What I Learned From 5 Weeks of Science Communication

Anna here –

As an undergrad wrapping up my first year of college this past spring, I remember sitting in my dorm room with a thermos of hot tea, scanning website after website, asking myself what I was going to do with my summer. At the time, I was about halfway through my first-ever geology class, which had sent me on an earth and climate science kick that inspired most of my searches. Eventually, my professor sent me a link to the TimeScavengers website and internship information page. It seemed like a perfect opportunity – something that would allow me to geek out about science from the comfort of my own home, where I could still spend time with my friends and family. I decided to apply.

Naively, I assumed the internship would be a breeze. Looking back, I’m ashamed of how smug I felt about it – I had grown up hearing people telling me that I was a good writer, and that I was a good scientist, so I imagined that it wouldn’t be that hard to combine the two. Within the first week, I quickly found out I was mistaken. It turned out that there’s likely a reason most scientists aren’t writers, and vice versa: because it is hard. 

For me, the biggest challenge was the time and effort it took to dissect each article to a level where I could rewrite it for others. I remember multiple occasions when I put my highlighter away, thinking I fully understood an article, only to sit in front of an empty Google Doc and realize I had to go back and reread an entire section. I discovered there was a huge difference between understanding something in my brain and putting it in words. (This, of course, was shortly followed by the realization that the understanding locked in my brain was probably not all that complete to begin with). Point being, there’s another layer of insight that comes with trying to explain science, and, as painful as that layer might be to reach, it will definitely be beneficial in the long run.

While nothing about the internship proved impossible, it certainly challenged me in ways I didn’t expect. However, I was also struck by how much easier these processes became over time. In one of my first articles, I remember essentially skipping over a methods section that had too many big, scientific-looking words. The task of sorting through all of them, looking them up, rereading and rewriting seemed too daunting, and my mentors, Sam and Alex, had to explain the whole thing to me. On a more recent article, however, I was able to plow through an equally challenging methods section on my own. I sprawled out at a table at a library nearby, a printed out and highlighted article in front of me, with a notebook on one side and my laptop to look up words with on the other side. It still took quite a while, but it was satisfying in the end to see the improvements I had made over the course of the internship.

In the end, I don’t think my time with TimeScavengers has changed the path I hope to take as a scientist. If anything, the hours reading articles made me realize how much I itched to be out in the field doing my own research, rather than pouring over someone else’s. However, this internship definitely changed my perspective on science communication going forward. It seems to me that anyone who seeks the fancy title of “scientist” should also seek the title of “science communicator.” After all, earth-shattering research is worth nothing if only the researcher themself knows about it – they must be able to convey their findings to everyone else in order for it to make an impact. I also hope to make accessibility a priority in any research that I do in the future, so that aspiring scientists feel encouraged, rather than intimidated, when reading my findings.

Birds are more Vulnerable to Climate Change Impacts than Small Mammals in the Mojave Desert

Exposure to climate change drives stability or collapse of desert mammal and bird populations

E.A. Riddell, K.J. Iknayan, L. Hargrove, S. Tremor, J.L. Patton, R. Ramirez, B.O. Wolf, S.R. Beissinger

Summarized by Anna Geldert

What data were used? Researchers compared climate change responses in desert species, including 34 small mammal species and 135 bird species. Surveys were conducted at 151 sites throughout the Mojave Desert, concentrated mostly in Death Valley National Park, Mojave National Preserve, and Joshua Tree National Park (California, USA). Modern observations were compared to historical observations by Joseph Grinnell and colleagues in the early 20th century to assess change over time.

Methods: The authors used a dynamic multi-species occupancy model to determine how the proportion of sites that a species occupied changed over time. In summary, this approach assessed the probability of detecting a species  at different time periods, and used this data to determine the change in occupancy (likelihood of a species occupying a site), change in species richness (number of species at a site), colonization probability (likelihood of expanding to new sites), and persistence (long-term survival of a species at a site) probability. This model also factored in the impacts of climate change and habitat loss. The authors also estimated the degree of exposure (or how greatly an organism is affected by climatic changes) in small mammals and birds by simulating the “cooling costs” of each species. Cooling costs represent the water required for evaporative cooling to maintain a stable body temperature and were based on the species’ behavior, morphology, and microhabitat conditions.

