Research Article |
Corresponding author: Francesca Della Rocca ( fdellarocca@gmail.com ) Academic editor: Giuseppe Maria Carpaneto
© 2017 Francesca Della Rocca, Giuseppe Bogliani, Pietro Milanesi.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Della Rocca F, Bogliani G, Milanesi P (2017) Patterns of distribution and landscape connectivity of the stag beetle in a human-dominated landscape. In: Campanaro A, Hardersen S, Sabbatini Peverieri G, Carpaneto GM (Eds) Monitoring of saproxylic beetles and other insects protected in the European Union. Nature Conservation 19: 19-37. https://doi.org/10.3897/natureconservation.19.12457
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Urbanisation and the spread of agriculture have resulted in high levels of forest loss, habitat fragmentation and degradation in many regions of the world. In Italy, the Po Plain is the most human-dominated landscape of the country and, after decades of exploitation, old-growth forests have been reduced to small and isolated patches, often threatened by invasive tree species such as the black locust (Robinia pseudoacacia). In these habitats, the occurrence of many forest-dependent species is related to the quality and availability of suitable areas, as well as the connectivity between the remaining forested patches.
Thus, recently developed species distribution models have been applied, namely the Ensemble of Small Models (ESMs), to identify areas of occurrence for a rare and protected saproxylic beetle species, the stag beetle Lucanus cervus and the inverse of the resulting distribution maps as resistance maps have been used to estimate landscape connectivity for this species.
Response curves suggested that the probability of the stag beetle occurrence increased with habitat diversity, grassland coverage and native forests, especially oak and mixed forests. The other forest coverage, such as those with black locust, beech, chestnut and black cherry, showed a unimodal relationship peaking approximately at 70%, 8%, 55% and 13% respectively. The stag beetle occurrence was unimodal related to distance to watercourses and distance to human settlements and negatively related to shrub-lands, croplands, sparse and dense human settlements. Landscape connectivity showed similar patterns, except for oak forest coverage, which showed a negative relationship to landscape connectivity.
In conclusion, stag beetles can persist in a human dominated landscape only in the presence of forest patches, including those with black locust trees. It is also inferred that ESMs may be suitable for modelling rare species distributions and estimating landscape connectivity to promote species conservation.
Circuitscape, invasive species, Lucanus cervus, Robinia pseudoacacia, Species Distribution Models
Urbanisation and intensive agriculture have resulted in high levels of forest loss and natural landscape fragmentation in many areas of the world (
In this context, many forest-dependent animals are at risk of extinction, especially those with limited dispersal abilities, such as many saproxylic insects. Usually, these species are highly specialised and are linked to specific forest resources such as the amount of dead wood and particular conditions of temperature, humidity and fungal associations (
Thus, forest planning and management should explicitly include connectivity assessments, identifying the most suitable forest sites for the maintenance of habitat connectivity in human dominated landscapes (
Species distribution models (SDMs) have helped conservation planning for threatened species by identifying sites in which environmental conditions are favourable, especially in those areas where the species is not present and where restoration programmes could therefore be focused (Guisan et al. 2013). However, their predictive accuracy decreases drastically due to model overfitting when species with limited occurrences (such as rare species) and multiple predictor variables are considered in the models (
Landscape connectivity is increasingly estimated through circuit theory-based methods (
Although recent studies developed conservation frameworks using ESMs to identify high conservation priority areas (
Thus, the aims of this paper were (i) to identify habitat requirements for the stag beetle in a human-dominated landscape by developing ESMs, (ii) to identify ecological corridors for this species using the resulting probability of occurrence map of ESMs as a resistance surface in independent node-based models and (iii) to verify whether the above mentioned invasive tree species are related to stag beetle occurrence and landscape connectivity.
The empirical data set consisted of the locations of the stag beetle from one of the most human-dominated landscapes in Europe, the Po Plain in Italy (
The study was conducted in an area of about 1,025 km2 within the Varese Province in the Lombardy region of northern Italy. Elevation in the study area ranges from a few metres above sea level (a.s.l.) near the Ticino River to about 600 m a.s.l at the foothills of Campo dei Fiori, above the Varese lake. The climate is temperate sub-continental (
Study area and stag beetle sampling locations (black dots with white circles). Filled black dots indicate investigated sites where the species was not recorded. Black lines indicate the borders of the study area.
