Animals that turn white in the winter to hide themselves in snowy landscapes could struggle to adapt to climate change, research suggests.
A new study finds that declining winter snowfall near the Arctic could have varying effects on the survival of eight mammal species that undergo a seasonal colour moult from summer brown to winter white each year.
Species most at risk of standing out against the snow include mountain hares, snowshoe hares and short-tailed weasels. Without blending into the background, these animals could find it harder to hunt prey or hide from predators.
However, there are some parts of the northern hemisphere where colour-changing mammals could have a better chance of adapting to climate change, the study finds.
These “rescue” hotspots, which include northern Scotland and parts of North America, should be protected by conservationists to give colour-changing animals the best chance of adapting to future climate change, the lead author tells Carbon Brief.
Visual camouflage is a vital tactic used by both predatory animals, who must hunt while avoiding detection, and their prey, who must hide to avoid being eaten.
But in parts of the northern hemisphere, the changing of the seasons offers a unique challenge to those trying to camouflage with their surroundings.
In winter, when the landscape is snowy and barren, animals with white colouring find it easiest to blend in. However, when spring arrives and snow is replaced with brown soils and growing vegetation, those with mottled brown colouring tend to find it easier to escape detection.
One solution to this problem, used by a range of animals, is to undergo a seasonal molt from brown to white each year.
Scientists have recorded 21 mammal and bird species using this colour-changing tactic, including the Siberian hamster, the collared lemming and the willow ptarmigan.
The new study, published in Science, focuses on how climate change could alter the survival chances of eight of these species.
“All of these species literally live or die by the effectiveness of their camouflage, which evolution has exquisitely crafted to match the average duration of winter snow.”
This mismatch could make some animals, such as hares, more vulnerable to predators, he says. It could also make it harder for predators, such as the arctic fox, to effectively hunt their prey. Mills says:
“One thing to realise is that all 21 of these species, including the carnivores, are prey: Arctic fox get clobbered by golden eagles, weasels are killed by foxes, coyotes, and raptors. For the hares and rodents, tasty snacks for multiple predators, camouflage is everything.”
Arctic fox (Alopex lagopus) in snow, Churchill, Manitoba, Canada, North America. Credit: robertharding / Alamy Stock Photo
Charting colour change
For the study, the researchers first collected coat colour and location data for more than 2,500 live animals and museum specimens spanning 60 countries.
They then analysed this data using modelling to study the moulting behaviour of animals living in different parts of the world.
The researchers found that, within one species, not all individuals will moult in the winter.
The chances of an animal moulting depend on the landscape in which they live. The more snowy the landscape, the higher the chance an animal will turn white in the winter.
This is shown on the chart below, where the number of snow-covered days per year is plotted against the likelihood of turning white in the winter for the Japanese hare (dark blue), the white-tailed jackrabbit (light blue), the least weasel (yellow) and the long-tailed weasel (red).
The probability of a colour-changing mammal having a white winter coat in regions with 0-320 days of snowfall per year. Results are shown for the Japanese hare (dark blue), the white-tailed jackrabbit (light blue), the least weasel (yellow) and the long-tailed weasel (red). Source: Mills et al. (2018)
The chart also includes a “broad polymorphic zone”. A “polymorphic zone” is a term used to describe regions where the probability of having either a brown or white winter coat is close to equal.
In these “zones”, mammals could have the best chance of adapting to declining snowfall conditions, Mills says. That is because, in these areas, a proportion of each species do not turn white in the winter and are therefore more able to blend in with snowless environments.
These brown-coated animals will be more likely to survive winters with less snow cover and pass on their genes to their offspring. Over time, this would increase the proportion of animals with brown winter coats, allowing the population to adapt – and ultimately survive – in environments with less snow.
The maps below show where the polymorphic zone of two or more species overlap. On the charts, red shows where the zones of two species overlap, while brown shows areas where the zones of three species overlap.
Regions in North America (A) and Eurasia (B) with polymorphic zones in winter coat colour for more than two species (red) and more than three species (brown). Source: Mills et al. (2018)
The charts show that parts of the US, Canada and Scotland show the largest overlap.