Results: Overall, modern bird species declined in occupancy when compared to historical records, while small mammal occupancy remained relatively consistent. The model estimated that the occupancy of 29% of bird species decreased, 70% were unchanged, and only 1% increased. Meanwhile, only 9% of small mammals saw an occupancy decrease, while 79% stayed constant and 12% increased. Similarly, bird species richness decreased at 90.1% of sites and only 3.3% of sites for small mammals. The authors also found that bird populations experienced higher exposure to climate change than small mammals. The exposure model estimated that cooling costs were approximately 3.3 times higher in birds than they were in mammals, with this number projected to increase to 3.8 times by 2080. Finally, the level of adaptation and specialization among species of both groups had little influence on changes in cooling costs, suggesting that microhabitat conditions and their behavioral ability to “buffer” against climatic changes had a much greater impact.

The figure shows a histogram graph, labeled ‘B’, which represents the change in species occupancy over time for both birds and small mammals. The x-axis is labeled “change in occupancy,” and ranges from -0.6 to 0.4, increasing by a factor of 0.5. Two y-axises appear stacked vertically on top of one another so that data on birds and small mammals can be graphed separately; both are labeled “number of species.” On the top right corner of each graph is the black silhouette of a bird on the top graph, and a small rodent on the bottom graph. The top axis, which shows data for birds, ranges from 0 to 30. Gray bars (roughly 70% of total) represent no significant change in occupancy compared to historical records, while red bars (roughly 30%) represent significant increases and decreases. Occupancy bars for birds are concentrated left of zero, indicating an overall decrease in species occupancy. The number of species is highest for changes in occupancy of -0.1 and -0.05, which each have about 25 species. As change in occupancy continues to decrease, the number of species slopes off rapidly, with only 5 species or less for occupancies lower than -0.35. Only 3 bird species have a positive change in occupancy, with probability values at 0.1, 0.15, and 0.4. The bottom y-axis ranges from 0 to 15, and represents data for small mammals. Gray bars (roughly 80% of total) again represent no significant change, while blue bars represent significant increases or decreases. Change in occupancy for small mammals is much less skewed than occupancy for birds. The change in occupancy of 0 has the highest number of species, at roughly 15. All other occupancies have 7 species or less, and quickly decrease to zero on either side by -0.2 and 0.3 change in occupancy. Small mammals, therefore, have a much lower range in change of occupancy probability than birds. Occupancy probabilities are also much more similar to historical records for small mammals than for birds.
Fig. 1 Change in occupancy (modern – historical) of bird and small mammal species in the Mojave desert. Changes in occupancy were estimated using a dynamic multi-species occupancy model based on survey data collected during two different time periods: first, by Joseph Grinnell and colleagues in the early 20th century (historical), and second, by the authors of this paper in 2007-2018 (modern). The gray bars represent the number of species with no significant change in occupancy between modern and historical records, while colored bars (red for birds; blue for small mammals) indicate significant increases or decreases over time.

Why is this study important? This study counters the traditional approach of assessing impacts from climate change, which often assumes that exposure within an ecosystem is uniform across all species. This study revealed that in the same locations birds were more severely impacted by climate change than small mammals, as shown by the lower occupancy probability, lower species richness, and higher cooling costs in birds. Additionally, this study highlighted the importance of microhabitat buffering potential, which may be a driving factor as to why small mammals were sheltered in their burrows during the day  from the worst of the impacts of heat, while birds were not.

The big picture: As the impacts of climate change on animal populations progress, desert communities remain especially vulnerable. In order to minimize these impacts, it is important to understand how ecosystems respond to climate changes. This study suggests that impacts should be considered at the population level, rather than the community level, as species responses varied greatly even within the same ecosystem. Furthermore, the results suggest that microhabitat buffering is especially important in reducing impacts from climate change, and should be given greater attention in conservation efforts and future studies.

Citation: Riddell, E. A., Iknayan, K. J., Hargrove, L., Tremor, S., Patton, J. L., Ramirez, R., … Beissinger, S. R. (2021). Exposure to climate change drives stability or collapse of desert mammal and bird communities. Science, 371(6529), 633–636. https://doi.org/10.1126/science.abd4605

Impacts From Climate Change and Other Threats Increase for At-Risk Canadian Wildlife

Increasing importance of climate change and other threats to at-risk species in Canada

Catherine Woo-Durand, Jean-Michel Matte, Grace Cuddihy, Chloe L. McGourdji, Oscar Venter and James W.A. Grant

Summarized by Anna Geldert

What data were used? In this study, researchers assessed threats to biodiversity in Canada. They drew upon the methods of a previous study by Venter et al. (2006), which recognized six primary threats to biodiversity in Canada: habitat loss, introduced (non-native) species, over-exploitation (i.e., excessive hunting or harvest), pollution, native species interactions, and natural causes. They also assessed the threat of climate change. In total, researchers assessed threats to 820 species from 12 taxa, including: vascular plants (e.g., trees, flowering plants, ferns, clubmosses, etc), freshwater fishes, marine fishes, marine mammals, terrestrial mammals, birds, reptiles, molluscs, amphibians, arthropods, mosses, and lichens. All of these species were classified as at-risk (in decreasing severity: extinct, extirpated, endangered, threatened, or of “special concern”) by COSEWIC (Committee on the Status of Endangered Wildlife in Canada).

