Importance of wildlife conservation in points –
“Habitat loss and degradation”, “climate change” as well as “excessive nutrient load and other forms of pollution” represent three of the major threats to biodiversity worldwide, with urbanization and agriculture being important drivers for all three (Secretariat of the Convention on Biological Diversity, 2010). Due to city growth, more and more natural surfaces are sealed, increasing numbers of roads and railways are fragmenting the landscape, and cities become heat islands characterized by higher temperatures compared to their surrounding (Liu et al., 2016; Ward et al., 2016). The world’s human urban population has grown rapidly and is associated with an expansion of urban areas but also with an increase of global food demand, and accordingly, with an agricultural expansion.
Agriculture is a major cause for deforestation, increase in greenhouse gas emissions, and the consumption of a substantial amount of freshwater (Ramankutty et al., 2018). The invention of the Haber Bosch procedure in the early 20th century permitted the synthesis of nitrogen fertilizer from atmospheric nitrogen. The resulting increased use of fertilizer, in combination with pesticides, led to a boost in agricultural productivity but with the result in decrease of freshwater quality with detrimental effects to freshwater ecosystems and their terrestrial surroundings (Ramankutty et al., 2018).
However, some species, such as wild boars, red foxes, house sparrows, American toads, or whistling tree frogs, persist in habitats altered by urbanization or intense agriculture (Bateman & Fleming, 2012; Hamer & McDonnell, 2010; Isaksson, 2018; Koumaris & Fahrig, 2016; Stillfried et al., 2017). They are traditionally referred to as urban‐adapters (McKinney, 2002). Species in both types of these modified ecosystems have usually been considered generalists with a broad environmental tolerance. Although scientific attention has rather focused on identifying reasons for the exclusion of certain species from human‐modified landscapes (Ducatez et al., 2018; Hassall, 2014; Howard et al., 2020), the characteristics behind a species’ ability to persist, as well as the limits in environmental changes that still allow persistence, have rarely been investigated (Hamer & McDonnell, 2008; Jung & Threlfall, 2016; Marques et al., 2019).
However, it is essential to understand the organisms’ responses to anthropogenic impacts and environmental features facilitating their persistence in order to develop sustainable management strategies, which might promote and maintain biodiversity and associated ecosystem services in human‐modified landscapes (Alberti, et al., 2017; Donihue & Lambert, 2015; McDonnell & Hahs, 2015). General responses of organisms to rapid environmental changes, natural as well as those deriving from human actions, are either moving into more suitable areas, or stay and adjust physiology, behavior, life‐history, and/or morphology (Alberti, 2015; Alberti et al., 2017; Lowry et al., 2013; Sparkman et al., 2018). These adjustments are facilitated via phenotypic plasticity and contemporary evolution, that is, evolution occurring over less than a few hundred years (Palkovacs et al., 2012), with humans acting as major driving forces (Hendry et al., 2008; Hendry et al., 2017). Phenotypic trait changes, in turn, can alter ecosystem function and, in case of heritability, create the potential for eco‐evolutionary feedback, with consequences for human well‐being (Alberti, 2015; Alberti, et al., 2017; Rivkin et al., 2019; Rudman et al., 2017).
The study of potential adaptive trait changes in persisting species requires long‐term data, which are not easily accessible for physiological, behavioral, or life‐history traits. However, changes in morphological and biochemical traits of preserved specimens may be used as a proxy for species’ responses to rapid environmental changes (Holmes et al., 2016; Kern & Langerhans, 2018; Meineke et al., 2018; Pergams & Lawler, 2009; Schmitt et al., 2018; Stumpp et al., 2016). Several morphological characters have been shown to be affected by urbanization and agricultural land‐use, for example, body sizes of birds, amphibians, and arthropods as an indicator of habitat quality and life history (Jennette et al., 2019; Meillère et al., 2015; Merckx et al., 2018). Fluctuating asymmetry (FA), defined as small, random deviations from perfect bilateral symmetry, has been used as a measure of developmental instability caused by environmental stress in reptiles and fish (Lazić et al., 2013; Lutterschmidt et al., 2016). Additionally, stable isotopes can serve as an indicator of nitrogen enrichment in amphibians, fish, invertebrates, and plants (Donázar‐Aramendía et al., 2019; Jefferson & Russell, 2008). Notably, so far only few studies investigating human‐induced morphological changes in mammals and fish included the temporal component (Kern & Langerhans, 2018; Pease et al., 2018; Pergams & Lawler, 2009; Tomassini et al., 2014).