These regions could be considered “evolutionary rescue zones” where a number of colour-changing mammal species could be able to adapt to declining snowfall, says Mills:
“Because areas with the most [coat colour] variation evolve most quickly, these ‘polymorphic’ zones emerge as hotspots for rapid evolutionary response to climate change. Here in the polymorphic zones the populations are most likely to rapidly evolve towards winter brown, and to disperse the winter brown genes out into the adjacent winter white populations.”
The species whose range most commonly fall into these zones include the arctic fox, the white-tailed jackrabbit and the long-tailed weasel, the research finds. These species may have the best chance of adapting to declining winter snowfall, Mills says, but it is still too soon to tell what their chances of survival could be:
“To really evaluate risk to various species will require a lot more fieldwork and genetic analyses for other species, like we’ve been doing with snowshoe hares. We’re starting to work with weasels and Arctic fox, but I really hope that this paper initiates researchers around the world to start investigating coat colour mismatch.”
Picture of change
The findings should provide “yet another push to policymakers” to reduce the “global carbon footprint”, Mills says:
“I hope that the picture of white animals on brown snowless ground ‘paints a thousand words’ that shows that with continued human-caused climate change and reduction in snow duration, winter white animals on a brown snowless winter background will be in trouble.”
The research also shows that conserving “evolutionary rescue zones” could help wildlife to survive future climate change, Mills says:
“I hope it also helps [policymakers] see that other short-term, yet effective, options are available for protecting wildlife in the face of climate change.”
Polar bears could be failing to hunt enough seals to meet their energy demands, new research suggests.
A study tracking the behaviour of nine female bears from 2014 to 2016 over the Beaufort Sea found that some of the animals exerted so much energy during the hunting season that they lost up to 10% of their body mass in an 8-11 day period.
Polar bears live on a diet made up of ringed seals, which they hunt from the ice surface. However, sea-ice cover in the Arctic is falling at a rate of 14% per decade. This may be forcing some polar bears to travel further in order to find their prey, the authors of the new research say.
Female bears who lose large amounts of weight during the spring hunting season could find it more difficult to raise their cubs to maturity in the following months, the lead author tells Carbon Brief.
However, it is not yet clear how such changes could be affecting the long-term survival of adult polar bears, he adds.
Polar bear with a GPS-equipped video camera collar, lying on the sea ice of the Beaufort sea. Credit: Anthony Pagano, USGS.
Polar bears live across the Arctic and spend the spring and early summer months hunting ringed seals, which provide the animals with a high source of energy and fat.
When autumn arrives, pregnant bears will enter the “denning” season. At this time, females will build themselves a maternity den out of snow, which is where she will give birth to her cubs and nurse them until the following spring.
Though previous studies have looked at the hunting activities of polar bears during the spring months, the new study, which was published in Science, has revealed these habits in striking detail.
The research followed the behaviour of nine female bears living in the Beaufort Sea area for a period of 8-11 days at some point between 2014 and 2016.
To track the bears’ day-to-day activities, the scientists fitted the females with GPS-equipped video-camera collars and accelerometers. They chose to study females because male polar bears’ necks are “larger than their heads” and so they are unable to retain collars, according to the research team.
The video-camera collars allowed the researchers to collect a large amount of data, including how far each bears tends to roam across the ice, how much time they spend walking and swimming and how often they come into contact with other bears.
The camera footage also allowed the scientists to observe the techniques bears use when trying to hunt seals. Most of the time, polar bears catch their prey using the “sit-and-wait” tactic, says lead author Anthony Pagano, a PhD candidate at the United States Geological Survey (USGS) and the University of California, Santa Cruz. He tells Carbon Brief:
“Polar bears walk around until they find a breathing hole that a seal is actively using and they’ll typically stay there, they’ll either sit down or lay down or stand, and they’ll wait at that breathing hole. In some cases, they wait for hours.
“If they detect a seal has come out to breathe, they’ll stand up on their hind legs, raise their bodies up into the air and then pounce through the water as a way to try to stun the seal. If they’re successful, they’ll try to grab the seal around the neck with their jaws and pull them out of the water.”
