Polar Bear Habitat Dynamics in a Changing Arctic Environment

Author

Manon Gressard, Zoe Dardare, Ugo Rombach, Victorien Rodondi

Published

December 20, 2023

1 Executive summary

Climate change is an unprecedented challenge that poses significant threats to the delicate ecosystems of the Arctic, impacting iconic species such as the polar bear. This project aims to delve into the intricate relationship between polar bear habitat utilization and the changing environmental conditions in the Arctic. By analyzing available GPS data, we seek to map the current distribution of polar bears, laying the foundation for understanding their baseline habitat preferences.

Moving beyond the static representation of polar bear distribution, our second objective involves a thorough analysis of the correlation between sea ice coverage and polar bear location data over time. Investigating how polar bear movements align with changes in sea ice will provide valuable insights into their habitat preferences and adaptation strategies.

The third key objective extends our focus to the broader implications of climate change on Arctic ecosystems. We will specifically examine temperature variations and ice melt patterns in critical polar bear habitats, such as the regions in Alaska. Through this investigation, we aim to shed light on the effects of climate change on the availability and suitability of polar bear habitats, offering a comprehensive perspective on the challenges polar bears face.

2 Introduction

2.1 Overview and motivation

As climate change accelerates, the Arctic is experiencing unprecedented shifts in sea ice patterns and temperature regimes, directly affecting the habitats critical to polar bear survival. The motivations behind this project are deeply rooted in the urgency of addressing the profound impact of climate change on Arctic ecosystems. The alarming decline in sea ice coverage poses a direct threat to the bears’ ability to hunt, breed, and sustain their populations. This project is driven by a commitment to comprehensively understand the intricacies of polar bear habitat utilization, the correlation between their movements and changing sea ice conditions, and the broader consequences of climate change on their ecosystems. This project marries the necessity of thorough, data-driven research with the urgency of the current climate crisis. Through our work, we aim to enrich the dialogue on wildlife conservation and support policies that help secure a future for the Arctic’s most emblematic species.

2.2 Research questions

  1. How has climate change impacted polar bear movement patterns and habitat utilization as depicted by GPS data?

  2. Can movement patterns of polar bears be predicted using GPS data?

  3. Is there a link between the diet of polar bears and the number of days they spend in open water?

3 Data

3.1 Data Source

Our first database is from the Living Planet Index (LPI), which tracks population trends of various vertebrate species and serves as a barometer for global biodiversity. This data science explores the population dynamics of ‘Ursus maritimus’ (polar bears) and ‘Pusa hispida’ (ringed seals) through five-year moving averages. As polar bears predominantly feed on seals, understanding their interrelation is vital for assessing the Arctic ecosystem’s health. Despite challenges like irregular data collection and methodological variances, we aim to differentiate genuine ecological trends from potential data anomalies, shedding light on how these species are navigating the evolving environmental landscape.

For our analysis of bear movements, we use the data set collected by USGSIt. The data contains polar bear tracking.

We collected the minimum and maximum temperatures over a period from 1993 to 2023 from the PELLY ISLAND weather station. This is the most advanced weather station on the Beaufort Sea coast.

The last data frame provides information about the Northern Hemisphere-wide sea ice coverage. MASIE-NH (Multisensor Analyzed Sea Ice Extent – Northern Hemisphere) gives a graphical view of ice extent in various formats.. It relies on visible imagery data, so the position of the ice edge will generally be more accurate than that of the sea ice index. The input is the daily 4km sea ice component of the National Ice Center (NIC) Interactive Multisensor Snow and Ice Mapping System (IMS).

The third database has been provided by Aerial Master. The first data set provides us with information about location of polar bears between 1979 and 2011 in Alaska, and the second provides information from 2015 to 2017. Our purpose is to join the two data sets from the Aerial database, into one.