Despite its important ecological role and the high biodiversity value of the Ticino Valley Regional Park, which has been acknowledged as the MAB Biosphere Reserve “Valle del Ticino” (
The target species, the stag beetle, is considered as threatened in several countries within its geographical range (
A set of 18 predictor variables were derived that were contiguously available for the entire study area (Table
Variables used in the development of stag beetle (Lucanus cervus) Ensemble of Small Models. Variables with Variance Inflation Factor (VIF) > 3 have to be removed due to multi-collinearity with other variables. Average values ± standard deviations at sampled and presence sites are also shown.
Variable | VIF | Sampled sites | Presence sites |
---|---|---|---|
Native broad-leaved forests (%) | 2.854 | 26.35 (±38.21) | 28.76 (±40.71) |
Mixed woods (broad-leaved and coniferous) (%) | 2.507 | 19.06 (±38.25) | 19.62 (±39.54) |
Oak forests (%) | 1.081 | 9.87 (±23.74) | 14.66 (±29.31) |
Beech woods (%) | 1.001 | 6.34 (±26.93) | 8.85 (±34.07) |
Chestnuts woods (%) | 1.629 | 3.76 (±17.21) | 6.11 (±21.75) |
Distance to watercourses (m) | 1.416 | 804.24 (±576.81) | 859.34 (±626.99) |
Shrub-lands (%) | 1.121 | 7.85 (±6.11) | 9.27 (±7.01) |
Grasslands (%) | 1.843 | 14.12 (±22.02) | 10.48 (±19.12) |
Croplands (%) | 2.155 | 10.01 (±21.31) | 10.67 (±23.13) |
Invasive broad-leaved forests (%) | 1.002 | 3.14 (±2.25) | 3.09 (±2.22) |
Black cherry woods (%) | 1.006 | 3.12 (±2.41) | 3.21 (±2.51) |
Black locust woods (%) | 1.979 | 9.41 (±22.71) | 8.95 (±23.32) |
Other woods (%) | 1.011 | 4.56 (±12.36) | 5.50 (±15.16) |
Shannon diversity index of habitats (unitless) | 1.307 | 1.81 (±0.59) | 1.71 (±0.56) |
Dense human settlements (%) | 2.961 | 2.18 (±4.88) | 1.91 (±6.15) |
Sparse human settlements (%) | 2.989 | 4.59 (±13.47) | 4.76 (±14.41) |
Distance to human settlements (m) | 2.582 | 166.57 (±130.69) | 180.35 (±148.06) |
Distance to roads (m) | 1.701 | 221.89 (±257.03) | 167.67 (±200.11) |
To avoid multi-collinearity amongst predictors, the Variance Inflation Factor (VIF) was calculated. Following
The ESMs approach is based on the development of all the possible bivariate models (only two predictors at a time out of a larger set of predictors), followed by their combination into an ensemble (
20-fold split sampling (90% training data and 10% test data) was used to evaluate the bivariate models and the resulting ESMs. Similarly to
A resistance map was derived as the inverse (1 – probability of occurrence) of the resulting map from ESMs and thus it was combined with circuit theory to explore landscape connectivity (
Since high current is produced near nodes, using species locations as nodes could lead to a biased estimation of landscape connectivity (
A total of 222 specimens were found, 167 males and 55 females, in 21 of the 34 sites monitored (Table
Number of specimens collected within the study area in each sampling site from 2012 to 2015. (F= Female; M= Male).
