Muñoz-Pedreros Andrés, González-Urrutia Marilyn, Encina-Montoya Francisco, Norambuena Heraldo V.. 2018: Effects of vegetation strata and human disturbance on bird diversity in green areas in a city in southern Chile. Avian Research, 9(1): 38. DOI: 10.1186/s40657-018-0130-9
Citation:
Muñoz-Pedreros Andrés, González-Urrutia Marilyn, Encina-Montoya Francisco, Norambuena Heraldo V.. 2018: Effects of vegetation strata and human disturbance on bird diversity in green areas in a city in southern Chile. Avian Research, 9(1): 38. DOI: 10.1186/s40657-018-0130-9
Muñoz-Pedreros Andrés, González-Urrutia Marilyn, Encina-Montoya Francisco, Norambuena Heraldo V.. 2018: Effects of vegetation strata and human disturbance on bird diversity in green areas in a city in southern Chile. Avian Research, 9(1): 38. DOI: 10.1186/s40657-018-0130-9
Citation:
Muñoz-Pedreros Andrés, González-Urrutia Marilyn, Encina-Montoya Francisco, Norambuena Heraldo V.. 2018: Effects of vegetation strata and human disturbance on bird diversity in green areas in a city in southern Chile. Avian Research, 9(1): 38. DOI: 10.1186/s40657-018-0130-9
Núcleo de Investigación en Estudios Ambientales NEA, Departamento de Ciencias Ambientales, Facultad de Recursos Naturales, Universidad Católica de Temuco, Casilla 15-D, Temuco, Chile
2.
Programa de Biodiversidad y Manejo de Vida Silvestre, Centro de Estudios Agrarios y Ambientales, Casilla 164, Valdivia, Chile
Urbanisation is a dominant geographical trend and an important component of global change, with unprecedented implications for socio-economic, cultural and environmental characteristics. However, green areas, including original fragments, can help to conserve native diversity, improving the functioning of these artificial systems in the long term. Urban areas can still provide habitats usable by wild birds, however the structural characteristics of the habitat formed by different types of green area differ, and therefore dissimilar bird diversities are to be expected. The object of this study was to characterise the α and β diversities of birds in different green areas and to analyse how diversity relates to ten variables that characterise the habitat.
Methods
We studied the green areas in the city of Temuco, southern Chile (Park, Square and Median strips of main streets), evaluating the variables: (a) surface area, (b) vegetation, (c) estimated human impact as the proportions of vegetation and bare soil by area, and the vehicle traffic. The bird assemblage structures were characterised by α (intra-environment) diversity and β diversity (between environments) and the statistical analysis identified the environmental variables related with the presence and abundance of birds. A statistical model was constructed to describe the contribution of the variables to bird diversity.
Results
We found significant differences between the diversity of bird species in the three types of green area. The β showed medium to high similarity between the different study units. There was a negative correlation with bare soil areas; the correlations with vehicle flow, plant structure and tree and shrub cover were not significant, meaning that these variables did not explain the variation in the richness of bird species between the green areas. However the surface area did explain this variation presenting a positive potential relation. There was also a high correlation with the origin (native) of shrub species.
Conclusions
The bird diversity varied significantly according to the type of urban green area. The environmental variables presenting significant correlations with bird diversity were: surface area, native species of shrub stratum, shrub cover, and bare soil area. The best multiple regression model showed that the three most important variables for bird diversity are the surface area of the green area, the cover of the shrub stratum and the presence of native shrub species.