The third database gives us information about the diets of polar bears in Beaufort, which is a marginal sea of the Arctic Ocean that stretches north from the coast of Alaska. This database is very relevant for EDA and further analysis. It has been provided by USGSIt.

3.1.1 Data Description

Data Frame 1, Aerial Master:

Variables Meaning
ID Unique ID per observation
GMT_Minus8_DateTime Date and time of the observation
ArcLat Latitude
Arclong Longitude
Many others variables finally not used ...

Data Frame 2, Beaufort Polar Bears:

Variables Meaning
BearID Unique observation per bear
Year Year of the capture
Capture.date Date of the capture
Ageclass The stage of maturity of the polar bear
Sex The sex of the polar bear
OW_50pcrt Number of days covered in open water
Meltseason Number of days of the meltseason in the corresponding year
Bearded_seal Percentage of bearded seal eaten in the corresponding period
Ringed_seal Percentage of ringed seal eaten in the corresponding period
Beluga_whale Percentage of beluga whale eaten in the corresponding period
Bowhead_whale Percentage of bowhead sealed eaten in the corresponding period
Seabird_nestling Percentage of seabird nestling eaten in the corresponding period

Data frame 3, Sea Ice Cover:

Variables Meaning
yyyyddd Date of the observation
Beaufort_Sea Daily 4km sea ice component in the region of Beaufort Sea
Many others variables finally not used ...

Data Frame 4, Temperatures:

Variables Meaning
Date Date
TAVG..Degrees.Fahrenheit. Average temperature in degrees Fahrenheit
TMAX..Degrees.Fahrenheit. Maximum temperature in degrees Fahrenheit
TMIN..Degrees.Fahrenheit. Minimum temeperature in degrees Fahrenheit
Many others variables finally not used ...

3.2 Data Cleanup

We are going to organize our databases into several tables that will help us to answer our research questions individually.

3.2.1 Living Planet Database

We start by defining the year columns and convert them to numeric, then we calculate the annual sum for each species. We then calculate a 5-year cumulative sum for each species, and prepare the data for plotting. We also calculate the moving average for the plot data. Then we convert all columns with names starting with ‘X’ followed by 4 digits to numeric, and replace non-numeric values in these columns with NA. We calculate the sum of values in columns starting with ‘X’, select only relevant columns for mapping, and plot the global distribution of polar bears.

3.2.2 Aerial Master Database

Here’s how we’re going to proceed for our Aerial Master table. We start to calculate the number of missing values for each column in the AM data set, then we identify the columns with more than 200,000 missing values, and decide to remove them from the data set. For better clarity, we rename the columns in the data set. Then we calculate the proportion of missing values for each column in the AM data set, and create a data frame with column names and proportions of missing values, by sorting the data frame by the proportion of missing values in descending order.

We create a bar chart to visualize the proportion of missing values for each column:

Due to the significant amount of missing data, only location data is retained. Other columns are binary variables representing parameters of little interest or with too many missing values. We then calculate the proportion of missing values for each column in the final dataset, and we remove rows with missing values from it. Then we separate the “Datetime” column into individual components (month, day, year, hour, minute, second, period), and select relevant columns for further analysis.

We create a bar chart to visualize the number of observations per year:

Let’s do the same operations for the second database Aerial Master 2015-2017. We repeat the same operations on the second data set than on the first one to clean it up.

Again, we create a bar chart to visualize the number of observations per year.

We combine the filtered data from the two final datasets into a single data frame.

For the third database about the diets of polar bears in Beaufort, there is fast no data cleaning.

x
BearID 0
Year 0
Capture date 0
Ageclass 0
Sex 0
OW_50prct 0
OW_15prct 0
Meltseason 0
Bearded_seal 0
Ringed_seal 0
Beluga_whale 0
Bowhead_whale 0
Seabird_nestling 0

Since there are no missing values, the data set is considered “perfectly clean”.

We rename specific columns in the “pb” data set for better clarity.