Methods: Between October 2018 and September 2019, researchers examined the COSEWIC website for evidence of Venter et al.’s six primary threats, where threatened species and the reasons they are threatened are cataloged . They looked at COSEWIC’s “Reason for Designation” statement, as well as details from the Assessment and Status Report. Any mention of any of the six major threats was recorded, so that multiple threats could be identified for each species. This data was compared to data from Venter et al. (2006) to determine changes in prevalence over time. Additionally, researchers noted mentions of climate change threats to species on the COSEWIC website. Climate change threats were classified as current, probable, or future based on a list of keywords. All seven of the biodiversity threats were assessed over time by comparing their prevalence to species with multiple COSEWIC status reports, including a total of 188 species.

Results: 814 of the 820 species studied were impacted by at least one of the six primary threats to biodiversity. Habitat degradation was the most significant threat, affecting 81.8% of species, followed by natural causes (51.0%), over-exploitation (46.9%), introduced species (46.4%), pollution (35.1%) and native species dynamics (27.2%). This represented an overall increase in threats compared to Venter et al., though introduced species and natural causes were the only threats that increased with statistical significance. Climate change impacted a total of 37.7% of species, with 13.3% of species impacted by current climate change, and 14.7% and 9.7% that will likely be impacted by probable and future climate change, respectively.

The figure shows a bar graph comparing the prevalence of the primary threats to biodiversity in the modern 2018 study and the 2005 Venter et al. study. In the top right corner, a legend indicates that white bars represent data from 2005, which included 488 species total, and black bars represent data from 2018, which included 814 species total. The x-axis shows the biodiversity threats, including habitat loss, introduced species, over-exploitation, pollution, native species interactions, natural causes, and current climate change. For each threat category, a pair of historical and modern bars are shown, with the exception of current climate change, which only has a bar for 2018. The y-axis is labeled “percentage of at-risk species,” and ranges from 0 to 90, increasing at increments of 10. For modern data, habitat loss is the most prevalent threat, affecting 81.8% of species, followed by natural causes, over-exploitation and introduced species, which all affected roughly 45-50% of species. Pollution and native species interactions (affecting 35.1% and 27.2% of species respectively) were moderate threats, while climate change was the lowest, affecting only 13.3%. For the 2005 Venter et al. data, habitat loss was also the most significant threat and was slightly more prevalent than it is today, affecting 83.8% of species. Native species interactions were also slightly higher in the 2005 study than the 2018 study, though not enough to be significant. All other threats were higher in the modern study, though introduced species and natural causes were the only categories that increased with statistical significance.
Fig 1. Percentage of at-risk species in Canada that were impacted by the six primary threats to biodiversity, comparing modern data from December 2018 and data recorded by Venter et al. in June 2005. The modern threat of climate change is also included, though there is no corresponding 2005 record. N represents the number of species (n=488 in 2005, n=814 in 2018).

The analysis comparing threats to species with multiple COSEWIC status reports found an average increase from 2.5 to 3.5 threats per species in newer reports. The prevalence of many threats also increased significantly over time, including a 27.6% increase in introduced species, a 13.3% increase in over-exploitation, and a 10.1% increase in pollution. Mentions of the threat of climate change also increased from 11.7% in the oldest reports to 49.5% in the newest reports.

Why is this study important? This study reveals that threats to biodiversity continue to increase today, despite protections that have been put in place. In particular, the threat of introduced species has increased significantly in recent years, reflecting rises in globalization and human-environmental interactions. Overall, researchers were surprised by the relatively low percentage of species currently impacted by climate change (13.3%), as this topic has gained so much global attention. The authors suggested the unexplained increase in death by natural causes compared to the Venter et al. report may actually account for impacts from climate change, as climate change has increased the severity of storms, droughts, and other weather events worldwide.

The big picture: This study emphasizes the importance of wildlife conservation, in Canada and all over the world. On-going threats such as habitat loss, pollution and overexploitation continue to impact hundreds of species in Canada, so it is likely that stricter protections are needed to enact effective change. Additionally, this study indicates that climate change is among the most significant threats to biodiversity and is projected to continue increasing in prevalence in the future. Although it was not considered to be one of the six primary threats by Venter et al. in 2005, it should definitely be recognized as one today.

