Do coniferous plantations have an importance for biodiversity conservation? Bats as indicators in the Foreste Casentinesi National Park

doi:10.21203/rs.3.rs-3241583/v1

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Do coniferous plantations have an importance for biodiversity conservation? Bats as indicators in the Foreste Casentinesi National Park

https://doi.org/10.21203/rs.3.rs-3241583/v1

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Artificial forest and plantations are gaining an ever more central importance in the discussion about forest role in biodiversity conservation, climate change mitigation and in many aspects related to social and economic importance of these environment.

Particularly to what regards biodiversity conservation, there is a heated debate on the potential role that these artificial habitats, if properly managed, could have as species habitat.

We evaluated the role of these forest for bat species in the Foreste Casentinesi National Park, that hosts widespread coniferous plantations, many of which have now an "old-growth" type structure, with numerous large trees, relatively low densities and abundant deadwood. These forest, if compared with beech forest, the other most spread forest type, have also a more diversified structure.

Our results show a strong positive effect associated with the presence of conifer forests for four out of eight species tested, specifically P. pipistrellus, P. kuhlii, H. savii, M. schreibersi. The presence of coniferous plantations has effect at different spatial scale, suggesting that different species use this habitat in different way. Coniferous plantations have also a positive effect on the number of feeding buzz, the specific sequence of ultrasound impulses bats emitting during the approaching to preys.

Although other studies have also shown that these forests can actually play a role as habitat for numerous species, including other taxa, there is still a widespread belief, even among those with active roles in forest management, that these environments are "green deserts". Particularly inside protected areas, we believe that work should be done to identify and apply correct forms of management for these forests instead of generally promoting their replacement with native species.

Forests are universally acknowledged as a habitat of primary importance for bats. According to Lacki et al. (2007), over half of North America’s bat species depend on forests, and this percentage appears to be even higher in Europe (Russo et al. 2016). The relationship between forests and bats becomes even more evident in tropical forests (Medellin et al. 2000), where co-evolution between plants and bats has made the latter one of the main drivers of dispersal for countless plant species (Muscarella & Fleming 2007), to the extent that they play a key role in rebuilding damaged forests (Whittaker & Jones 1994). Depending on their composition, structure, and the characteristics of the broader landscape they are inserted in, woodlands and forests provide key shelter and feeding grounds for a great many species (Cervenỳ & Burger 1989, Kaňuch & Kaňuch 2005, Fuentes-Montemayor et al. 2013, Dodd et al. 2012, Kalda et al. 2014). In recent years, a growing focus on the ecology of forest-dependent bat species and their conservation has made it possible to delve into the effects that various forms of forestry management have on bat communities and individual species (Graves et al. 2000, Barclay & Kurta 2007, Ruczyński et al. 2010, Russo et al. 2010, Titchenell et al. 2011, Tillon et al. 2018). Some authors have shown that even in areas with high degrees of forest exploitation there are several key factors that can influence the presence of certain species. Forests that are not excessively dense (Erikson & West 2003, Regnery et al. 2013, Blackey et al. 2016) and with a diversified structure (Russo et al. 2010 & 2016), including in terms of microhabitats (Paillet et al. 2018), usually host a higher number of species, and often of greater conservation interest. Another feature that some authors believe has a significant positive impact on the presence of bats in forest environments is the quantity of dead wood, both standing and lying (Rignery et al. 2013, Bouvet et al. 2016), although agreement on this point is not unanimous (Tillon et al. 2016). An aspect that has instead received limited attention, at least until recently, is the composition of forests, in particular whether there are differences between deciduous and conifer woodlands, especially those of artificial origin (Russo et al. 2016). In the future, this aspect is likely to become increasingly relevant, since in much of Europe many conifer woodlands are of artificial origin, and their management has been under discussion in recent years, especially concerning their potential role in protecting biodiversity and reducing pressure on natural forests (Stephens & Wagner 2007).