The video below shows the polar bears in action:
In order to collect this data, the researchers had to capture the bears at the start and end of the study period. Catching the bears required the team to track the animals using a helicopter, Pagano says:
“The easiest way to catch the bears is from a helicopter. It’s the safest method both for the bears and for the biologists. It’s also the best way to try to locate bears as well. Bears occur over a pretty extensive landscape and so locating the bears is a serious challenge.”
The recorded field movements of each bear over the two-year study period are shown on the diagram below, where each colour represents the movements of one bear. On the diagram, the black bear symbol shows where a bear was captured while the white bear symbol shows where they were recaptured.
Diagram showing the field movement of nine female polar bears in the Beaufort Sea area in April from 2014-2016. On the diagram, the black bear symbol shows where a bear was captured while the white bear symbol shows where they were recaptured. Source: Pagano et al. (2018)
The data collected by the researchers suggest that polar bears are more active than previous research has suggested, Pagano says:
“Previous modelling work has tried to guess what a polar bear’s energy expenditure might be and how many seals they might need to capture. They speculated that, because polar bears use the ‘sit-and-wait’ hunting tactic, they would be able to conserve energy.
“What we found in the study is that the activity rates of these bears are very similar to other terrestrial carnivores, despite this sit-and-wait approach to hunting. They are still quite active and they are still travelling long distances.”
In fact, the study finds that polar bears burn energy at a rate that is 1.6 times that of what previous research has suggested.
However, the activity rates of the bears may have been affected by the capturing process, the researchers note in their research paper:
“Admittedly, the activity levels…in the study may be biased low owing to the effects of recovery post-capture. On the basis of movement rate and activity sensor data, recovery post-capture for polar bears may last two to three days.”
Using the new estimate, the researchers predicted that a solitary female bear would need to eat, on average, either one adult seal, three subadult seals or 19 newborn seal pups every 10-12 days to gain enough energy to maintain her current weight.
However, in the study period, more than half of the bears did not eat enough seals to meet their energy needs and subsequently lost body mass. Four bears lost more than 10% of the 8-11 day study period, with an average loss of 1% per day.
Previous research also shows that, in recent years, female bears have been more likely to enter the “denning” season with inadequate fat reserves than in previous years, Pagano says.
The rate of Arctic sea-ice melt over the spring and summer has been increasing in recent decades. Pagano suggests that this could be forcing the bears in the region studied to travel further to find food, and, therefore, be causing them to lose body mass at a faster rate than previously observed. He says:
“In this area, 80-90% of the population is following the ice as it recedes to the north. They’re moving much greater distances than they had historically to follow the ice as it retreats hundreds of kilometres further to the north than it did historically.
“Once the ice returns in the fall and the winter, they’re following the ice back and making a long distance migration back to areas that are thought to have a higher variability of seals.”
Losing weight during the spring months could leave female bears without the adequate resources needed to raise their young in the “denning season”, Pagano says, which could be causing declines in cub survival:
“Some work has shown that they are emerging from their dens in lower body condition and they are not able to locate as much food as they have done historically. Basically, they’re not able to provide for their young at an adequate level and that’s driving declines in cub survival.”
The threats facing polar bears in this region are likely to worsen as climate change continues, he says:
“The concern is that as the ice breaks up earlier each year, the bears will be impacted in three ways: they’ll be less successful at catching seals because they’re being displaced from their primary foraging habitat earlier; they’re putting on less weight than they would have done historically; and then they’re also moving greater differences. If that trend continues, we would expect continued declines in reproductive success.”
However, it is less clear how these changes could impact the survival rates of adult bears, he adds:
“From what we’ve seen so far there doesn’t seem to be large decreases in adult survival, it really seems to be a function of females to be able to produce offspring and successfully raise them.”
Research from the International Union for the Conservation of Nature (IUCN) polar bear specialist group shows that polar bear populations in the southern Beaufort Sea are “likely to decline” in the future. Carbon Brief has previously published an article examining how climate change could affect polar bear population numbers.
Polar bear wearing a video camera collar, hunting for seals on the sea ice of the Beaufort sea. Credit: Anthony Pagano, USGS.