3.2.3 Polar Bear Distribution Database

We began by extracting the essential information, such as each bear’s identifier, date and geographical coordinates. This data was then converted into standardised formats to ensure the consistency and accuracy of the analysis. A crucial aspect of our processing was data cleaning, where we eliminated incomplete records to ensure the reliability of our study. After organising the data by bear identifier and date, we calculated the distance traveled between successive bear points using Haversine’s formula. As a result, the first GPS broadcast for each bear does not have a value in the distance column calculated in this way. This method provides us with valuable insights into the movement patterns of bears. This approach created a coherent and detailed data set, essential for our behavioural analysis of bear movements in their natural habitat.

3.2.4 Temperatures Data Base

In order to clean this data set, we converted the temperatures from Fahrenheit to Celsius, an international standard that makes them easier to understand and compare. The next step was to merge the maximum and minimum temperature data based on date to get a consistent overview. Finally, we calculated the daily average temperature, which provides a more balanced and meaningful perspective of weather conditions. To simplify and clarify our results, we chose to keep only the mean temperature data, eliminating the maximum and minimum temperature columns. This process provided an accurate and easily interpretable overview of climate trends on Pelly Island.

3.2.5 Sea Ice Cover Data Base

To refine our analysis of the Beaufort Sea, we first filtered the data, discarding information relating to other maritime regions. Next, we converted the date format from ‘yyyyyddd’ to a standard format, facilitating temporal analyses.These data preparations are crucial to ensuring the accuracy and relevance of our future analyses.

3.2.6 Combined Data Sets

From this data set, we assigned the nearest mean temperature date to each polar bear GPS point. In order to do this, we scanned each polar bear observation, calculated the difference in days between that observation and the dates of the temperature readings, and selected the temperature corresponding to the closest date. This process made it possible to precisely link the climatic conditions to the specific positions of the bears.

3.2.7 Data Set Regression for Travel Forecasts

We load the data set we have been working on in the Analysis section. We will need it to be able to predict bear movement in the future.

  • We use a methodical approach to develop and evaluate predictive models of latitude and longitude. We began by dividing our selected data set into two distinct sets: one for training and one for testing, ensuring reproducibility through a fixed seed parameter. This division ensured that models would be trained on a subset of the data and tested on a separate set, not used during training, for an unbiased assessment of their performance.

  • We then trained two separate models using the random forest algorithm, one to predict latitude and the other for longitude, excluding longitude and latitude respectively from the predictor variables to avoid redundancy. After training, we used the models to predict latitude and longitude values on the test set.

  • The performance of the models was evaluated by calculating the root mean square error (RMSE) for the latitude and longitude predictions. The results showed an RMSE of 1.2227 for latitude and 13.4141 for longitude. By quantifying the mean difference between predicted and actual values, these RMSE measurements revealed that the latitude model was relatively accurate, while the longitude model had a greater margin of error.

  • We extend our study to include predictions over the next five years. Using the latest date in our data set as a starting point, we have generated a sequence of future dates. To these dates, we used a function to associate the relevant mean temperature and ice extent data for the Beaufort Sea, taking care to fill in any gaps with the median of the available temperatures, to maintain data consistency.

  • We then enriched the future data by assigning bear identifiers (BearIDs) to each GPS point, simulating the tracking of 20 different bears. This step allowed us to contextualize our predictions within a more realistic framework, reflecting the potential diversity of bear movements in the future.

  • Using the random forest models, we had previously trained, we predicted the geographical positions (latitude and longitude) for each combination of BearID and future date. These predictions, based on anticipated environmental conditions, were compiled into a new dataset, providing a prospective view of possible polar bear movements in the face of future climate and environmental change. This work represents a significant advance in our understanding of polar bear dynamics and their potential adaptation to changes in their habitat.