Unlike in North America (Baker et al. 2008, Erickson & West 2009, Lacki et al. 2007), the vast majority of studies on the ecology of forest bats in Europe, and especially in the Mediterranean countries, has focused on deciduous forests; save for a few exceptions, limited attention has been paid to conifer forests (e.g. Kaňuch et al. 2006).

Particularly in the Mediterranean region, the few studies that have been carried out have mostly taken place in habitats of limited environmental value, such as young Mediterranean pine plantations with a very simplified structure, sometimes due to the recurrence of wildfires (Russo & Jones 2003, Zahn et al. 2005, Davy et al. 2007, di Salvo et al. 2009). Indeed, if we exclude a handful of other examples, most coniferous woodlands in the Mediterranean basin seem to be of limited interest for biodiversity (Andrés & Ojeda 2002, Maestre & Cortina 2004, Gonzales-Moreno et al, 2011).

There are nevertheless some exceptions, which are increasingly widespread in Europe, concerning mature conifer forests of artificial origin, particularly in protected areas, which have long been managed according to conservation criteria and which are now of great natural interest (cf. Humphrey et al. 2013, Calladine et al. 2018). One such example is the Foreste Casentinesi (Tellini Florenzano et al. 2004), a vast woodland complex straddling the Tuscany and Emilia regions in Italy, which is now protected by National Park and no less than 12 Natura 2000 sites. In addition to small native conifer forests (European silver fir Abies alba), the area hosts extensive artificial conifer forests (European silver fir, Norway spruce Picea excelsa, Douglas fir Pseudotsuga mnziesii and Austrian pine Pinus nigra). Careful management have allowed many of these woodlands to develop some characteristics typical of old-growth forests, in spite of their exploitation for productive uses. Some silver fir stands are over 150 years old, with a diversified structure, many large trees, and much dead wood, both fallen and standing (snags).

The goal of this study is to evaluate the importance of conifer forests of artificial origin and high natural history value, such as those in the Foreste Casentinesi, for bat communities.

The Foreste Casentinesi National Park covers a surface of ca. 36,400 ha, embracing a tract of the northern Apennines between Tuscany and Emilia Romagna, ranging from ca. 500 m to 1658 m a.s.l. Most of the National Park is covered by woodland (91.4%), primarily beech forests (over 14,600 ha) and artificial conifer plantations (7,930 ha), mainly managed as high stands, while traditional coppicing is confined to a few privately-owned restricted areas.

Thanks to this type of management, first adopted by the State and the Regions (more than 67% of the Park falls on public land), then by the National Park, the vast majority of these woods, including those of artificial origin, have an "old-growth" type structure, with numerous large trees, relatively low densities and abundant deadwood.

The most widespread conifer woodland are those of silver fir. These woodlands, the largest in the northern Apennines (Rovelli, 1995), were initially planted by Benedictine monks (Gabbrielli & Settesoldi, 1977) and then expanded, especially after the 19th century, when these forests became public property (Borchi, 1989).

Recent data gathered with Lidar methodology in a vast part of the National Park (Tuscany Region), stress some interesting differences between artificial conifer and hardwood - mainly beech -, woodlands. As shown in Table 1, conifer forests generally have a more diversified structure, particularly with a greater extension of areas of low vegetation (< 0.5 m) such as forest clearings and a higher degree of structural diversity. These features are particularly interesting in light of the interpretation of the results of our model, and will be taken up again in the discussion section.

