), which makes them unsuitable for this species. On the contrary, stretches for which the regional model indicated a low suitability and the local model a high suitability were rare as high altitude plateaus with large and less steep rivers stretches are very uncommon. This too might be explained by the fact that low temperature habitats will mostly be located at high altitudes, where rivers will be both narrower and steeper. Since the variables chosen in both models are related to altitude, one could expect crayfish to be present at low altitudes and slowly disappear as altitude increases. However, this may not always be the case as the current edges of crayfish distribution in the most northern basins of the study area are located at an altitude of approximately 25 m a.s.l. albeit this species is known to occur up to varying altitudes such as 190 m a.s.l in the SW Portugal (Cruz and Rebelo 2007), 572 m a.s.l. in the NE Portugal (Bernardo et al. 2011) and 750 m a.s.l. in southern Spain (MacedaVeiga et al. 2013). The restriction of crayfishes to low altitude sites in the northern edge of the study area may be due to insufficient propagule pressure due to limitations to natural dispersal. However, the river located just north of the area where we tested sampling efficiency (river Estora˜os) had a nearby (3.3 km) high density site for more than 10 years, and yet the crayfish altitudinal limit in this river is still very low (51 m a.s.l.). Individuals of this species are known to disperse upstream up to 1.74 km/year in other edge areas (Bernardo et al. 2011); the fact that they were not able to disperse over 3.3 km during a 10 years period suggests that low altitude regions of NW Portugal are less suitable to this species. This may be due to a stronger stream flow, lower overall temperature or even to other unassessed variables such as the stream’s chemical properties (e.g. calcium concentration) at each site (Cairns and Yan 2009; Favaro et al. 2010).
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One of the assumptions for building a presence/ absence model is that the species is at equilibrium and violating this assumption may have severe consequences on the results (Guisan and Zimmermann 2000; Gallien et al. 2012; Vaclavik and Meentemeyer 2012). This is especially important for invasive species during the invasion process since these are the ones that violate this assumption more severely while being also the ones for which predictions of potential distributions are most desirable. An important assumption of modelling approaches based on presence/absence data is that locations where the species is absent are truly unsuitable. To ascertain this, all the sites in the three northernmost rivers that had no crayfish in the 2010 survey were sampled again in 2012 and were found to be still devoid of crayfish. However, overlaying the current distribution of crayfish with the results of any of the models, it is clear that there should be suitable habitat for the species in the non-invaded area and lack of crayfish in some places may be just the result of a slow expansion. Another hypothesis is that environmental variables other than the ones accounted for in these models are preventing the species from expanding its distribution. A possible effect of physical barriers is not plausible, considering that no physical barriers were found at the current edge of invasion in any of the rivers. We therefore assume that the sites we considered as true absences still are or have been unsuitable for crayfish in the last 5–10 years. However, climate change may considerably change this picture, particularly if precipitation/ river flow are affected (Capinha et al. 2013). Conservation implications Approximately 30 % of NW Portugal is under some sort of environmental protection. Therefore progression of crayfish in this region may increase the impacts on several endemic species and communities. Most feared are those on native amphibians (e.g. Cruz and Rebelo 2005) and fishes (e.g. Correia 2003) but also important are those on macroinvertebrates (e.g. Geiger et al. 2005; Correia and Anasta´cio 2008) and, indirectly, on macroinvertebrate-feeding species like the threatened Pyrenean desman (Galemys pyrenaicus). Crayfish may also have a direct influence on riverbed vegetation by damaging soil structure and increasing water turbidity through its burrowing
The red-swamp crayfish (Procambarus clarkii)
behavior (Correia and Ferreira 1995; Angeler et al. 2001). Last but not least, its recognised impacts on the higher trophic levels that will influence the whole ecosystem. Particularly important is the role it may play in supporting populations of native species (such as the European otter Lutra lutra); it may also be a facilitator of the expansion of another new invader in NW Portugal, the American mink (Neovison vison) (Rodrigues et al. 2014), that in turn exerts predation over the local biodiversity, strengthening the negative impacts of the crayfish. Due to crayfish biological and ecological traits and continuing human mediated dispersion, most of the Iberian Peninsula is seen as a lost cause in terms of preventing its expansion. However, this work suggests that crayfish might not colonize as many habitats as one would expect. The current abundance levels and the extent to which the species has spread indicate that an eradication program is no longer feasible. Instead, a management program, coupled with regular monitoring efforts, is urgent and critical to understand and prevent its further progression. New insights on the needs of this species at a local scale are essential as these are easier to act on through management programs. The use of physical barriers on rivers (Kerby et al. 2005; Rosewarne et al. 2013) to control the numbers of crayfish that progress upstream is a management option that has already shown promising results in another region of the Iberian Peninsula (Dana et al. 2011). These barriers would function as a high slopes that would increase water flow. However, as a barrier will impact multiple other species, its use might be controversial and will impose mitigation measures, meaning that it cannot be applied at a widespread scale and needs a detailed and scientifically sound planning. Today P. clarkii is the most widely introduced crayfish in the world. While it is an abundant and widespread species in southwestern Europe, it is currently spreading in central and even northern Europe, as well as in other hilly or mountainous temperate areas of the world (Gherardi 2006). In these regions the physiographic and climatic conditions encountered by this invasive are relatively similar to those of NW Portugal and the constraints on its dispersal identified in this work may be used to help to contain its invasion. Also important is the recognition that the high invasion rate of the crayfish is a human-mediated
process, already referred by Capinha et al. (2013) at the European level, but particularly evident in our study area (similar years of arrival in distant basins). This evidence reinforces the urgent need of informing local stakeholders of the consequences of the species expansion to new areas, developing local solutions and promoting social awareness of this environmental problem at the national level. Acknowledgments This study was financed by FCT— Fundac¸a˜o para a Cieˆncia e Tecnologia (Portugal) through project DILEMA—Alien species and conservation dilemmas: the effects of native competitors and alien prey species on the spread of American mink in Portugal (PTDC/BIA-BECD/ 102433/2008). The authors would like to thank the staff at ‘‘Paisagem Protegida da Lagoa de Bertiandos e S. Pedro d’Arcos’’ for logistic support and everyone involved in the fieldwork. The authors would also like to thank G.F. Ficetola and an anonymous reviewer for their overall contribution and consequent improvement of the manuscript.
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