Site | Locality | Years | Total | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
2012 | 2013 | 2014 | 2015 | M | F | ||||||
M | F | M | F | M | F | M | F | ||||
1 | Buguggiate | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
2 | Azzate | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
3 | Galliate lombardo | 1 | 1 | 2 | 0 | 1 | 0 | 2 | 0 | 6 | 1 |
4 | Casale litta | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
5 | Inarzo | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
6 | Inarzo | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
7 | Vergiate | 5 | 1 | 4 | 2 | 0 | 1 | 2 | 1 | 11 | 5 |
8 | Casciago | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 3 | 1 |
9 | Barasso | 1 | 0 | 2 | 0 | 3 | 0 | 3 | 2 | 9 | 2 |
10 | Biandronno | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
11 | Biandronno | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
12 | Besozzo | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
13 | Malgesso | 0 | 1 | 1 | 0 | 1 | 0 | 2 | 0 | 4 | 1 |
14 | Brebbia | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
15 | Travedona-Monate | 2 | 1 | 0 | 1 | 1 | 0 | 3 | 0 | 6 | 2 |
16 | Cadrezzate | 1 | 0 | 1 | 0 | 2 | 0 | 1 | 0 | 5 | 0 |
17 | Cadrezzate | 10 | 5 | 4 | 2 | 4 | 1 | 4 | 0 | 22 | 8 |
18 | Taino | 5 | 0 | 5 | 0 | 2 | 0 | 1 | 1 | 13 | 1 |
19 | Vergiate | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
20 | Sesto Calende | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
21 | Vergiate | 2 | 0 | 1 | 0 | 1 | 0 | 2 | 0 | 6 | 0 |
22 | Bodio Lomnago | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 4 | 0 |
23 | Bregano | – | – | 0 | 2 | 1 | 0 | 3 | 0 | 4 | 2 |
24 | Vergiate | – | – | 1 | 0 | 1 | 0 | 1 | 0 | 3 | 0 |
25 | Vergiate | – | – | 2 | 0 | 1 | 0 | 2 | 1 | 5 | 1 |
26 | Arsago Seprio | – | – | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
27 | Sesto Calende | – | – | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
28 | Sesto Calende | – | – | 2 | 0 | 1 | 0 | 1 | 0 | 4 | 0 |
29 | Castano Primo | – | – | – | – | 11 | 8 | 7 | 3 | 18 | 11 |
30 | Castano Primo | – | – | – | – | 2 | 0 | 3 | 1 | 5 | 1 |
31 | Lonate Pozzolo | – | – | – | – | 2 | 0 | 4 | 1 | 6 | 1 |
32 | Vizzola ticino | – | – | – | – | 7 | 4 | 5 | 3 | 12 | 7 |
33 | Vizzola ticino | – | – | – | – | 6 | 3 | 3 | 5 | 9 | 8 |
34 | Vizzola ticino | – | – | – | – | 8 | 2 | 4 | 1 | 12 | 3 |
Multi-collinearity was not detected amongst the predictors (VIF > 3) and thus they were all considered in further analyses (Table
a Suitability areas for the stag beetle derived by the Ensemble of Small Models (green and black areas indicate suitable and unsuitable areas, respectively) b Resistance surface for the stag beetle (green-red scale indicates lower-higher resistances, respectively) derived by the Ensemble of Small Models, ESMs (1- probability of occurrence) c current map for the stag beetle (black-yellow scale indicates lower-higher connectivity, respectively) derived from resistance surface using Circuitscape software.
The probability of occurrence for the stag beetle increased with habitat diversity and native broad-leaved forests, oak forests and grassland. The other forest coverage, such as those with black locust, beech, chestnut and black cherry showed a unimodal relationship peaking approximately at 70%, 8%, 55% and 13% respectively (Fig.
The probability of occurrence for the stag beetle was unimodal related to distance to watercourses and distance to human settlements (with a peak at 800m and 180m respectively) and negatively related to shrub-lands, croplands, sparse and dense human settlements and increased distance to roads (Fig.
Response curves and 95% confidence intervals (in grey) of the probability of occurrence of the stag beetle derived by the Ensemble of Small Models in relation to predictor variables values.
Using the inverse of the probability of occurrence as a resistance surface (Fig.
The landscape connectivity showed similar patterns of relationships with the considered predictor variables, except for oak forest coverage which tended to decrease as landscape connectivity increased (Fig.
This study took place within projects aimed at preserving the natural habitats in a human-dominated landscape that play a crucial role in connecting the Mediterranean basin to Northern Europe. In this context, suitable areas were identified for stag beetle reproduction and fundamental corridors for this species during dispersal. The most recent and robust species distribution and landscape connectivity modelling techniques were applied. The results confirmed that occurrence and connectivity of the beetles is related to natural habitats instead of anthropogenic habitats. Moreover, the research highlighted a complex puzzle in how managing invasive tree species (such as black locust thicket) may provisionally help in maintaining native animal populations in human-dominated landscapes. The research also identified that ESMs may be suitable for modelling rare species distributions and estimating landscape connectivity, provided that detectability problems are overcome.