Table 1

Comparison between the surface and the structure of deciduous (mostly beech) and conifer forests in the Foreste Casentinesi National Park; the data used are from Lidar Flights. For each ECM classes we report the lower, and upper quartile and the median value. The P value of the U Mann-Whitney test is also reported
Forest type	Height vegetation	Percentage of cover			U Mann-Whitney test
		lower quartile	median	upper quartile
Conifer	< 0.5m	1.99%	4.39%	9.98%	1.83E-09
Deciduous	< 0.5m	1.28%	2.86%	5.99%	1.83E-09
Conifer	0.5-1.5m	0.66%	1.22%	2.23%	0.4308
Deciduous	0.5-1.5m	0.52%	1.11%	2.71%	0.4308
Conifer	1.5m-7m	4.00%	6.23%	9.30%	0.000374
Deciduous	1.5m-7m	3.82%	7.12%	15.05%	0.000374
Conifer	7m-20m	27.31%	40.88%	56.63%	2.20E-16
Deciduous	7m-20m	40.56%	55.66%	66.43%	2.20E-16
Conifer	> 20m	21.92%	40.43%	59.64%	4.70E-16
Deciduous	> 20m	6.15%	24.73%	47.32%	4.70E-16
	Diversity index
conifer		0.86	1.02	1.18	8.69E-09
deciduous		0.82	0.94	1.08	8.69E-09
	Surface (ha)
conifer		1.57	3.28	6.24	4.16E-09
deciduous		2.44	4.69	8.38	4.16E-09

4.1 Field Samples

Bat data were gathered in the context of Chirotteri Monitoring project of Foreste Casentinesi National Park, by acoustic transect using a Pettersson D240X bat detector (Pettersson Elektronik AB, Uppsala, Sweden) in time-expanded mode. We identified 25 transects (min 7 km, max 36 km) in the whole Park, homogeneously distributed within the Park (Fig. 1), in order to guarantee a satisfactory level of coverage of the different environmental conditions (altitude, exposure, land use). Transects, traveled only by car to minimize double or multiple counts (Braun de Torrez et al. 2017), both along paved and dirt roads, at an average speed of 30 km/h, were made across four years (2014 and 2016–2018), always between 16th August and 15th September, without fixed time slots. Every year we repeat the same transects, covering an average of 585 km (Table 2); the differences between the years are related to problems of practicability of some stretches (landslides, fallen trees, etc...). The location of each record was registered with a GPS device.

Acoustic records were analyzed using BatSound software (Pettersson Elektronik AB); for species identification we followed Russo and Jones (2002), Pfalzer & Kusch (2003), Obrist et al. (2004) and Barataud (2015).

Table 2

Monitoring effort of the four census years. For each year we report the number of transects carried out, the total amount of kilometers covered and the days effort.
year	n. of transect	km	days effort
2014	25	608	9
2016	25	562	9
2017	25	567	10
2018	25	598	11

4.2 Data analysis

The goal of this study is to evaluate the importance of conifer forests for the bat fauna in the Foreste Casentinesi National Park. For each species examined, we built two different ecological models using a step procedure. We first built a generic habitat suitability model, then a second species-specific habitat suitability model, which we used to test the effect of conifer forests.

The models were built using MaxEnt (Phillips et al. 2006; Phillips & Dudík 2008), a frequently used method in ecological research and conservation planning since it provides reliable results even with small samples (Papeş & Gaubert 2007; Pearson et al. 2007). One of MaxEnt’s advantages over other methods of analysis is that it is able to account for uneven levels of sampling (Fourcade et al. 2014); nevertheless, the presence of strong biases in the distribution of samples may negatively affect the accuracy of the models, and thus the ability to interpret the results obtained (Kramer-Schadt et al. 2013, Syfert et al. 2013). To reduce the effects that may arise from uneven sampling – since some transects were surveyed fewer times than others, often due to roads being impassable – we used a bias file (Fourcade et al. 2014), an information layer similar to those normally used in ecological analyses (e.g. soil use types, DEM), whose geo-referenced information concerns the sampling intensity for each spatial unit under consideration. The bias file was also built with MaxEnt, by designing a model in which the sampling points - which in this case were obtained by subdividing the transects in points located at a distance of 100 meters from one another – are considered in terms of presence data and are compared with the background, which is the National Park as a whole. The habitat types that will most contribute to defining the environmental characteristics of the sampling points will be those sampled most extensively. To build the model, we used all of the variables in Table 2, which we had previously transformed in PCA, computed with RSToolbox package in R.

