Long-term population trends of Rhinolophus hipposideros and Myotis myotis in Poland

Abstract

Bats are particularly susceptible to environmental changes because of their low reproductive rate, longevity, and high metabolic rates, which lead to relatively high food requirements. Thus, bat populations take a relatively long time to recover from increased mortality rates, and monitoring schemes should cover long time periods. In this work, we analyzed the population trajectories of two bat species, Rhinolophus hipposideros and Myotis myotis, the most numerous in five caves in southern Poland, which are known as important bat hibernacula on a continental scale. Data were collected by regular counts in 1985–2020, depending on the particular cave; in addition, previous data on the number of hibernating bats in these caves, available since 1951, were taken from existing publications. We analyzed time-series data using average locality indices and TRIM (TRends and Indices for Monitoring data) methods, and both produced similar results. Generally, the populations of the two studied bat species showed recent increasing trends, especially visible as an effect of recovery after years of decline. The situation recorded in southern Poland is very similar to that described in other places in Europe, where recoveries of bat populations have also been observed in the last decades. Although it is difficult to present results from formal analyses, because of the lack of good data, at least some factors—less exposure to contaminants (pesticides, heavy metals), improving food availability due to climate change, and a lower predation rate (including human pressure), both in the breeding season and during wintering—positively affected both species.

Keywords:

• Bats
• endangered species
• long-term monitoring
• caves
• hibernacula

1. Introduction

Bats are particularly susceptible to environmental changes because of their low reproductive rate, longevity, and high metabolic rates, which lead to relatively high food requirements (Voigt & Kingston 2016). Thus, bat populations take a relatively long time to recover from increased mortality rates (Racey & Entwistle 2000; Fleischer et al. 2017). Moreover, bat populations show responses to environmental stressors, ranging from alterations in habitat quality to climate change, as well as direct exploitation (Russo & Ancillotto 2015; Jung & Threlfall 2016; Gottfried et al. 2020), and bats are thus recognized as excellent indicators of anthropogenic changes in the environment (Jones et al. 2009; Russo & Jones 2015; Zukal et al. 2015).

Important for methodological, statistical and conservation purposes are the existing long-term series, coming mainly from winter bat censuses, especially in caves (Furey & Racey 2016; Zukal et al. 2017). For example, bats have been counted since 1944 in the Schenkgroeve, an artificial limestone cave in south Limburg in the Netherlands (Grol & Voute 2010), since 1946 in Hermann’s cave in Lower Austria (Spitzenberger & Engelberger 2013), and since 1957 in some caves in the Moravian Karst, Czech Republic (Gaisler et al. 2006). Thanks to the results obtained, it was discovered that the European bat populations, particularly the lesser horseshoe bat Rhinolophus hipposideros and the greater mouse-eared bat Myotis myotis, declined dramatically in the second half of the 20th century (Stebbings 1989; Řehák & Gaisler 1999; Bontadina et al. 2000; Uhrin et al. 2010). After the period of decline, since the 1990s bat populations have begun to recover (Van der Meij et al. 2015). An increase in the number of some species of hibernating bats has been reported from many European countries: Austria (Spitzenberger & Engelberger 2013), Belgium (Kervyn et al. 2009), the Czech Republic (Bufka & Červený 2012; Chytil & Gaisler 2012), the UK (Barlow et al. 2015), Italy (Toffoli & Calvini 2019), Ireland (McAney 2014), the Netherlands (Grol & Voute 2010), Poland (Fuszara et al. 2010), Slovakia (Uhrin et al. 2010), Spain (Machado et al. 2017), Sweden (Rydell et al. 2019) and Switzerland (Bontadina et al. 2000).

The reasons for these changes in population trends have not been conclusively identified (Bontadina et al. 2000; Tournant et al. 2013; Afonso et al. 2016; Froidevaux et al. 2017). It is believed that the bat population declines and subsequent increases may be caused by a combination of various factors, such as the spread of chemical pollutants, habitat destruction, changes in landscape structure, disturbance and destruction of roost sites, climate change, declines in insect prey, competition for prey, genetic inbreeding, and diseases (Arlettaz et al. 2000; Bontadina et al. 2000; Zahn et al. 2007; Van der Meij et al. 2015; Auteri & Knowles 2020).