During migration birds face many challenges, including unfamiliar foraging and refuge habitats, resulting in a much higher rate of mortality during migration than during other seasons of the year. Weather may significantly affect a bird's decision to initiate migration, the course and pace of migration, and its survival during migration (Miller et al. 2016). Favourable weather conditions for migration enhance the orientation of birds, reduce the use of energy for flying and increase the speed of migration (Emlen 1975; Bloch and Bruderer 1982; Gauthreaux 1982; Akesson 1993; Liechti 2006; Shamoun-Baranes et al. 2017). According to the majority of the studies, most migrations take place in windless, clear, anticyclonic weather conditions without precipitation, or with support of tail winds (Alerstam 1990; Gyurácz et al. 1997, 2003; Bruderer and Boldt 2001; Erni et al. 2002). Favourable conditions can occur in different macrosynoptic weather situations (Kerlinger et al. 1989). Atmospheric conditions are the primary extrinsic factors influencing decisions regarding migratory flights, particularly over water bodies with limited opportunities to land (Richardson 1990). Wind plays a critical role, affecting departure date and migratory directions, routes, speeds, flight durations, energy consumption and the crossing of ecological barriers (Cochran and Kjos 1985; Weber and Hedenström 2000; Pennycuick and Battley 2003; Cochran and Wikeski 2005; Bowlin and Wikelski 2008; Shamoun-Baranes and van Gasteren 2011; Bulte et al. 2014; Gill et al. 2014). Decisions to depart stopover sites and initiate flight over large water bodies are also influenced by barometric pressure, temperature, relative humidity, and short-term trends in these variables, which are indicative of synoptic weather patterns and may provide information about future weather conditions (Able 1972; Newton 2008). In case of tail winds, birds are capable of flying longer distances while exerting less energy (Emlen 1975; Bloch and Bruderer 1982; Gauthreaux 1982, 1991; Alerstam 1990; Richardson 1990; Bruderer and Boldt 2001). Weather conditions that delay migration include cloudy skies, poor visibility, strong winds (head winds and cross winds), and warm or occluded fronts (Akesson 1993; Pyle et al. 1993).
This study aims to describe the effects of various weather elements on the numbers of migrating Siberian breeding Phylloscopus warbler species at a stopover site in Far East Russia.
Methods
The study was carried out within the Amur Bird Project during spring(2013, 2015, 2016, 2017) and autumn (2011‒2014) migration at Muraviovka Park along the middle stream of the Amur River in the Russian Far East (Heim et al. 2012). The study site is located 60 km southeast of the city of Blagoveshchensk (49°55ʹ08.27ʺN, 127°40ʹ19.93ʺE).
The study periods were the following: in 2011, from 7 September to 15 October; in 2012, from 29 August to 15 October; in 2013, from 25 April to 08 June and from 27 July to 17 October; in 2014, from 25 July to 15 October; in 2015, from 25 April to 10 June; in 2016, from 25 April to 07 June, while in 2017, from 28 April to 08 June. During this period, in total 52 mist-nets were used for the work. Netting sites were not changed within season, however, the sampling effort varied among years (Table 1). The nets were set up in a variety of habitats: homogeneous reed-beds, sedges and grassy swamps interspersed with willows and raspberries, rich shrub-layered mixed forest, very dense scrub and stubble. The work was carried out daily from sunrise to sunset and the nets were checked every hour. In case of storm, heavy wind and rain, nets were closed.
Table
1.
Sampling effort between 2011‒2017 at the study site (total number of nets × daily mist-netting hours)
Data analysis was carried out based on 6191 individuals of four species: Yellow-browed Warbler (Phylloscopus inornatus), Dusky Warbler (P. fuscatus), Radde's Warbler (P. schwarzi) and Pallas's Leaf Warbler (P. proregulus) (Table 2). All birds were marked with rings of the Moscow Ringing Centre. Species identifications were based on Svensson (1992) and Brazil (2009). We only included data of first captures and excluded all recaptures.
Table
2.