As noted above, the analyses were performed using a step procedure. For each selected species, we first built a basic suitability model, testing the effects of certain generic variables identified according to current knowledge (Table 3). We used climatic variables such as ombrotype (the higher the value, the more mesophylic the conditions, see Blasi et al. 2004); morphological ones such as the standard deviation of elevation as a measure of the morphological variability of the landscape; anthropic ones, specifically distances from single houses and human settlements; and explicitely environmental ones, such as woodland surface area. As specific data on forest age were not available, we used an indirect estimate (proxy), namely presence in one of the five State Reserves located within the National Park. These reserves, which have been state property since the early 20th century (Chiari 2014) or earlier in the case of the Camaldoli forest (Vazzano et al. 2011), had long been managed in a sustainable matter, albeit with productive goals, and in more recent decades in a very conservative manner. Compared to surrounding areas, State Reserves now host extensive areas of mature topsoils, often centuries old, with some of the oldest and best-preserved forests in the entire Apennines. They are thus an excellent proxy for the degree of forest development, at least at a relatively vast scale.

To deal with potential problems of correlations between variables, we transformed them using the PCA analysis, again computed with RSToolbox package in R. By using PCA we lose the ability to assess the contribution of each single variable in detail. However, the purpose of this study is not to analyze the ecology of individual species, but rather to evaluate whether conifer forests have a possible effect on their presence. In this case, PCA is thus a worthwhile method of analysis.

Table 3

List of parameters used to define the environmental preferences of the species analyzed, divided between those used for the basic models and those for the specific models.
Factor	description
Base model
Ombrotype	Rainfall at the site, based on Blasi et al.(2004)
D_House	Distance from the nearest house
D_Villages	Distance from the nearest 100 houses
Alt	Altitude
SD_Alt300	Standard deviation of altitude calculated within a circular buffer of a diameter of 300 meters centered on presence data
SD_Alt600	Standard deviation of altitude calculated within a circular buffer of a diameter of 600 meters centered on presence data
Wood_100	Forest surface area calculated within a circular buffer of a diameter of 100 meters centered on presence data
Wood_300	Forest surface area calculated within a circular buffer of a diameter of 300 meters centered on presence data
Wood_600	Forest surface area calculated within a circular buffer of a diameter of 600 meters centered on presence data
Reserves	Variable set at 1 if the record is within a State Reserce, 0 if outside it
Specific model
Con_100	Surface area of conifer forests calculated within a circular buffer of a diameter of 100 m centered on presence data
Con_300	Surface area of conifer forests calculated within a circular buffer of a diameter of 300 m centered on presence data
Con_600	Surface area of conifer forests calculated within a circular buffer of a diameter of 600 m centered on presence data

The best approximating model was selected based on the information theoretic approach (ITA) (Burnham & Anderson, 2002), using the Akaike information criterion corrected for small samples (AICc, Burnham & Anderson 1998), testing the effect of different combinations of feature types and regularization coefficients (Campedelli et al. 2012).

Once we defined the best base models, we tested the effect of the presence of conifers (Table 3). We first looked at the correlation level between the PCAs used for the base models and for the Con_ variables (parameters related with coniferous surface area; see Table 3), and between the latter. Spearman analysis showed a lack of correlation in the former case, while in the latter correlation was very high, as expected (always > 0.97). On the basis of these results, we thus separately tested the effect of each of the three “conifer forest” variables, once again using the Akaike information criterion (AICc) to select the best model.

The models were built using a subset of the available data, selected randomly and amounting to 50% of the total; the rest of the data was used as a test for validation. We believe that the decision to use a higher percentage of the data than usual for validation, sacrificing part of it to build the model, is a justifiable compromise in a situation with a limited number of localizations, so as to both have a sufficiently high number of observations to build effective models and to be able to validate them with a sufficient amount of data. Performing validations with a lower percentage of the dataset, as would normally happen with larger datasets, would have increased the likelihood of including aberrant data in the test and of excluding important parameters once the procedure was complete. Unlike what was proposed by Warren and Styres (2011), validation was not carried out on the entirety of the available sample, but only on the test, thus privileging the independence of the process.