Both species selected for this study, Rhinolophus hipposideros and Myotis myotis have similar preferences for shelters. In winter both species hibernate in caves, mines and other cave-like structures. They prefer places with high humidity (over 80%) and stable temperatures of 6–9°C. In summer the females form maternity colonies in caves (Southern Europe) or in buildings with spacious roofs such as church attics and castles (Central Europe), where they give birth and nurse their offspring (Dietz et al. 2009; Berková et al. 2014; Jan et al. 2017). Both species are insectivorous, but they differ slightly in their manner of foraging and their diet. R. hipposideros forages exclusively in woodlands, preferentially in dense areas, capturing its prey using echolocation in flight. It preys mainly on moths and Diptera. M. myotis preys on large, ground-dwelling arthropods such as beetles, crickets, and spiders, gleaning them from the ground (Motte & Libois 2002; Rudolph et al. 2009). The two species are the most numerous hibernating species in the caves of southern Poland (Grzywiński et al. 2015). These bat species are excellent for monitoring population trends, as they are easy to recognize and are relatively easy to count, because they do not hide in crevices (Dietz et al. 2009; Rudolph et al. 2009).

Both bat species occupy different ecological niches, especially in terms of foraging strategies, and then we test a hypothesis on differences between study species in relation to long-term environmental changes. Saying more precisely, the aim of the study was to determine long-term population trends of R. hipposideros and M. myotis and the probable causes of changes in the numbers of hibernating bats of these two species.

2. Material and methods

2.1. Study area

The five studied caves (Table I) are located in the Kraków-Częstochowa Upland (also known as the Polish Jura) in the southern part of Poland (Figure 1). The upland has elevations between 300 and 513 m a.s.l. The area is formed by upper Jurassic limestone, which creates a plate with single inselbergs several meters in height. This region is characterized by karst processes with numerous deep gorges, sinkholes, and caves (Kondracki 2001). Over 1800 caves and rock shelters of total length over 31 km are known in this area. Most of them are small: only 150 caves exceed a length of 40 m (Gradziński & Szelerewicz 2004). 20% of the region is covered by forests, dominated by deciduous and mixed types.

Figure 1. Location of the studied caves: 1 – Racławicka, 2 – Nietoperzowa, 3 – Łokietka, 4 – Ciemna, and 5 – Wierzchowska Górna (Source of spatial data: OpenStreetMap.org)

Table I. Characteristics of the studied caves

No	Cave	Length^1 (m)	Relative height (m)	Altitude (m a.s.l.)	Protection
1	Racławicka	165	−26	446	-
2	Nietoperzowa	337	−23	447	G
3	Łokietka	320	−7	453	NP, G
4	Ciemna	209	+10	372	NP, G
5	Wierzchowska Górna	975	−25	390	G

NP – cave located in the national park, G – gate at the entrance.

¹dimensions of caves after: Szelerewicz and Górny (1986), Gradziński and Szelerewicz (2004), and Gradziński et al. (2007).

2.2. Data collection

Standardized and regular annual censuses during the hibernation period (in the first half of February) have been conducted in the caves since 1985 (Nietoperzowa and Ciemna), 1991 (Łokietka), 2000 (Racławicka) and 2001 (Wierzchowska Górna). All bats roosting in the caves are counted. The counting protocol includes visual species determination and counts of visible hibernating bats with the aid of torches and binoculars. Previous data on the number of hibernating bats in these caves, available from 1951, were taken from existing publications (Kowalski 1953; Wołoszyn 1976; Harmata 1981; Godawa & Wołoszyn 1990; Nowak & Kozakiewicz 2000; Grzywiński et al. 2015).

Climate data for the years 1951–2019—average annual temperature (°C), total precipitation (mm), number of days with rainfall—were obtained from the nearest meteorological station in Kraków (20 km to the south).

