Abstract It is wader, breeding on farmlands and other

Abstract

Changes in temperature has affected may species worldwide. The lapwing abundance is decreasing in East Anglia, as in Great Britain in general. The data gathered from the British Trust Ornithology, was used to analyse the values of population of lapwings across East Anglia. Significant fluctuations in population densities were found during the years 1994-2013. The decline in the population in the last years can be explained with the climate change, as the linear regression analysis showed that populations and summer and winter temperatures have significant positive linear relationship (p=0.0126 for summer, p= 0.0238 for winter).

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Keywords: Vanellus vanellus, northern lapwing, East Anglia, climate change, population fluctuations.

 

Introduction

Climate change has become, the largest threat to global biodiversity. Increased amount of “greenhouse gases” in the atmosphere and changes in the temperature, has affected birds’ behavior, such as causing shifts in migration patterns, altered food abundance and the distribution of competitor species. (Crick, 2004)

Because of the migratory nature of many bird species, they are especially affected by climate change. Also, they depend on the food supplies, making them dependent to their vulnerability. (Crick, 2004)

The northern Lapwing (Vanellus vanellus), present in East Anglia and all over United Kingdom, are migratory species. It is wader, breeding on farmlands and other grassland territories. As they live in habitats modified by humans, agricultural changes, such as pesticide usage or new crop types will be an important factor for lapwings. (Crick, 2004)

The lapwings lay eggs from late March to early June, and chicks hatch in a 3-4-week period. After hatching parents are taking them to the feeding areas with a big food supply and this transfer from the nesting to chick-rearing zones can be very dangerous. And they stay there for 5-6 weeks. (MacDonald & Bolton, 2008)

There is available data showing significant decline of lapwing populations in areas of England and Wales in the 1962-83 in cereal areas. Possibly related to changes in crop density and height.  (Shrubb, 2003). Lapwing numbers are correlated to the grassland management. The less intensive the management is, the bigger number of lapwings are in the territory. (Smith, 1983).

The population of the species is showing a fluctuation in the recent years.  In the IUCN Red List, it is classified as “Near Threatened”. (BirdLife International 2017).  In the UK the lapwing is classified as “Vulnerable” due to land use changed (intensified farming).  It is happening in most of Europe and is reflected in the declines across the continent. (RSPB, 2017)

Soil quality and type limit the invertebrates living in them and with this also limits the distribution of some bird species. In the case of lapwings, as their main source of food are earthworms, they are affected by the influence of UK soil PH and drainage results on earthworms. (Laursen, 1980).

The conservation of species facing extinction at the European scale is very important, as is analysing the main factors causing their decline. Agriculture is an important factor in their decline, but other possible factors  as the length of the breeding season, winter mortality and other impacts of climate, as it is a fast changing circumstance. (Williamson, 1975)

 The objective of this study is to find out the role, if any, of changing temperatures on the population declines in East Anglia.

Methods

We used data from the British Trust for Ornithology’s breeding bird survey dataset with the information from 1994 to now. Data gathering was performed by volunteers, at the sites of 1 km2. Transects were pre-designed and citizens performed bird count (species and number of individuals at 100 m maximum from the transect line) on the transects composed of 10 sections 200m each. And the temperature data was collected from the British Met Office.

Data was gathered for 366 grid squares over 20 years. But for our purposes only the grid squares, having data for at least 5 years was used that in total was 109 locations.

Data was analyzed on a seasonal basis (Summer and Winter) to better reflect the possible effect of changing temperatures on their life. As the transfer period from the to the chick-rearing nesting territories is very important to the chick survival, the summer temperatures would be very important to them.

 Linear models are used to test for possible effects of temperature on population index. To verify that the assumptions of the linear model are met few steps were performed:

(1)   After looking at the linear relationship the outlier #17 was removed (year 2010). (2)   Multivariate normality was tested using Q-Q plot and Kilmogorov-Smirnov tests, showing that data is normally distributed (P > 0.05).(3)   Testing Multicollinearity with Pearson model made it clear that summer and winter temperatures are little correlated and can be used as separate measurements.  (4)   Auto-correlation was assumed as the number of birds for the year depends on their number the year before.  (5)   Data homoscedasticity was tested using Fligner-tests that was not successful for unknown reason. (Fligner-Killeen: med chi-squared = NaN, df =18, p-value = NA

Results

From Table 2 we can conclude that, residuals are symmetrically distributed, the predicted values of the model are not far from the observed values.

The estimate of the average winter, as well as summer temperatures indicate a gradual and not a steep slope. The t value is not so far from 0. The P value or the slope is 0.0126 for Winter and 0.0238 for Summer temperature averages.

This confirms the linear relationship between the population index and summer temperatures, as well as relationship with winter temperatures.

The Residuals vs Fitted plot from the Figure 2 shows if residuals have non-linear patterns. In this case data is unbiased and homoscedastic. The plot of Normal Q-Q shows if residuals are normally distributed, that is a case in our analysis. The Spread-Location plot shows if residuals are spread equally along the ranges of predictors. With the result obtained from this plot we can assume homoscedasticity that we could not test before. The Residuals vs Leverage plot helps us to find influential cases, if any. All outliers do not influence the linear regression analysis. In our case after removing the data from 2010, that was outside the Cook’s distance, there are no outliers any more. (Bommae, 2018)