At the end of the process, we compared the two best models for each species, choosing from the base models and from the models with the “conifer forest” variables. The best model was chosen by means of the ΔAICc and the Akaike weights (wAIC). In the former case, the more efficient models are those in which the addition of the conifer forest variables leads to a reduction in the AICc value. The Akaike weights are instead a measure derived from the AICc that can give the weight of evidence in favour of each model among a set of competing ones (Wagenmakers & Farrell 2004). Since the Akaike weight is a measure that also depends on the number of models compared, for an unbiased comparison it is necessary to have an equal number of models for each comparison step, in our case the best base model and the best specific model.

Models were evaluated using validation datasets. Model evaluation should deal with two aspects, the performance and the significance of the model (Peterson et al. 2011; Tarjuelo et al. 2014). Model performance shows how well the model classifies presence and absence of the species. Omission error rate (OER, the proportion of presence occurrence records of the evaluation dataset that fall in an area predicted as unsuitable for the species) was used as a measure of model performance, expecting low omission rates for good models. This measure of model performance was selected because it does not require true absence records. It is also necessary to assess model significance, i.e. whether the model predicts presence/occurrence records from the evaluation dataset better than expected under random predictions. Thus, one-tailed binomial tests (one per model) were performed to evaluate whether the proportion of correctly classified occurrences differs from the proportion of area predicted as presence by the model.

A procedure such as this one has the advantage of making it possible to evaluate, step-by-step and in an objective manner, the contribution of each individual variable, which in this case have profoundly different ecological meanings.

Analyses were performed on all species with a minimum of 60 occurrences, and on the number of feeding buzzes, considering all species, that are the sequences of sounds that bats emit when they approach their prey.

5.1 Sampling results

We recorded at least 18 species over four years of monitoring (Table 4), taking into account the sister species pair M. myotis/M. blythii and species in the genus Plecotus, which are not identifiable with certainty on the basis of bioacoustics only. The bat community in the Foreste Casentinesi National Park is thus particularly rich (an area of just over 36,000 hectares hosts over 50% of all Italian species; Ancillotto et al. 2019, Veith et al. 2019) and included numerous species of conservation interest. P. pipistrellus was by far the commonest species, with over 1,500 records, followed by P. kuhlii with almost 950 records and H. savii with over 600 (Table 4). These are common and widespread species in Tuscany and Italy (Lanza 2012). Other species of note included B. barbastella, with over 100 records, underscoring the natural history value of these forests. Much more than N. leisleri, which is less common but still widespread in the study area, B. barbastella is associated with old-growth forests with large trees, preferably dead or senescent, which provide shelter (Russo et al. 2004).

Table 4

Number of occurrences for each species gathered along the four-years samples; number of feeding buzzes used for the model building were also reported.For *Myotis* genus, we report only the contacts for which it was possible to identify the specie. Unidentified contacts (contact for which it was no possible to attribute neither the genus) are not included * *following the last systematic update this species should be named Myotis criptic (*Juste et al. 2019), *although some doubts remain*.
species	n. occ.
Pipistrellus pipistrellus	1574
Pipistrellus kuhlii	941
Hypsugo savii	640
Miniopterus schreibersii	184
Barbastella barbastellus	100
Nyctalus leisleri	84
Tadarida teniotis	80
Eptesicus serotinus	68
Myotis daubentonii	43
Nyctalus noctula	42
Myotis mystacinus	42
Myotis emarginatus	33
Myotis myotis/Myotis blythii	14
Rhinolophus hipposideros	12
Myotis nattereri*	11
Rhinolophus ferrumequinum	3
Pipistrellus pygmaeus	1
Plecotus sp.	4
Feeding buzz	128

5.2 Ecological models

Table 5 summarizes the results of the Principal Component Analysis, used to build both the bias file and the actual models. In the former case (All parameters), we selected five components, which explained a total of 83% of variance; in the latter case we selected four PCAs (No coniferous), which explained 81% of total variance