We herein aim at identifying specific trait changes reflecting the changing living conditions of a species that have persisted in rapidly changing environments. To this end, we tested the hypotheses that in the widespread European Common Frog, Rana temporaria Linnaeus, 1758, morphological traits, that is, body size and FA, and nitrogen stable isotopes (δ15N) as a reflection of the environment the frogs lived in, (a) have changed over a period spanning the last 150 years (1868–2018); and (b) that changes in these traits can be attributed to urbanization and agricultural intensity. More precisely, we expected decreasing body sizes, increasing levels of FA and increasing δ15N values in response to decreasing habitat quality, increasing environmental stress and enrichment of nitrogen through artificial fertilizers and air pollution, respectively. If we could detect such changes, we assumed that they were associated with the change of specific land use features.
We selected R. temporaria for three reasons. First, amphibians have highly specific ecosystem demands, low dispersal abilities either due to physical constraints, a high breeding site fidelity, or to anthropogenic barriers, and are therefore particularly affected by landscape modifications (Arntzen et al., 2017; Hamer & McDonnell, 2008; Stuart et al., 2008). They rely on terrestrial and aquatic environments due to their biphasic life cycle, making them very vulnerable to changes in both ecosystem types (Becker et al., 2010). Consequently, they have experienced a global decline since the second half of the 20th century (Beebee & Griffiths, 2005). Second, the European Common Frog has persisted in a large range of habitats (Sillero et al., 2014), including cities and agricultural landscapes, during the entire Anthropocene, despite fundamental environmental changes (Carrier & Beebee, 2003; Schlüpmann et al., 2004), making the species suitable for tracking the effects of environmental changes (Vander Wal et al., 2013). Third, historical series of museum‐preserved specimens were available, making long‐term analyses possible. We chose specimens originating from the Berlin–Brandenburg region, Germany, which comprises both urban areas and areas with intense agricultural activities, and thus potentially huge environmental change during the last 150 years.
2 MATERIALS AND METHODS
2.1 Study area
The study area comprises Germany’s capital, the city of Berlin (52°31ʹN, 13°24ʹE), covering an area of 891 km2 with a current population of 3.65 million inhabitants (Amt für Statistik Berlin‐Brandenburg, 2019), and the surrounding rural Federal State of Brandenburg. Since the early phase of industrialization, Berlin has grown continuously. It reached its highest population in 1943. At the end of the Second World War in 1945, 50% of Berlin’s residential and one‐third of its industrial area were destroyed and the population decreased to 2.8 million. After 1945, Berlin was rebuilt consistently (Senate Administration for Urban Development, 2002). Today the city contains 53% built‐up area (i.e., residential or industrial areas and roads or railway tracks), 36% green infrastructure, such as forests, grassland or urban greenspaces (i.e., parks, gardens, cemeteries), and 6% water bodies. These calculations are based on the habitat type mapping of Berlin (Senate Department for Urban Development and Housing, 2014). Brandenburg, on the other hand, is mainly characterized by agricultural land (45%) and forest (37%; Ministry of Rural Development, Environment, & Agriculture of the Federal State of Brandenburg, 2016; Statistisches Bundesamt (Destatis), 2017). The Berlin sites used in this study are predominantly characterized by a certain amount of built‐area and developed urban green spaces. Very few Berlin sites are located within forested areas or close to agricultural fields at the edge of the city. The Brandenburg sites are predominantly, but not exclusively, characterized by agriculture. Some are also located close to forested patches, within grasslands (not to be confused with developed urban greenspace) or urbanized areas (e.g., the city of Potsdam).
2.2 Specimen selection
We used all ethanol‐preserved adult voucher specimens of R. temporaria, unambiguously originating from the Berlin–Brandenburg area in the collection of the Museum für Naturkunde Berlin (ZMB, https://doi.org/10.7479/5tm4‐9r29). Snout–vent length (SVL) of at least 5 cm was used as a criterion to define adults (Dittrich et al., 2018; Miaud et al., 1999). This provided us with an initial sample size of n = 124 specimens, divided into n = 56 specimens collected at 15 different locations in Brandenburg, and n = 68 specimens collected at 17 different locations in Berlin, covering a time span from 1868 to 2017.
2.3 Morphological traits and stable isotopes
Body size was measured as SVL (Watters et al., 2016) with a digital caliper, always by the same observer. In addition to the measurements of preserved specimens, we also took SVL of 34 live adults from Berlin in 2018. Total sample size for SVL analysis over time was n = 158.