2.3. Data analysis

2.3.1. Average locality index (ALI)

Following Loman and Andersson (2007) and Kyek et al. (2017) we calculated the average locality index (ALI), which allowed us to compare the average population changes of the two bat species over the years, even though the numbers of both species are very different. In the first step, we calculated a locality index for each cave (LI_ys) using the formula

$L{I}_{ys}=N_{ys}/\mathrm{\varnothing }{N}_s$

where:

$N_{ys}$ - the number of individuals counted for a particular species, cave, and year

$\mathrm{\varnothing }{N}_s$ - the average number counted for a particular species at a given cave during the whole study period

Values below 1 indicated a relatively low count of individuals, while numbers above 1 indicated a relatively high count. Then we calculated the average locality index (ALI) in all caves for each year, using the formula

$ALI=\mathrm{\varnothing }L{I}_{ys}$

which represents the overall population trend for both species. Finally, we adjusted the linear and nonlinear trend of population change over time and tested the significance of Beta coefficients using a t-test.

In the case of monotonic functions, such as a quadratic function,

$f\left(x\right)=a{x}^2+bx+C$

for each species we calculated an extreme point of the function according to the equation

$E=-\frac{b}{2a}$

which shows up to which year the population decreased in number, and analogously, from which year the number of individuals increased.

2.3.2. TRIM

The ALI method does not provide test statistics significant for population change, nor does it provide standard errors and 95% confidence limits. Thus, as a second approach to the analysis of the population trends we used TRIM (TRends & Indices for Monitoring data method) (Pannekoek & van Strien 2005) implemented in the rtrim library for R. This procedure makes better use of the available data, especially when some data for the years are absent—a common issue in long-term time series (in our case in the years 1950–1980)—calculating standard errors and confidence limits and offering various test statistics; it also takes into account overdispersion and serial correlation of data (Pannekoek & van Strien 2005). TRIM is also capable of categorizing data by covariates and testing their influence on the observed changes, using Wald tests.

TRIM fits log-linear models and indices that represent the effect of change between years, which indicates the relative variation of the total population size. Two types of indices are estimated: (i) model-based indices, which are the values predicted by the model; and (ii) imputed indices, which equal the observed count if an observation is made, and the model prediction for missing counts (Pannekoek & van Strien 2005). Dissimilarity between the two indices reflects a mismatch between observed (i.e. imputed indices) and model predictions (i.e. model-based indices) and, therefore, a lack of fit of the statistical model applied. In the next step, indices are used to estimate a mean annual change rate (Pannekoek & van Strien 2005). This technique has been widely employed for the analysis of temporal series in bird populations (Paradis et al. 2002; Gregory et al. 2007; Lehikoinen et al. 2019) and also bat populations (Uhrin et al. 2010; Van der Meij et al. 2015; Froidevaux et al. 2017; Machado et al. 2017; Toffoli & Calvini 2019). We developed models with and without covariates (five caves). The best-fit models were selected according to goodness-of-fit tests (the Likelihood Ratio (LR) and chi-squared tests) and the Akaike information criterion (AIC). A significance value for a model greater than 0.05 indicates that the data fit a Poisson distribution and, therefore, that the model can be accepted. Indices, overall slope and Wald tests remain reliable in case of lack of fit (Pannekoek & van Strien 2005). In case of overdispersion or serial correlation (default TRIM thresholds: >3.0 and >0.4, respectively) the Wald test for the significance of slope was employed (Pannekoek & van Strien 2005).

All calculations were performed in the language R 4.0.2 using the stats, rtrim, psych and ggcorrplot libraries (R Core Team 2018).