Table 5

Descriptive figures of the results of the PCA analysis. “All parameters” are those used for the bias file computing; “No coniferous” for the models building. The environmental factor codes refer to Table 1.
	All parameters					No coniferous
	PCA1	PCA2	PCA3	PCA4	PCA5	PCA1	PCA2	PCA3	PCA4
variance explained	0.26	0.48	0.63	0.76	0.83	0.33	0.55	0.71	0.81
environmental factors
Wood_100	0.42	0.23	0.25	0.15	0.38	0.42	0.20	0.30	0.16
Wood_300	0.47	0.23	0.23	0.11	-0.16	0.47	0.20	0.29	0.14
Wood_600	0.46	0.19	0.16	-0.53	-0.43	0.47	0.15	0.23	-0.68
SD_Alt300	0.21	0.18	-0.59	-0.75		0.22	0.20	-0.63	-0.69
SD_Alt600	0.31	-0.23	-0.45	-0.17	0.24	0.32	-0.46	-0.25	0.23
Alt	0.31	-0.23	-0.45	-0.17	0.24	0.32	-0.46	-0.25	0.23
D_House	0.14	0.23	0.19	-0.61	-0.71	0.14	0.39	-0.56	0.11
D_Villages	0.15	0.21	0.16	-0.62	0.14	0.16	0.35	-0.57	0.11
Ombrotipo	0.17	-0.40	-0.26	0.23	0.82	0.19	-0.36	-0.32	0.85
Reserves	0.24	-0.10	-0.14	-0.64	0.51	0.25	-0.16	-0.58	0.67
Con_100	-0.13	-0.48	0.16	-0.19	-0.12
Con_300	-0.51	0.32	0.15	-0.77
Con_600	-0.50	0.29	0.42	-0.41	0.55

Using a minimum threshold of 60 records, we selected eight species for our ecological analyses, in addition to the parameter related to feeding buzzes (Table 4). With the sole exceptions of E. serotinus and the feeding buzzes, for which the best base model includes none of the variables used, thus describing a homogeneous distribution, all other models appear to be efficient and informative; indeed, OER values are rather low in all cases (Table 6). Here we should note that the goal of our study was not to analyze the ecology of each species in detail, but rather to evaluate the effect of the presence of conifer forests. This approach, which led us to use PCAs rather than individual parameters, may have lowered the efficiency of the base model – or at least their understanding – by camouflaging the effects of certain environmental factors.

Table 6

Results of the models. For each species and for Fbuzz, and for each of the two kind of models (basic, specific) we report: the factors included (see Table 2), with their positive (+), negative (-) or with a threshold (t) contribution, the AICc for the basic model the ΔAICc values, calculated respect to the basic model and, only for the best model at each step, the omission error rate (OER) with the corresponding significance level (*P < 0.05; **P < 0.01; ***P < 0.001), and the Akaike weight (wAIC). “–” means that MaxEnt does not produce any model.
		basic model				complete model
specie	n	factors	AICc	OER	wAICc	factors	ΔAICc	OER	wAICc
Pipistrellus pipistrellus	742	PCA1(+), PCA2(-), PCA3(-), PCA4(+)	20584.12	21.81***	0	PCA1(+), PCA2(-), PCA3(-), PCA4(+) Con_300(+)	-82.20	17.15***	1
Hypsugo savii	250	PCA1(-), PCA2(+), PCA3(+), PCA4(-)	6910.88	30.89***	0	PCA1(-), PCA2(+), PCA3(+), PCA4(-) Con_600(+)	-116.26	26.42***	1
Miniopterus schreibersii	69	PCA1(t), PCA2(-), PCA3(+), PCA4(t)	1936.00	28.17***	0.05	PCA1(t), PCA2(-), PCA3(+), PCA4(t), Con_300(+)	-6.07	25.35***	0.95
Barbastella barbastellus	45	PCA4(+)				-
Nyctalus leisleri	37	PCA4(+)				-
Tadarida teniotis	38	PCA1(+), PCA2(t), PCA3(t), PCA4(-)				-
Eptesicus serotinus	31	-				-
Feeding buzz	29	-	812.46	-	0	Con_300(t)	- 20.65	16.13	1