#>      date               bearid       latitude      longitude   
#>  Length:193502      Min.   : 32   Min.   :63.9   Min.   :-180  
#>  Class :character   1st Qu.:170   1st Qu.:70.6   1st Qu.:-154  
#>  Mode  :character   Median :248   Median :71.2   Median :-148  
#>                     Mean   :228   Mean   :71.9   Mean   :-148  
#>                     3rd Qu.:277   3rd Qu.:72.4   3rd Qu.:-143  
#>                     Max.   :301   Max.   :82.4   Max.   : 180  
#>                                                                
#>     Distance       Closest_Avg_Temperature  Count_Bears  
#>  Min.   :      0   Min.   :-38             Min.   : 1.0  
#>  1st Qu.:    324   1st Qu.:-18             1st Qu.:15.0  
#>  Median :    991   Median : -5             Median :24.0  
#>  Mean   :   3536   Mean   : -7             Mean   :24.6  
#>  3rd Qu.:   2444   3rd Qu.:  2             3rd Qu.:37.0  
#>  Max.   :1157362   Max.   : 21             Max.   :43.0  
#>  NA's   :130       NA's   :5498                          
#>      month          season             yyyyddd       
#>  Min.   : 1.00   Length:193502      Min.   :2006001  
#>  1st Qu.: 4.00   Class :character   1st Qu.:2009188  
#>  Median : 6.00   Mode  :character   Median :2011287  
#>  Mean   : 6.15                      Mean   :2011306  
#>  3rd Qu.: 8.00                      3rd Qu.:2013178  
#>  Max.   :12.00                      Max.   :2016172  
#>                                     NA's   :2554     
#>   Beaufort_Sea    
#>  Min.   : 189237  
#>  1st Qu.: 913555  
#>  Median :1058547  
#>  Mean   : 964571  
#>  3rd Qu.:1070445  
#>  Max.   :1070445  
#>  NA's   :2554

4 Exploratory Data Analysis

The two following graphs show cumulative trends over 5 years for two distinct species: the ringed seal (Pusa hispida) and the polar bear (Ursus maritimus), respectively. The first trend, represented by a green line, shows the cumulative sum over 5 years of seal counts. This shows how the total number of seals and bears observed has evolved over a rolling 5-year period. The second trend, a dotted red line, represents a moving average. This average smoothes out annual variations to give a better idea of the general trend over the long term. Note: on n’a plus de données a partir de 2010 pour les ours!!

Focus on Alaska

In light of recent data, predominantly sourced from the American continent, there is growing concern over the declining population of Polar Bears (Ursus maritimus). This trend warrants a closer examination of the origins of our data. To gain a clearer understanding of these patterns, we will delve into the geographic source of this information. Such an approach is crucial, as it allows us to pinpoint specific regions contributing most significantly to the data set.

On the following map, we identify the locations from which the majority of our polar bear sightings are reported. Given the essential nature of these data for understanding the state of polar bear populations, identifying the main source regions is a step that helped us greatly in subsequently guaranteeing the robustness of our results.

From this graph, we will identify the locations from which the majority of our polar bear sightings are reported. Given the essential nature of these data for understanding the state of polar bear populations, identifying the main source regions is a step that helped us greatly in subsequently guaranteeing the robustness of our results.

In order to address our research questions, we decided to focus on Alaska, as it is the region that provides the best amount of data.

4.1 How has climate change impacted polar bear movement patterns and habitat utilization as depicted by GPS data?

In this initial phase, we have gathered the essential data to analyze the movement of polar bears and the impacts of climate change. We selected temperature readings from various meteorological stations in the Beaufort Sea as our primary indicator. Additionally, we have investigated the trends in ice cover and its temporal fluctuations. The process of processing, merging, and analyzing disparate data sets presents a significant challenge. Currently, our statistical analysis primarily considers temperature changes as the key factor in explaining the migration patterns of polar bears.