Number of ringed birds for all study species and their migration periods at Muraviovka Park
To analyse the effects of weather for bird migration, we collected the following weather data for every day: minimum and maximum temperature (℃), precipitation (mm), average air pressure (mb), average wind speed (Beaufort scale 0‒12), average wind direction (cross winds: E and W winds in spring and autumn; tail winds: N, NE and NW in autumn and S, SE and SW in spring; head winds: N, NE and NW in spring and S, SE and SW in autumn). Air pressure and precipitation data were gathered from the webpage of World Weather Online (World Weather Online 2017) for the area of Blagoveshchensk, while the remaining data were collected by the ringing teams at Muraviovka Park.
The migration periods are species-specific, therefore it was necessary to determine migration periods for each species. On the basis of the daily numbers caught, migration periods were determined for all studied species (Fig. 1). Only days within the migration period were considered in the following models.
Figure
1.
Spring (left) and autumn (right) migration periods of the four leaf warbler species
General Linear Mixed Models (GLMM) were used to evaluate the weather impact on the number of trapped birds per day. Models were built for each species for both spring and autumn season. Year and Julian day were included as random factors. Minimum temperature was excluded from the models, as it was highly correlated with maximum temperature (R = 0.77, t = 30.2, df = 722, p < 0.001). The full models were of the following structure:
the number of birds trapped per day
~ temperature + precipitation + air pressure
+ wind speed + wind direction + 1|year + 1|day.
Furthermore, we built a model for all four species together, and included species as additional random factor as well. We checked for overdispersion using the dispersion-glmer function of the R package blmeco. For analysis, R version 3.4.2 (R Core Team 2017) and packages lme4 (Bates et al. 2014) and piecewiseSEM (Lefcheck 2016) were used.
Results
The migration periods of the four study species are shown in Table 2.
The GLMM for single species explained only a small part of the variability, with higher values during autumn migration (Rmarg2 : 0.07‒0.18) compared to spring migration (Rmarg2 : 0.03‒0.10). This was also true for the overall model, including all four species—see Table 3. Much more of the variance was explained by the random factors (Rcond2) (Table 3).
Table
3.
Outputs of general linear mixed models
Species
Spring migration
Autumn migration
Estimate
χ2
p
Estimate
χ2
p
ALL
Temperature
0.42
133.3
***
Temperature
0.22
18.4
***
Precipitation
0.09
8.7
0.003
Precipitation
- 0.53
121.4
***
Air pressure
‒0.10
9.4
0.002
Air pressure
- 0.25
39.3
***
Wind speed
0.09
9.3
0.002
Wind speed
- 0.29
65.6
***
Wind direction
65.8
***
Wind direction
54.6
***
Headwind
- 0.30
Headwind
- 0.39
Crosswind
- 0.05
Crosswind
- 0.17
Tailwind
- 0.72
Tailwind
0.08
Rmarg2
0.03
Rmarg2
0.12
Rcond2
0.82
Rcond2
0.84
Dusky Warbler
Temperature
0.41
17.0
***
Temperature
0.35
16.9
***
Precipitation
0.09
1.0
0.321
Precipitation
- 0.50
37.7
***
Air pressure
- 0.01
0.0
0.861
Air pressure
- 0.13
4.3
0.039
Wind speed
0.08
1.0
0.310
Wind speed
- 0.09
2.4
0.121
Wind direction
17.0
***
Wind direction
18.8
***
Headwind
0.42
Headwind
0.29
Crosswind
1.12
Crosswind
0.53
Tailwind
0.08
Tailwind
0.76
Rmarg2
0.09
Rmarg2
0.18
Rcond2
0.75
Rcond2
0.84
Pallas's Leaf Warbler
Temperature
0.40
5.2
0.022
Temperature
- 0.27
2.3
0.126
Precipitation
0.01
0.0
0.946
Precipitation
- 0.24
2.2
0.137
Air pressure
- 0.03
0.0
0.870
Air pressure
0.10
0.7
0.417
Wind speed
0.02
0.0
0.900
Wind speed
- 0.43
13.1
***
Wind direction
4.7
0.096
Wind direction
6.3
0.043
Headwind
- 1.11
Headwind
- 2.05
Crosswind
- 0.34
Crosswind
- 1.74
Tailwind
- 1.35
Tailwind
- 1.51
Rmarg2
0.09
Rmarg2
0.07
Rcond2
0.34
Rcond2
0.71
Radde's Warbler
Temperature
0.56
8.9
0.003
Temperature
0.38
5.9
0.016
Precipitation