A specific analysis of the effect of the “conifer forests” variables show that in five cases out of nine, their addition significantly improves the efficiency of the models, namely those for P. pipistrellus, P. kuhlii, H. savii, M. schreibersi and for feeding buzzes. In three out of five cases, the difference in terms of AIC between the base model and the complete model (which includes the conifer forests variables) was of an average of 70 units, with a maximum of 116 in the case of H. savii. These are quite significant differences, considering that a difference of just two units is usually considered a meaningful improvement in model efficiency. If we consider wAIC values, it is evident that in all cases, the model that includes the conifer forests variables is by far the most efficient. In the case of feeding buzzes, the positive relationship shows a threshold effect; in all the other cases the effect is simple positive, increasing as the area occupied by conifers.

The effect of the conifer forests variables is positive in all cases. In three cases - P. pipistrellus, M. schreibersi and feeding buzzes – the effect acts at an intermediate spatial scale, within a buffer of 300 meters around the location of the individuals. In P. kuhlii and H. savii the effect manifests itself at a larger spatial scale, within a buffer of 600 meters of the location of the individuals.

Species that showed no preference for conifer forests, or at least for which the presence of these habitats does not lead to an improvement in environmental suitability, include B. barbastella, N. leisleri and T. teniotis. In the first two cases, the base model shows a positive effect for PCA4; if we analyze the composition of this component (Table 5), we see that the variable that characterizes it the most, with a positive contribution, is the Nature Reserves variable. As stated above (cf. Materials and Methods), this variable was used in the absence of more precise measurements as a proxy for old-growth forests of high natural history interest. In keeping with what is known about these species (Russo et al. 2004, Ruczyński et al. 2010, Rachwald et al. 2021), this result shows a clear preference for these types of woodlands, and suggests that at least in this case, this preference is not influenced by forest composition. A specific study carried out in a montane area not far from our study site, with conifer forests similar to those found in the Foreste Casentinesi National Park, instead showed that these forests are actually avoided by this species (Dondini et al. 2013).

The situation is different with T. teniotis; in this case, an analysis of the base model does not provide clear indications on the predominant effect of one or more parameters. Compared to all other species recorded in the study area, T. teniotis is less tied to forest areas, which it uses exclusively for feeding (Marques et al. 2004). In addition, there is the issue of detectability, since in forest habitats this species feeds well above the canopy, making it difficult to detect, due in part to branches and leaves serving as obstacles.

For P. pipistrellus, P. kuhlii, H. savii, M. schreibersi the results of the analysis show a strong positive effect associated with the presence of conifer forests. This positive effect varies, among species, at different spatial scale, suggesting that different species use this habitat differently. The fact that for the parameter "feeding buzz", the positive effect lies within 300 meters, seem to indicate that these forests provide good hunting habitats. This effect could be related to the structure of these forests, with numerous large trees, including dead (snags) and dying ones, which provide plenty of shelter for the bats themselves, as well as ideal habitat for many invertebrates that the bats prey upon. Old-growth forests with large trees are generally rather open, with clearings and other non-forested areas where grasses and shrubs can grow and small wetlands can form; these are excellent hunting areas for forest bats and also host large populations of invertebrates. As we have seen, these characteristics, which help create suitable habitats for bats (for a review see Taylor 2006), are quite common in the conifer forests of the Foreste Casentinesi National Park (Table 1) and help explain the different results that emerged from other studies carried out in Mediterranean conifer forests, but with quite different characteristics (Russo & Jones 2003, Davy et al. 2007, di Salvo et al. 2009).