Code Visualization

To represent the movements of polar bears within our data set, we needed to generate a trend for the average distance these mammals travel each day. We calculated the total distance traveled by each bear per day and per year. Next, we determined the daily average of this distance for each bear per year. Finally, we calculated the overall average of these daily averages for all the bears, year by year, in order to identify a general trend in the distances traveled by polar bears over time. These results are shown in the following interactive graph, and you can select a specific year by double-clicking on it. As explained in the graph (graph number on the number of gps points per year) we have a wide disparity in our data depending on the year. Despite this, a trend in the daily distance traveled by polar bears can be seen in the graph. There has been a downward trend in this distance over the last 15 years.

In addition to temperature and ice cover in the Beaufort Sea, we want to add another variable to our data set. It seems important to us to reflect the number of active polar bears (i.e. transmitting GPS data) present in our data set on a date T. This is why we have created a “Bear Counter” function, which associates the number of active polar bears with each GPS point. This allows us to adjust our data set in anticipation of our analysis. The following graph shows this function and thus the evolution of the number of active polar bears in the study over the years. Here again we can see that our data is fairly heterogeneous from year to year. This reinforces our idea of increasing our understanding of the data set to develop a rigorous statistical analysis.

We have been able to separate the temperatures into 4 seasons over the years. We then notice a very strong seasonality through the average temperatures. What’s more, we don’t specifically see an increase or decrease in these temperatures over time.

To represent the average ice cover in the Beaufort Sea, we have also chosen to divide it up by season. In the following graph, you can double-click on a season to isolate it from the others. As with temperature, there is a strong seasonal effect on the surface area (in km2) of ice in the Beaufort Sea. In addition, there is a tendency for this to decrease over the years.

4.2 Can movement patterns of polar bears be predicted using GPS data?

Code Visualization

We can then plot the movements of each polar bear in our Beaufort Sea study on a map. What’s more, each GPS point is characterised by its year and the distance the polar bear has traveled since its last GPS fix. As the GPS points are not homogeneous depending on the BearID and the year, the track between the points is not the real trajectory of the polar bear selected. It presents the general idea of the journey made. For greater accuracy, it would be necessary to increase the number of GPS readings over time.

5 Analysis

5.1 A decreasing polar bear population

We can observe that the seals’ numbers oscillate yet show an overarching increase, which hints at their adaptability to environmental shifts—a resilience that might be bolstered by conservation measures. However, data irregularities, such as inconsistent collection frequencies and varying methodologies, could be obscuring the true narrative of these trends. These factors remind us to interpret the data cautiously.

Anyhow polar bears present a different story. The data, primarily from the American continent, indicate a concerning decrease in their population. The retreating Arctic ice is a clear signal of the climate crisis, stripping away the habitat crucial for the bears’ hunting and breeding. The data’s geographical limitations warrant a cautious approach to generalizing these findings across the glob.

From our study, we can observe that the decline in the number of polar bears is markedly more significant than the one of their primary prey, the seals. This pattern suggests a hypothesis: faced with an invisible enemy and the only predator of polar bears – climate change – these bears are in a dire situation. The warming climate leads to the ice in the bay forming later and melting earlier each year, which may abbreviate the seal hunting season to such an extent that polar bears may not have sufficient time to accumulate the fat reserves needed to survive their summer fast. The breeding cycle of the females could be disrupted, threatening the continuity of the species.