3. Results

3.1. Population size

The detected number of individual bats between 1951 and 2020 was very variable. The total number of Rhinolophus hipposideros in the five studied caves was over 300 individuals in the early 1950s. In the 1980s and early 1990s, it did not exceed 40 individuals, and in the last decade (2011–2020), it averaged 665 individuals (max 1050 ind. in 2020). The highest number of R. hipposideros was observed in Ciemna cave (max 531 ind. in 2020, min 5 ind. in 1986). Myotis myotis was a less abundant bat species. A total of over 50 individuals were observed in the five studied caves in the 1950s. In the 1980s and early 1990s, fewer than 40 individuals were usually recorded. In the last decade of observations, there were on average 88 individuals (max 112 ind. in 2016). M. myotis was most abundant in Nietoperzowa cave (max 55 ind. in 2019, min 6 ind. in 1982).

3.2. Analysis based on average locality indices

For both species, the β-coefficients for linear and quadratic functions were significant (Table II). However, for both species, a lack-of-fit test showed that a quadratic function was better than a linear one (Table II). Calculation of the extreme point of the function for R. hipposideros showed that the population decreased up to the year 1979, after which it increased (Figure 2). In the case of M. myotis, the extreme point occurred in 1980 (Figure 2). We did not find any significant differences between the two values (chi-square = 0.98, p = 0.86).

Figure 2. Average locality index (ALI) with quadratic regression for Rhinolophus hipposideros (a) and Myotis myotis (b)

Table II. Comparing the directional factor (β) for a population trend between a linear and a quadratic function

Bat species	Function	β± SE	p for β	R^2	Lak-of-fit
Rhinolophus hipposideros	Linear	0.041 ± 0.0075	<0.01	0.38	F = 299.5, p < 0.0001
	Quadratic	0.0022 ± 0.000014	<0.0001	0.89
Myotis myotis	Linear	0.15 ± 0.03	<0.01	0.32	F = 243.8, p < 0.0001
	Quadratic	0.0094 ± 0.00055	<0.0001	0.90

SE – standard error.

3.3. TRIM

All of the tests showed the same slope for both species. R. hipposideros and M. myotis are stable and show a moderate increase. Models using the five caves as covariates have higher AIC, smaller Wald statistics and higher standard deviation than models without them (Table III). The goodness-of-fit tests for both species are significant. The overall slope of the linear trend model for R. hipposideros and M. myotis represents a moderate increase. The indices show a sharp drop in the years 1950–1973; thereafter, the indices are stable again until 2001. The early increase in 2002 was mainly driven by the population dynamics in the Ciemna and Wierzchowska caves. The imputed overall index for 2010 is equal to the index from 1951.

Table III. Test statistics for the different TRIM models for Rhinolophus hipposideros and Myotis myotis.

Bat species	Covariates	AIC	Wald-test covariates	Mean SE	Goodness-of-fit
Rhinolophus hipposideros	Only time effect	1724.6	-	0.093	P < 0.001
	5 caves	1989.2	3.4, p = 0.48	0.459	n.s
Myotis myotis	Only time effect	297.7	-	0.113	P < 0.001
	5 caves	435.3	2.1, p = 0.57	0.243	n.s

AIC – Akaike information criterion, SE – standard error.

The overall slope of the linear trend model for M. myotis shows an increase (p < 0.01). The indices show a negative trend between 1951 and 1953, followed by a moderate increase to 1981. The first peak of the increase in 1991 is mainly driven by the population dynamics in the Nietoperzowa cave.

Despite the differences between the species, their numbers (expressed as a TRIM index of year-to-year changes) were moderately correlated (Figure 3). We also found a positive relationship between the average annual temperature and the numerical change in the TRIM index for both species, while no such significant relationship was found for precipitation or the number of days with rainfall in a particular year (Figure 4).

Figure 3. Changes in relative abundance according to TRIM models for Rhinolophus hipposideros and Myotis myotis. A blue or red circle indicates the means, bars indicate standard error, while the dashed lines - 95% confidential limits. Bottom part: DDT production (GEF 2004) and important heavy metal (ZN, Pb, Cu and Ni) emission in Poland – data available since 1980 (GUS 2020)

Figure 4. Correlation matrix between the TRIM index of year-to-year changes in the number of bats for Rhinolophus hipposideros (trim RHH) and Myotis myotis (trim MYM) and weather conditions: average annual temperature (Temp), total precipitation (Prec) and number of days with rainfall (DwP) in study years