For the assessment of FA whole preserved frogs were removed from ethanol, wrapped in bubble wrap, transferred to a dry plastic tube and scanned with non‐destructive micro‐3D‐computed tomography (μCT; Niemeier et al., 2019). Images were generated using a Phoenix|X‐ray nanotom of the company GE Sensing & Inspection Technologies GmbH at 90 kV and 150 μA with fast scan settings for upper and lower body scans, acquiring 1,000 projections per scan. Effective voxel size ranged between 19 and 21 μm for each scan. Volumetric reconstructions were made in Datos|x‐reconstruction software (GE Sensing & Inspection Technologies GmbH). We measured each specimen at the right and left side of: humeri from the highest point of the head to the end of the capitulum, radio‐ulnae from the olecranon process to the styloid process of the ulna, femora from the medial condyle to the femur head, and tibio‐fibulae from the intercondylar eminence to the tip of the medial malleolus of the tibia (Figure 1a–e). We used VG Studio Max 3.0 with the distance measurement tool for the measurements. Broken bones were excluded from analyses.
For determining nitrogen stable isotope values, thigh muscle tissue was extracted from preserved frogs (n = 104). Samples were dried at 60°C in a drying chamber for 72 hr. Stable isotope analysis of 1 mg dried tissue per frog was performed with a THERMO/Finnigan MAT V isotope ratio mass spectrometer (Thermo Finnigan), coupled to a THERMO Flash EA 1112 elemental analyzer via a THERMO/Finnigan Conflo IV‐interface in the stable isotope laboratory of the Museum für Naturkunde, Berlin. Stable isotope ratios are expressed in the conventional delta notation (δ15N) relative to atmospheric nitrogen (Mariotti, 1983). Standard deviation for repeated measurements of laboratory standard material (peptone) was generally better than 0.15 per mille for nitrogen.
2.4 Land use features
We assessed the proportion of built‐up area (impervious surfaces e.g., buildings, roads, industrial areas), greenspace (public parks, cemeteries, private gardens, sports grounds), and agricultural fields (arable land i.e., intensively/extensively cultivated fields and fallow arable land excluding meadows and orchards) in a 1 km buffer zone around the vouchers’ collection sites using the open source geographic information system (QGIS) (QGIS Development Team, 2018). Calculations for the pre‐World War II vouchers were based on the digitized maps of the Preußische Luftaufnahmen (1927–1940) provided by the Technical University of Berlin (Figure 2a). Calculations for the recent samples (1968–2018) were based on the habitat type mapping of Berlin (Senate Department for Urban Development and Housing, 2014) and Brandenburg (Landesamt für Umwelt Brandenburg, 2009; Figure 2b).
2.5 Data analysis
Prior to FA analyses, measurement error (ME) was quantified by repeating measuring bones on both sides (right and left) in a subset of n = 20–22 individuals per limb character (see Niemeier et al., 2019). Significant ME outliers were identified using the Grubb’s test (Grubbs & Beck, 1972) leading to the exclusion of two tibio‐fibula measurements. We then applied a mixed‐model ANOVA (R package “lme4”; Bates et al., 2015) with Side as a fixed factor, Individual as a random factor, and the Side by Individual interaction as a mixed effect. Significance in the fixed factor Side would indicate directional symmetry, which has to be excluded. To verify that FA exceeded ME, variance components (σ2) were extracted from the random effects and signal (FA)‐to‐noise (ME) ratios calculated (Graham et al., 2010; Knierim et al., 2007). The variance component for the interaction () is an estimate for FA. The residual random variance () is an estimate for ME (Table 1). To check if ME was of similar magnitude for each character, variations in the degree of ME were tested with a mixed‐model ANOVA with Character as a fixed factor and Individual as a random factor (Figure S1). Absence of antisymmetry was validated for the whole dataset by examining the frequency distributions of the signed FA values that is, right side minus left side (R − L), visually for symmetry and kurtosis (Figure S2) and by using the Anscombe–Glynn kurtosis test (R package “moments”; Komsta & Novomestky, 2015; Table S1). Character‐size dependency was tested by Spearman’s rank correlation between absolute values of FA (|R − L|) and character‐size (averaged (R + L)/2; Table S1). Directional asymmetry was further excluded for the whole dataset by comparing deviations of the mean FA (R − L) from zero for each character with a one‐sample t test (Table S1). Characters suitable for FA analysis were finally selected according to the following criteria: (a) a significant level of FA; (b) a signal‐to‐noise ratio > 1 to avoid that FA was masked by ME; (c) no significant variation in the degree of ME; (d) absence of directional asymmetry and antisymmetry; and (e) no character‐size dependency in signed FA values. Only the humerus and radio‐ulna met all the criteria (Table 1). In a recent study, selection pressure on the symmetry of functionally important characters, such as the hind limbs of anurans, has been proposed as an explanation for that observation (Didde & Rivera, 2019). Therefore, we excluded the femur and tibio‐fibula from FA analyses.
|Character||Source of variation||df||Expected MS||Variance component σ2||Signal:noise ratio||F/LRT||p value|
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