5.2 Impact of climate change on polar bear movement patterns and habitat utilization depicted by GPS data

#> 
#> Call:
#> lm(formula = Distance ~ Closest_Avg_Temperature + Count_Bears + 
#>     latitude + longitude + season, data = ours_clean)
#> 
#> Residuals:
#>     Min      1Q  Median      3Q     Max 
#>  -16035   -6269   -4127   -1406 1173337 
#> 
#> Coefficients:
#>                          Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)             -15704.36    2148.12   -7.31  2.7e-13 ***
#> Closest_Avg_Temperature   -218.36       4.03  -54.15  < 2e-16 ***
#> Count_Bears                 98.31       4.69   20.98  < 2e-16 ***
#> latitude                   277.32      28.59    9.70  < 2e-16 ***
#> longitude                    4.61       3.24    1.42     0.15    
#> seasonSpring             -4241.47     147.27  -28.80  < 2e-16 ***
#> seasonSummer                39.89     149.27    0.27     0.79    
#> seasonWinter             -3645.02     195.35  -18.66  < 2e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 24000 on 249484 degrees of freedom
#>   (14394 observations deleted due to missingness)
#> Multiple R-squared:  0.0158, Adjusted R-squared:  0.0158 
#> F-statistic:  572 on 7 and 249484 DF,  p-value: <2e-16
#> 
#> Call:
#> lm(formula = Distance ~ Closest_Avg_Temperature + Count_Bears + 
#>     Beaufort_Sea, data = dataset_regression)
#> 
#> Residuals:
#>    Min     1Q Median     3Q    Max 
#>  -7199  -3321  -2024   -955 184946 
#> 
#> Coefficients:
#>                          Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)              7.88e+03   3.65e+02    21.6   <2e-16 ***
#> Closest_Avg_Temperature  2.76e+02   1.34e+01    20.6   <2e-16 ***
#> Count_Bears              9.41e+01   5.43e+00    17.3   <2e-16 ***
#> Beaufort_Sea            -7.27e-04   2.70e-04    -2.7    0.007 ** 
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 11700 on 66268 degrees of freedom
#>   (191297 observations deleted due to missingness)
#> Multiple R-squared:  0.0133, Adjusted R-squared:  0.0133 
#> F-statistic:  298 on 3 and 66268 DF,  p-value: <2e-16

6 Conclusion

The comprehensive study on the impact of climate change on Arctic ecosystems, particularly focusing on polar bears, reveals significant findings and implications. Our investigation, driven by the urgent need to understand the effects of environmental shifts in the Arctic, combines extensive data analysis with predictive modeling to paint a detailed picture of the current and future state of polar bear populations and their habitats.

Key Findings

  • Declining Polar Bear Population: The data indicates a notable decline in polar bear numbers, particularly in regions like Alaska. This trend is alarming and points to the broader implications of shrinking sea ice and changing climatic conditions in the Arctic. The contrast with the ringed seals’ population trends, which show some resilience, underscores the unique challenges faced by polar bears.

  • Movement Patterns and Habitat Utilization: Our analysis of GPS data and environmental factors such as temperature and sea ice coverage reveals that polar bears’ movement patterns are intricately linked to these variables. The decreasing distances traveled by bears over the years suggest adaptations to the rapidly changing environment. Our predictive models, while indicating some level of accuracy, also highlight the complexity of these movement patterns.

  • Diet and Open Water Days: The relationship between the polar bears’ diet and the time they spend in open water is complex. Our statistical models suggest a connection, albeit not a straightforward one, between these factors. The impact of the melting season on bears’ dietary habits is evident but requires further exploration for conclusive insights.

Implications and Future Directions

The findings underscore the urgency for targeted conservation efforts aimed at preserving polar bear habitats, particularly in light of the diminishing sea ice cover in the Arctic. The insights gleaned from this study can inform policymakers and conservationists in formulating strategies that address the specific needs of polar bears, considering the broader context of climate change. Continued monitoring and research are vital to deepen our understanding of polar bears’ adaptation strategies to the changing environment. Future studies should aim to integrate more variables, such as human activities and their impact on bear habitats.

Concluding Remarks

In conclusion, this study highlights the profound impact of climate change on Arctic ecosystems, particularly on polar bears. The changing patterns in sea ice coverage and temperature regimes are not just altering the physical landscape but are also reshaping the life cycles and survival strategies of these iconic species. Our findings, while offering significant insights, also open the door for further research and action in the realm of wildlife conservation and climate change mitigation.