4. Discussion

4.1. Population trends

Both analytical methods produce similar results: a recent increase in population size, and an especially visible effect of recovery after years of decline. The situation is very similar to that described in other places in Europe, where recoveries of many bat populations in the last decades have been reported (Van der Meij et al. 2015). Rhinolophus hipposideros is the bat species that has faced the most dramatic decline in western and central Europe in the 1950s–1980s, becoming locally extinct (Stebbings 1989; Kokurewicz 1990; Bontadina et al. 2000; Afonso et al. 2016), a subsequent reversing trend and population rebound have been observed since the 1990s (Uhrin et al. 2010; Bufka & Červený 2012; Chytil & Gaisler 2012; Spitzenberger & Engelberger 2013). At the same time, similar but not as spectacular trends were observed in the European population of Myotis myotis (Zahn et al. 2006; Kervyn et al. 2009; Uhrin et al. 2010; Van der Meij et al. 2015).

The caves we studied are among those where bat observations have been conducted for the longest time in the world. Our findings confirm the importance of long-term bat monitoring, especially winter censuses that have been ongoing for many decades at some sites (Spitzenberger & Engelberger 2013; Furey & Racey 2016; Zukal et al. 2017). The results of short-term observations can be disturbed by annual fluctuations in numbers. In addition, bat populations take many years to recover after disturbances (Racey & Entwistle 2000; Fleischer et al. 2017), so only long-term observations can identify such changes (Jones et al. 2009; Russo & Jones 2015).

For effective bat conservation, it is important not only to perform long-term bat monitoring, but also to identify drivers of their population decline (Bontadina et al. 2000; Zahn et al. 2007; Afonso et al. 2016). Because the bat species we studied have a similar pattern of population trends at many sites in Europe, it is likely that the same (or very similar) factors influenced them. Therefore, results obtained locally may be relevant at the continental scale. Below we discuss a set of potential factors that may have affected local bat populations, also paying attention to potential limitations in the explanation of population trajectories.

4.2. Factors affecting long-term population trends

4.2.1. Exposure to contaminants (Pesticides, heavy metals)

Exposure to organochlorine insecticides, especially DDT (dichlorodiphenyltrichloroethane), has been identified as a possible cause of declining bat populations (Luckens & Davis 1964; Jefferies 1972; Clark et al. 1978). DDT was used ubiquitously for pest control in agriculture and forestry in Poland in the years 1946–1976, but since then the amount of DDT in the environment has been systematically decreasing (Falandysz et al. 2001; Bayat et al. 2014). However, due to their relatively long lifetime and their high daily food intake, insectivorous bats may be exposed to higher concentrations of cumulative chemicals such as heavy metals, which accumulate through the food chain (Zukal et al. 2015; Hernout et al. 2016).

The caves studied are located between the Kraków agglomeration and the Upper Silesia industrial region, where there are hundreds of industrial facilities (metallurgical works, chemical and cement plants, power stations). In the last decades of the 20th century, this region was the most polluted in Poland and one of the most polluted in Europe (Dmuchowski & Bytnerowicz 1995). As a result of the political and economic transformations in Poland at the end of the 1980s, industrial production, including that of heavy metals, declined considerably.

4.2.2. Changes in the structure of habitats

The area where the caves are located has not changed significantly since World War II. For both studied species, the availability of woodlands (foraging areas) is crucial (Motte & Libois 2002; Rudolph et al. 2009). However, the absence of significant changes in land use, particularly reduction in forest cover, indicates that changes in the physical (vertical) structure of habitats could not have been the main reason for the long-term changes in bat populations.

4.2.3. Decline of roosts

Both studied bat species (R. hipposideros and M. myotis) have similar preferences for winter and summer roosts (Dietz et al. 2009). In winter, bats hibernate mostly in caves and other underground places. In the Polish Jura, the number of caves available for bats has not changed noticeably in the 20th and 21st centuries (Gradziński & Szelerewicz 2004). In the caves we monitored, the conditions of hibernation have not changed since the early 1950s (Kowalski 1953). The summer roosts in this area are not well recognized and have not been monitored. However, there is no information about a significant number of building renovations that might have caused the loss of summer bat colonies.