Another feature that may explain the positive effect of conifer forests is the artificial diversity associated with them, and which could in many cases favor certain species, at least those that prefer to hunt along ecotones or use them for commuting (Kalcounis-Rueppell et al. 2013). This result appears to be consistent with what was proposed by Denzinger & Schnitzler (2013); according to these authors, most of the species belonging to the Vespertilionidae family, which includes three of the four species that have shown a positive effect of the presence of conifers, belong to the "bat guild" of the "edge space aerial foragers". As we have said, most conifer forests in the Mediterranean have been planted by humans, more or less recently, for productive purposes, and the study area is no exception. As these forests are used for logging, they often feature artificial structures such as roads or water and soil management infrastructure, which in heavily forested contexts are often among the few elements diversifying the landscape, and which can be used for hunting by several species (Kaňuch et al. 2006, Rachwald et al. 2021). Additionally, if we exclude woods in remote, hard-to-reach areas, and thus less likely to be economically profitable, most of these forests – and the Foreste Casentinesi is one such example – continue to be used for logging and other productive purposes, and are thus often subject to forestry interventions. While in the Foreste Casentinesi National Park these interventions are now much less invasive, and with an eye towards improving the natural history value of the forests, they nevertheless impact forest cover and structure, and create forest clearings along with borders and habitat ecotones. The importance of such habitat for foraging (Grindall & Brigham 1999, Blakey et al. 2016) and orientation (Hein et al. 2009) in bats is well known, and has also been confirmed in forests managed for logging (Apoznański et al. 2020).

The significance of conifer forests for bats has attracted growing interest in recent years, and a number of authors, especially in eastern and central Europe, have shown that these habitats are fairly important for bats (Wegiel et al. 2019, Apoznański et al. 2020).

This renewed interest in studying bat communities in artificial woodlands managed for productive use fits within a broader process of re-assessing the importance of these woods for biodiversity conservation (Stephens & Wagner 2007), or at least in questioning conventional wisdom, which holds that the effects of these artificial habitats is always negative (Brockerhoff et al. 2008, Horák et al. 2019). Although they are generally – and often correctly - considered not particularly suitable for hosting rich communities or species of conservation interest, if not an actual liability for biodiversity conservation (for bats see Flaquer et al. 2008, Bremer & Farley 2010, Vasko et al. 2020; for other taxa see also Liu et al. 2014, Iezzi et al. 2018, Bohada-Murillo et al. 2019), artificial conifer woods can actually host rich and diverse communities if properly managed (Humphrey et al. 2013, Kirkpatrik et al. 2017, Calladine et al. 2018). They can also provide important services and goods, for example as part of nature-based solutions for fighting climate change, or simply by alleviating logging pressure on native forests (Russo et al. 2016, Carle et al. 2020).

In the specific case of bats, certain authors stressed that the main problems encountered in forests plantation, as well as in native forests, managed for logging, have to do with the lack of dead wood and shelter (Regnery et al. 2013, Bouvet et al. 2016, Law et al. 2018) or low habitat diversity (Węgiel et al. 2019, Carr et al. 2020). Nevertheless, they identify certain management strategies that could at least partly mitigate such problems (Taylor 2006).

In the current context, dominated by the effects of climate change and by the crises experienced by many forest habitats, delving into the role that properly managed artificial forests and plantations can play for conservation takes on great significance. In the European context – and in Italy in particular – the risk remains that a general negative perception of such habitats – which is pervasive among land use managers and environmental policy-makers, and indeed is often justified – might translate into forestry policies that call for the wholesale replacement of such forest with native species (Kerr 1999, Hérault et al. 2005, Nocentini 2005 & 2010). These policies are widely supported in management plans for Natura 2000 sites – in Tuscany, for example, the regulations on the management of such sites include artificially reforested areas among the problems to be solved (Bianchi et al. 2005) – and if they are applied in a generalized manner, without an objective assessment of the importance of these habitats, risk causing a significant reduction in biodiversity in many forest areas, at least in the short- and mid-term, until the deciduous species that these policies aim to favour will have reached comparable levels of development.

Acknowledgements

We really want to thanks Foreste Casentinesi National Park for funding the Chirotteri Monitoring project.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Do coniferous plantations have an importance for biodiversity conservation? Bats as indicators in the Foreste Casentinesi National Park

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