The findings could tell scientists more about how “polar bears are responding to climate change”, says Prof Charlotte Lindqvist, a biologist from the University at Buffalo in New York, who was not involved in the research. She tells Carbon Brief:
“The sample size is small, but what can you do when you sample rare and wild polar bears on the sea ice? If half of the studied bears are already showing signs of energy deficiency on the spring sea ice, we can only imagine a likely gloomy outlook for polar bears as sea ice continues to decline.”
The “major strength” of the research comes from the integration of several different methods to gain greater insight into the behaviour of polar bears, says Prof Andrew Derocher, a polar bear biologist from the University of Alberta, who was not involved in the current study. He told Carbon Brief:
“One challenge with the study findings is that we know that feeding varies widely over space and time with polar bears and the study period used was, by necessity, quite brief. Some of the results could be rather different a few weeks or a month later. However, none of this detracts from the study’s findings, but context is useful.”
The research highlights that the challenges facing polar bears are “complex” and scientists are “yet to fully understand them”, he adds:
“Arctic sea-ice loss is a global issue and there is no quick fix: no protected areas, no habitat modification, or other standard conservation approach could significantly alter the threats facing polar bears in a warming climate. Only the reduction of greenhouse gases can slow the rate of loss of polar bear habitat and improve the conservation outlook for the bears.”
The climate data for 2017 is now in. In this article, Carbon Brief explains why last year proved to be so remarkable across the oceans, atmosphere, cryosphere and surface temperature of the planet.
A number of records for the Earth’s climate were set in 2017:
It was the warmest year on record for ocean heat content, which increased markedly between 2016 and 2017.
It was the second or third warmest year on record for surface temperature – depending on the dataset used – and the warmest year without the influence of an El Niño event.
It saw record lows in sea ice extent and volume in the Arctic both at the beginning and end of the year, though the minimum extent reached in September was only the eighth lowest on record.
It also saw record-low Antarctic sea ice for much of the year, though scientists are still working to determine the role of human activity in the region’s sea ice changes.
Warmest year on record in the oceans
More than 90% of the heat trapped by increasing greenhouse gas concentrations ends up going into the Earth’s oceans. While surface temperatures fluctuate a bit from year to year due to natural variability, ocean heat content increases much more smoothly and is, in many ways, a more reliable indicator of the warming of the Earth, albeit one with a shorter historical record.
The figures below shows ocean heat content for each year in the region of the ocean between the surface and 2,000 meters in depth (comprising the bulk of the world’s oceans), as well as a map of 2017 anomalies.
The upper figure shows changes in ocean heat content since 1958, while the lower map shows ocean heat content in 2017 relative to the average ocean heat content between 1981 and 2010, with red areas showing warmer ocean heat content than over the past few decades and blue areas showing cooler.
Change in global ocean heat content between the surface and 2000 meters of depth from 1958 to 2017 (top) and distribution of ocean heat content anomalies in 2017 (bottom). Figure from Cheng and Zhu (2018), using data from IAP-CAS.
Ocean heat content in 2017 was significantly higher than in 2015, the next warmest year. While 2016 was the warmest year on the surface, it was only the third warmest year for ocean heat content as the El Niño event that helped 2016 surface temperatures be so warm redistributed heat out of the ocean and into the atmosphere.
Warmest surface temperatures without an El Niño
Global surface temperatures in 2017 were the second or third warmest on record since 1850, when global temperatures can first be calculated with reasonable accuracy. Unlike the other warmest years – 2015 and 2016 – there was no El Niño event in 2017 (or in late 2016) contributing to increased temperatures this year (and mild El Niño conditions in early 2017 were offset by mild La Niña conditions during the later part of the year).
The figure below shows global surface temperatures records from the principal research groups around the world since 1970. These are created by combining ship- and buoy-based measurements of ocean sea surface temperatures with temperature readings of the surface air temperature from weather stations on land. Temperatures are shown as anomalies relative to a 1970 to 2000 average. [Click the figure legend to show or hide different temperature records.]
Short-term variability in the record is mostly due to the influence of El Niño and La Niña events, which have a short-term warming or cooling impact on the climate. Other dips, such as the one in the mid-1990s, are associated with large volcanic eruptions. The longer-term warming of the climate is entirely driven by atmospheric increases in CO2 and other greenhouse gases emitted from human activity.