4.2.4. Climate changes

We found a significant positive correlation between the population trend of both species (R. hipposideros and M. myotis) and the average annual temperature in 1951–2020, but we did not find such a correlation with precipitation or with the number of days with rainfall in particular years (Figure 4). Numerous earlier studies have demonstrated the impact of climatic conditions on the activity, survival, and reproductive success of bats (Sherwin et al. 2013). Climate influences food availability (Arlettaz et al. 2001; Ciechanowski et al. 2007), timing of hibernation (Hope & Jones 2012; Jones & Rebelo 2013), frequency and duration of torpor (Stawski et al. 2014), rate of energy expenditure (Zahn et al. 2007; Jan et al. 2017), reproduction and the development rates of juveniles (Adams & Hayes 2008; Burles et al. 2009; Lučan et al. 2013). Global warming may influence the species richness and distribution of bats (Rebelo et al. 2010; Wu 2016). However, there are few studies showing the impact of temperature on bat population trends. Froidevaux et al. (2017) found that the annual growth rate of maternity colonies of the greater horseshoe bat (Rhinolophus ferrumequinum) in the United Kingdom was strongly correlated with spring temperatures and precipitation. Jones and Rebelo (2013) believed it highly likely that warmer conditions have contributed to considerable increases in abundance since 1997 for two species of horseshoe bats (R. ferrumequinum and R. hipposideros). Zahn et al. (2007) compared the impact of severe weather in Portugal and Germany on the body condition of M. myotis. They concluded that foraging constraints due to severe weather may contribute to poor body conditions, even when food resources are abundant. Thus, bouts of bad weather may cause high mortality in bats. On the other hand, Bowler et al. (2015) emphasized that none of the temperature variables showed a significant relationship with long-term bat population trends. Mehr et al. (2011) found that land use had a much greater effect than climate on bat species richness and community composition on a regional scale.

Our finding that long-term trends in bat populations were correlated with average annual temperature does not necessarily mean that temperature was the only factor affecting bat population changes. The correlation may be accidental, resulting from comparing two growing trends at the same time. Both studied bat species are thermophilic, which may be a reason for the effect of increasing temperature (Dietz et al. 2009), and can benefit from warming. However, temperature does not explain the decreasing trends between 1950 and 1980. Bontadina et al. (2000) also suggested that the fact that R. hipposideros was an abundant species early this century, when the climate was not significantly warmer than today, contradicts the scenario of a large thermic dependence as a single influencing factor.

4.2.5. Food availability

We have no information on food availability for bats on a local scale. However, Przybyłowicz and Buszko (2008) observed that in the last few dozen years, the species richness of Lepidoptera in the Ojców National Park has decreased. On the other hand, monitoring of forest tree pests (insects) suggested that the most important insect pests have a tendency to outbreak in forests (Perlińska & Hamera-Dzierżanowska 2016). In the vicinity of the study area, the only tree pest whose numbers increased was the pine sawfly (Acantholyda posticalis), and in the years 1971–2018 the fluctuations in its numbers were very small (Sławski & Sławska 2019). There is no information supporting the hypothesis that a shortage of insects could be the main cause of bat population changes. In Switzerland, Bontadina et al. (2008) found that changes in prey abundance are unlikely to explain the demography of R. hipposideros. However, the same factors that affected bat numbers may also have affected the number of insects.

4.2.6. Diseases

Bats have been considered to have a particularly effective immune system, but numerous bacteria and viruses apparently remain non-pathogenic in bats, likely due to a long process of co-evolution (Afelt et al. 2018). Although bacterial, viral and parasitic infections may be among the main causes of bat deaths, no mass mortality from epidemics has been observed in Europe (Mühldorfer et al. 2011). There is very little information about bat diseases in Poland. No mass mortality of bats or visible disease symptoms were observed in the caves of the Polish Jura during the winter censuses. We believe that diseases were not the main cause of long-term changes in bat numbers, but bats affected by other factors such as pollution may have been more susceptible to infection.