0.00
0.0
0.993
Precipitation
- 0.45
8.5
0.004
Air pressure
0.01
0.0
0.932
Air pressure
- 0.54
16.2
***
Wind speed
0.13
0.7
0.399
Wind speed
- 0.27
16.2
0.022
Wind direction
2.5
0.292
Wind direction
6.3
0.043
Headwind
- 1.12
Headwind
- 2.07
Crosswind
- 1.17
Crosswind
- 1.41
Tailwind
- 1.67
Tailwind
- 1.77
Rmarg2
0.10
Rmarg2
0.13
Rcond2
0.44
Rcond2
0.70
Yellow-browed Warbler
Temperature
0.44
118.6
***
Temperature
0.21
7.5
0.006
Precipitation
0.09
7.1
0.008
Precipitation
- 0.57
68.7
***
Air pressure
- 0.14
16.4
***
Air pressure
- 0.32
24.6
***
Wind speed
0.08
6.6
0.010
Wind speed
- 0.45
71.3
***
Wind direction
46.1
***
Wind direction
27.6
***
Headwind
0.98
Headwind
0.39
Crosswind
1.12
Crosswind
0.59
Tailwind
0.57
Tailwind
0.89
Rmarg2
0.03
Rmarg2
0.18
Rcond2
0.94
Rcond2
0.92
Estimates refer to the slope related to the variables, but represent the mean for categorical variables (wind direction, in italics). Shown are test statistics, Rmarg2 (fixed factors alone) and Rcond2 (fixed + random factor). p values below 0.001 are marked as ***
During spring, the most important factor for all species is temperature, with significantly more birds trapped during warmer days. Furthermore, wind direction is a significant predictor in the overall model and in two out of four species models, with the highest number of birds trapped during cross winds.
In autumn, the number of trapped warblers is negatively affected by precipitation and wind speed, while maximum temperature is positively related with the number of trapped individuals in the overall model and in three out of four species. Wind direction is an important factor in all models, with most birds trapped during tail wind, and least birds during head winds. Air pressure was found to have minor impact on the numbers, but a negative relation was found in three out of four species.
Discussion
During our work, the effects of air pressure, rainfall, temperature, wind strength and wind direction were investigated for four species. We found strong impacts of weather variables on the number of trapped warblers during spring and autumn migration. Overall, preferred or avoided weather conditions were similar among the studied species, but some slight differences were found.
All species seem to migrate preferably during warm, calm days without precipitation in spring and autumn too. It is important to note that in case of intense rainy weather, mist-nets were closed for the safety of the birds, but there would certainly not have been a significant amount of bird in these days. The fact, that the maximum temperature is positively related with the number of trapped individuals in the overall model and in three out of four species is certainly associated with the timing of migration.
Previous studies have shown that the migration peak of most species coincides with days of high air pressure (Alerstam 1990; Gyurácz et al. 1997). On such days, there is constant sunshine and low temperatures. In Siberia, a high anti-cyclone reigns from September to April, with its centre near Lake Baikal. This Siberian cycle is often characterized by high air pressures above 1040 mb and extremely low temperatures (Oliver 2005). In spring, however, the effects of cyclones are characterized by high precipitation and high temperature values. In contrast, air pressure was found to have minor impact on the numbers of trapped warblers both during spring and autumn migration, but a slight negative relation was found in three out of four species in autumn. This suggests that the studied species seem to migrate independently from the air pressure.
Both the strength and the direction of the wind can be decisive in the timing of migration (Elkins 1988). In stormy winds, mist-netting is not possible, but as during heavy rains, few birds are expected to fly during such conditions. Accordingly, the number of birds of all species and the strength of wind were negatively correlated in spring and autumn migration.