The record warm temperatures experienced over the past three years are not due to any adjustments made to the underlying temperature records. The figure above includes a “raw records” line calculated by Carbon Brief using data not subject to any adjustments or corrections for changes in measurement techniques. Since 1970, the raw data and the adjusted temperature records produced by different groups largely agree.
Global surface temperature records can be calculated back to 1850, though some groups choose to start their records in 1880 when more data was available. Prior to 1850, records exist for some specific regions, but are not sufficiently widespread to calculate global temperatures with any reasonable accuracy. Global temperature records since 1850 are shown in the figure below, again shown as the difference from a baseline of 1970-2000.
Same as prior figure, but with data extending back to 1850 (or as far back as each individual record is available). Chart by Carbon Brief using Highcharts.
Global surface temperatures in 2017 were 1-1.2C warmer than temperatures in late 19th century (between 1880 and 1900), depending on the temperature record chosen.
It is striking how warm 2017 was, despite the end of the massive El Niño event that pushed up 2015 and 2016 temperatures. The past three years are well above any prior years’ temperatures, by a margin of more than 0.15C,
This is shown in the figure below from Berkeley Earth. Each shaded curve represents the annual average temperature for that year, and the further that curve is to the right, the warmer it was.
The width of each year’s curve reflects the uncertainty in the annual temperature values (caused by factors such as changes in measurement techniques and the fact that some parts of the world have more sparse station coverage).
Global average surface temperatures for each year with their respective uncertainties (width of the curves) from Berkeley Earth. Note that warming is shown here relative to the temperature of the 1951-1980 period, but the relative position of the years would be the same using a 1970-2000 baseline. Figure produced by Dr Robert Rohde.
While El Niño and La Niña events have a sizable short-term impact on global temperatures, their influence tends not to extend for more than six months or so after the event has ended. With the large El Niño event of 2015 and 2016 fading by the summer of 2016, it had little direct influence on 2017 temperatures.
In the figure below, Dr Gavin Schmidt, director of the NASA Goddard Institute for Space Studies, uses a simple statistical model to estimate what the global temperature record (black line) would be like in the absence of El Niño or La Niña influences (red line).
Although El Niño bumped up the temperatures of 2015 modestly and 2016 quite a bit, it had almost zero effect on 2017 temperatures. When the influence of El Niño is removed from the record, according to Schmidt’s analysis, 2017 would be the warmest year on record.
Global average surface temperatures from NASA’s GISTemp (black) and with the influence of El Niño and La Niña (collectively referred to as ENSO) removed (red). Figure produced by Dr. Gavin Schmidt.
However, Dr Tim Osborn, director of the Climatic Research Unit at the University of East Anglia, cautions that these results are somewhat sensitive to the statistical method and El Niño index used.
He suggests that, while 2017 is probably the warmest when ENSO is taken out, it is not necessarily as clear a winner over 2016 and 2015 if different methods are used. It is clear, though, that 2017 is the warmest non-El Niño year by any measure.
A paper recently published in Geophysical Research Letters by researchers at the University of Arizona suggests that global temperatures may not return down to pre-2015 levels any time soon. They suggest that extra heat was absorbed by the tropical Pacific Ocean since the late 1990s and that the recent El Niño event acted as a trigger for that heat to be released. The cycle of extra heat uptake by the oceans may be over for at least a decade.
Near-record warmth in satellite records
In addition to surface measurements over the world’s land and oceans, satellite microwave sounding units have been providing estimates of global lower atmospheric temperatures since 1979. These measurements, while subject to some large uncertainties, also show 2017 as a near-record warm year.
The record produced by Remote Sensing Systems (RSS) shows 2017 as the second warmest year after 2016, while the record from the University of Alabama, Huntsville (UAH) shows it as the third warmest after 2016 and 1998. The two records are shown in the figure below – RSS in red and UAH in blue.
Global average lower troposphere temperatures from RSS version 4 (red) and UAH version 6 (blue) relative to a 1979-2000 baseline (as the satellite records begin in 1979). Chart by Carbon Brief using Highcharts.
These satellites measure the temperature of the lower troposphere and capture average temperature changes around 5km above the surface. This region tends to be influenced more strongly by El Niño and La Niña events than the surface and satellite records show correspondingly larger warming or cooling spikes during these events.