4.2.7. Predation, including human disturbance

Temperate zone bats face a very low risk of predation. In particular, there are no predators specialized on bats in Europe (Lima & O’Keefe 2013). Owls are the only nocturnal predators that can prey on bats in flight, but this is a rare and opportunistic phenomenon, and only two species of European owls, the barn owl Tyto alba and the tawny owl Strix aluco, feed on bats more frequently (Sieradzki & Mikkola 2020). Occasionally, bats in roosts may be killed by domestic cats (Ancillotto et al. 2013) and martens (De Marinis & Masseti 1995). Predation is therefore a marginal factor with little impact on bat mortality.

On the other hand, the Ojców National Park is exposed to relatively high tourist pressure, due to its small area (2146 ha), attractiveness, and location close to the city of Kraków. This pressure can be estimated on the basis of the number of visitors to the Łokietka Cave, one of the Park’s greatest attractions. Sales of tickets to this cave have been recorded since 1960. There is no visible trend in the number of visitors. The current number of visitors (120 thousand in 2019) is close to the average for the whole period and to that of the early 1960s. The cave is only visited outside the bat hibernation season. Despite the high human pressure, no deliberate killing or disturbance of bats has been recorded in the caves of the Polish Jura.

4.3. Assessment of factors

Both studied species showed positive trends in population size over the long time period (1951–2020). Because the study has a correlational character, and because there was no access to detailed spatial and temporal environmental (and other) data, we discuss the main potential factors affecting both bat species according to the proposal of Bontadina et al. (2000), and we rank their influence (Table IV).

Table IV. Assessment of factors that may have caused changes in long-term population trends of Rhinolophus hipposideros and Myotis myotis. List of factors after Bontadina et al. (2000)

No.	Factor	Method of assessment	Relevance of the assessment	Assessment of the importance of the factor
1	Exposure to contaminants (pesticides, heavy metals)	Data-based assessment	High	+++
2	Changes in the physical structure of habitats	Data-based assessment	High	-
3	Loss of roosts and roost deterioration	Expert evaluation	Medium	-
4	Climate changes	Statistical analysis	High	++
5	Food shortage	Data-based assessment	Medium	+
6	Competition against other species	Expert evaluation	Medium	-
7	Genetic inbreeding	Expert evaluation	Low	-
8	Diseases	Expert evaluation	Low	+
9	Predation, including human disturbance	Expert evaluation	Medium	-

Factor assessment: “+++” – most important factor, “++” – might be important, “+” – might play some role, “-“ – not relevant.

5. Conclusions

Both studied species, Rhinolophus hipposideros and Myotis myotis, have shown a significant increase in wintering population size over the last 70 years. We noted two directions of change: until the 1980s the population of both species was decreasing, and after that time it was increasing. Similar trends have been observed throughout Europe (Van der Meij et al. 2015).

Although the search for factors affecting population size has only a correlative character, we must note that reduced exposure to contamination was probably the most important factor in the long-term changes in the populations of both of these bat species. However, other factors, including climate change, food shortage and diseases, may also play some role in changes in bat populations.

Acknowledgements

We would like to thank the participants of the winter bat censuses, in particular: Przemysław Adamus, Jolanta Cerek, Małgorzata Hoppe, Barbara Karwowska, Anna Kmiecik, Paweł Kmiecik, Katarzyna Kozakiewicz, Katarzyna Malak, Wojciech Olma, Krzysztof Polowy, Rafał Sadowy, Marta Wieczorek and Mikołaj Zbonik. We would also like to thank numerous participants of winter censuses from: Student Foresters’ Club of the Poznań University of Life Sciences, Student Naturalist Society at the Jagiellonian University and Cracow Caving Club.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Additional information

Funding

The publication is co-financed within the framework of the Ministry of Science and Higher Education program “Regional Initiative Excellence” in the years 2019–2022 [project number 005/RID/2018/19].