SDMs and landscape connectivity based on resistance surfaces are not without caveats. On one hand, the predictive accuracy of SDMs decreases considerably when rare species are considered in the models, as few species’ occurrences and many predictor variables lead to model overfitting and thus reduced generalisation and applicability of the models (
However, ESMs can overcome model-overfitting for rare species and thus provide more accurate predictions compared to standard SDMs (
Despite their high predictive accuracy, ESMs have not been often used to model rare species distribution and they have never been used to derive resistance surfaces to model landscape connectivity. Thus, in this study, a novel application of ESMs has been provided and it is inferred that they might be valuable tools for estimating unbiased landscape connectivity. Since the presence of rare species and landscape connectivity are amongst the most frequently cited criteria for site selection by conservationists (
Suitable habitats for the stag beetle in the study area were mostly located in the largest patch of contiguous forest along rivers and lakes within the two natural parks, the Ticino Valley Regional Parkand Campo dei Fiori Park. Less suitable habitats were identified in the south-eastern part of the study area, which includes an intensive agricultural matrix, the Milano Malpensa airport and several cities surrounding Milan.
The relationship between the probability of occurrence predicted by ESMs and the predictor variables considered in this study is consistent with the ecological requirements of the species (
This result is of interest as no previous information on the use of invasive woodlands by the stag beetles has been available.
The idea of a negative impact of invasive tree species on native species and ecosystems is generally supported (
It was found that the black locust forest can contribute to stag beetle occurrence if its coverage does not exceed 70% of the landscape. This means that the remaining 30% should be represented by other tree species or forest types, especially oak trees and broad-leaved forests. It is possible that, despite the extensive spread of invasive forests, the occurrence of the stag beetle is assured by suitable natives tree species that remain in a small proportion scattered in the invaded area. This occurred in northern Spain, where the stag beetle was found in a Eucalyptus plantation due to the presence of very old chestnut trees remaining within those plantations (Marco Mendez personal comm).
These results showed that the stag beetle remains within the proximity of urban settlements and is positively affected by the presence of roads although it does not seem to be as anthropophilic as in other European countries such as Belgium or Great Britain (
Considering landscape connectivity, the stag beetle was directly related to the high coverage of woodlands. However, oak forests did not seem to be as important for the species during movement and dispersion compared to other forest types. This phenomenon is probably due to the difference that naturally exists between the habitats used for dispersion and those used for reproduction, as has already been reported for other species (
Finally, human settlements (both sparse and dense) were the main barriers for movement and dispersion of the stag beetle, while open habitat, especially grasslands and roads, represented important components for the species movement.
A successful management policy for the protection of threatened animal species in human-dominated landscapes should take into account strategies for ensuring the persistence of good-quality habitats and landscape connectivity (
According to a recent report on the distribution and conservation status of species and habitats of Community Interest in Italy (
These findings also showed that black locust deadwood can be considered in forest management operations aimed at restoring habitats for the reproduction of the stag beetles and which can serve as a temporary food source for the larvae. However, in light of a conservation strategy for this saproxylic beetle, it is essential to ensure the presence of other forest types, especially mixed broadleaved forests and to use black locust only in combination with oak deadwood.
This study was supported by the project LIFE 10/NAT/IT/241 – TIB_TRANS INSUBRIA BIONET “Connessione e miglioramento di habitat lungo il corridoio ecologico insubrico Alpi – Valle del Ticino and the project AMBROSIANO “Studio per il miglioramento della connettività ecologica tra il Parco Alto Milanese e il Parco del Ticino: contenimento dell’ambrosia ed incremento della biodiversità”. We thank Guido Bernini, Silvia Stefanelli and the students Alessantro Tonali and Riccardo Resente for helping during data collection and to Giuseppe Maria Carpaneto, Alessandro Campanaro, Marco Mendez and an anonymous reviewer for their helpful comments on a former version of this manuscript.
Special issue published with the contribution of the LIFE financial instrument of the European Union.