This is why, for example, 1998 shows up as one of the warmest years in satellites, but not in surface records.
Observations tracking close to climate modelling projections
Climate models provide projections of both long-term and shorter-term changes to the Earth’s climate. While climate models show their own El Niño- and La Niña-like behaviour, it does not necessarily occur at the same time in models as it does in the real world.
However, temperatures in recent years – both during the El Niño event and, more importantly, now that the El Niño event is over – are tracking rather close to the average projection of the climate models included in the latest report from the Intergovernmental Panel on Climate Change (the CMIP5 models).
These models used historical records of greenhouse gases and other factors through to 2005. Model estimates of temperatures prior to 2005 are a “hindcast” using known past climate influences, while temperatures projected after 2005 are a “forecast” based on a estimate of how things might change.
The figure below shows the range of individual models forecasts between 1970 and 2020 with grey shading, with the average projection across all the models shown in black. Individual observational temperature records are represented by coloured lines.
Annual global average surface temperatures from CMIP5 models and observations between 1970 and 2020. Models use RCP4.5 forcings after 2005. They include sea surface temperatures over oceans and surface air temperatures over land to match what is measured by observations. Anomalies plotted with respect to a 1970-2000 baseline. Chart by Carbon Brief using Highcharts.
While global temperatures were running a bit below climate models between 2005 and 2014, the last few years have been pretty close to the model average.
Low sea ice at both poles
In addition to near-record temperatures, 2017 also saw record-low sea ice during parts of the year, both in the Arctic and Antarctic.
The figure below shows the average Arctic sea ice extent for each week of the year for every year between 1978 and 2017. Prior to 1978, satellite measurements of sea ice extent are not available and the data is much less reliable.
The figure shows a clear and steady decline in Arctic sea ice since the late 1970s, with lighter darker colours (earlier years) at the top and lighter colors (more recent years) much lower. A typical summer now has nearly half as much sea ice in the Arctic as it had in the 1970s and 1980s.
Sea ice extent only provides part of the picture, as some sea ice is much thicker or older than others. The Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) project provides estimates of sea ice volume since 1979, shown in the figure below.
Arctic sea ice volume anomalies from 1979 through 2017 from PIOMAS.
According to PIOMAS, sea ice volume was around 12,000 cubic kilometers lower than in 1979. They found that 2017 tied 2012 for the lowest measured Arctic sea ice volume on record, though 2012 remains the year with the lowest summer minimum volume.
While the long-term decline in Arctic sea ice is clear, the Antarctic is much more complicated. Weekly Antarctic sea ice extent from 1978 through to 2017 is shown in the figure below.
Unlike in the Arctic, the Antarctic has no clear long-term trend in sea ice extent. In the figure early years (darker lines) and recent years (lighter lines) are intermixed. In fact, 2015 and early 2016 set records for the most sea ice extent observed.
In 2017, however, Antarctic sea ice hit record lows for much of the year. Even in recent months it has been the second lowest recorded after late 2016. It is unclear what role, if any, climate change is playing in Antarctic sea ice changes, though it is an area of very active research.
Finally, both Antarctic and Arctic sea ice extent is combined to estimate global sea ice extent in the figure below.
Global sea ice set a clear record low in the first half of 2017, driven in large part by record low Antarctic sea ice cover. There has been a long-term downward trend in summer global sea ice extent, though the trend is less clear in the winter, reflecting the fact that the Arctic shows a clearer long-term trend than the Antarctic.
Carbon Brief produced a raw global temperature record using using unadjusted ICOADS sea surface temperature measurements gridded by the UK Hadley Centre and raw land temperature measurements assembled by NOAA in version 4 of the Global Historical Climatological Network (GHCN).
Raw land temperatures were calculated by assigning each station to a 5×5 latitude/longitude grid box, converting station temperatures into anomalies relative to a 1971-2000 baseline period, averaging all the anomalies within each grid box for each month, and averaging all grid boxes for each month weighted by the land area within each grid box.
Raw combined land/ocean temperatures were estimated by averaging raw land and ocean temperatures weighted by the percent of the globe covered by each.