In autumn, in cases of a northerly wind (tail wind), birds are known to fly longer distances while exerting less energy (Emlen 1975; Bloch and Bruderer 1982; Gauthreaux 1982, 1991; Alerstam 1990; Richardson 1990). Accordingly, we would expect the highest numbers of migrating birds in autumn during northern tail winds and in spring during southern tail winds. However, a positive effect of tail winds was only confirmed in autumn. Tail winds were a good predictor in case of Dusky and Yellow-browed Warblers in autumn, which may support the weather hypothesis, according to which some authors explain the westward vagrancy of Siberian leaf warblers species by the effect of the weather (Baker 1977; Howey and Bell 1985; Baker and Catley 1987). It is interesting to note that in spring, most birds were trapped during cross winds (eastern or western winds). It seems likely, that the studied warbler species exhibit a loop migration, with their spring migration routes closer to the East Asian coasts, leading to a more longitudinal migration pattern (from east to west) in our study area during spring (Fig. 2). The Wood Warbler (Phylloscopus sibilatrix) and Willow Warbler (Ph. trochilus), well-studied European breeding species, show also loop migration (Gyurácz and Csörgő 2009; Jónás et al. in press), and a similar pattern was found for geolocator-tracked Siberian Rubythroats (Calliope calliope) along the East Asian flyway (Heim et al. 2018a).
Figure
2.
Breeding and wintering grounds, and the supposed migration route of the study species. The study site (Muravovka Park) is also marked on the map. Distribution maps for each species based on The IUCN Red List of Threatened Species (2018)
Despite the significant impacts of the weather variables, most of the variation was explained by interannual differences and preferred migration timing. The studied species are likely mainly following their innate migration schedule. Consistent migration timing between years was also found for Emberiza buntings at our study site (Heim et al. 2018b). This might especially be true for spring migration, when competition is high to arrive early at the breeding grounds (Nilsson et al. 2013), which could force individuals to continue migration under sub-optimal weather conditions.
Global change is expected to significantly alter weather conditions in Far East Russia (Mokhov and Semenov 2016) and a better understanding of how these factors could influence migratory birds is urgently required.
Conclusion
During our work we found strong impacts of weather variables (air pressure, rainfall, temperature, wind strength and wind direction) on the number of trapped warblers during spring and autumn migration. Birds seem to migrate preferably during warm, calm days without precipitation, however, relationships between weather variables and the number of migrating individuals were much stronger during autumn. Air pressure was found to have minor impact on the numbers of trapped warblers, while the strength and the direction of wind were important variables. Tail winds play an important role in autumn, but in spring migration was determined by cross winds, which indicates that the studied species might exhibit a loop migration.
Authors' contributions
LB and WH collected field data. TCS provided new ideas for this manuscript. LB and WH performed data analyses. LB collected the weather data from the online dataset. LB wrote an early version of the manuscript. WH and TCS revised and improved the manuscript. All authors read and approved the final manuscript.
Acknowledgements
The authors want to thank Sergei M. Smirenski and the staff of Muraviovka Park as well as the Amur Bird Project field teams for enabling these studies in Far East Russia. We kindly acknowledge the provision of rings by the Moscow Bird Ringing Centre, and thank Ramona J. Heim and Johannes Kamp for helpful comments on the statistical analysis.
Competing interests
The authors declare that they have no competing interests.
Availability of data and materials
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Ethics approval and consent to participate
The authors confirm that all experiments were carried out under the current law for scientific bird ringing in Russia, and all necessary permissions were obtained.
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Estimates refer to the slope related to the variables, but represent the mean for categorical variables (wind direction, in italics). Shown are test statistics, Rmarg2 (fixed factors alone) and Rcond2 (fixed + random factor). p values below 0.001 are marked as ***
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