Citation: Woo-Durand, C., Matte, J.-M., Cuddihy, G., McGourdji, C. L., Venter, O., & Grant, J. W. A. (2020). Increasing importance of climate change and other threats to at-risk species in Canada. Environmental Reviews, 28(4), 449–456. https://doi.org/10.1139/er-2020-0032

From Lynx to Coyotes: How Climate Change Has Impacted Hare Predation

Climate change increases predation risk for a keystone species of the boreal forest

By: Michael J.L. Peers, Yasmine N. Majchrzak, Allyson K. Menzies, Emily K. Studd, Guillaume Bastille-Rousseau, Rudy Boonstra, Murray Humphries, Thomas S. Jung, Alice J. Kenney, Charles J. Krebs, Dennis L. Murray, and Stan Boutin

Summarized by: Anna Geldert

What data were used? Researchers observed 321 snowshoe hares in southwestern Yukon from 2015-2018. Researchers also monitored changes in weather and snow conditions within the study region, including temperature, snow depth, snow hardness and daily snowfall.

Methods: Hares were captured in live traps and given collars with mortality sensors before being released back into the wild. In the event of hare death, researchers visited the site to identify any predators responsible for the death by looking for tracks, scat, and other indicators in the surrounding area. Researchers recorded data on weather and snow conditions at three different sites throughout the study region on a nearly daily basis, as well as at each kill site. They then used a computer model to compare the likelihood of hare death under different weather conditions (e.g., temperature, snow depth, and snow hardness), and generated a best fit line to model these relationships. Similar models compared weather conditions to hare predation from lynx and coyote, hare death by age group, and hare foraging time by age group. The models were tested by inputting randomized data and estimating uncertainty.

Results: Researchers found that 153 hares died of predation. Lynx and coyote were the most common predators, accounting for 59.4% and 25.5% of deaths respectively. Hare survival was lowest in 2015-2016, countering the predicted increase in hare populations based on predator-prey cycles. Low survival rates were correlated with shallow snow depth and high snow hardness. . The relationships between hare survival and these weather conditions are most likely due to changes in predator threats, not changes in foraging behavior. While lynx predation remained relatively constant across a wide range of snow conditions, coyote predation increased by a factor of 1.155 with higher snow depth and 1.244 with lower snow hardness.

The figure graphs the relationship between snow depth and hare predation risk by lynx and coyotes. The x-axis is labeled “snow depth (cm),” and ranges from 20 to 70, increasing at intervals of 10. The y-axis is labeled “risk (relative to baseline),” and ranges from 0 to 15, increasing at intervals of 5. A legend indicates that the purple line represents risk from lynx while the red line represents risk from coyotes. At a risk measurement of 1, a dotted line runs horizontally (slope=0) across the graph; this represents baseline risk. The risk from lynx almost exactly coincides with the baseline risk, indicating that snow depth has little impact. On the other hand, the risk for coyote has an inverse relationship with snow depth. At a snow depth of 20 centimeters (the lowest depth represented), risk from coyotes is approximately 10. The risk line then decreases exponentially, crossing the baseline risk at approximately 35 centimeters and plateauing close to a risk of zero around 50 centimeters.
Fig. 1. Hare predation risk by lynx and coyotes at different snow depths. The dotted line represents a baseline risk, while shaded regions represent standard errors.

Why is this study important? This study is an important example of the cascading effects that climate change can have on ecosystems in the boreal forest. Increasing temperatures due to climate change have altered traditional snow conditions in the Yukon, leading to lower snow depth and snow hardness in recent years. Coyotes – who, unlike lynx, are not well adapted to harsh winters – have gained a relative advantage in these milder conditions, leading to increased hare predation. Risk has increased so much, in fact, that they disrupted the natural rise and fall of hare populations due to existing predator-prey cycles. If these trends continue, they could potentially impact other aspects of boreal forest ecosystems.

The big picture: It is widely recognized that climate change threatens the survival of many species and ecosystems around the globe. However, this is most often talked about in terms of direct threats, such as increasing temperature, increasing severe weather conditions, etc. This article demonstrates that a further concern, particularly in boreal forests, is the impact of changing climatic conditions on food webs and predation threats. Further research is needed to determine if the changing predator-prey relationships between hares and coyotes in this study are consistent in other regions of boreal forest, and whether similar trends are reflected in other biomes as well.

Citation: Peers, M. J. L., Majchrzak, Y. N., Menzies, A. K., Studd, E. K., Bastille-Rousseau, G., Boonstra, R., … Boutin, S. (2020). Climate change increases predation risk for a keystone species of the boreal forest. Nature Climate Change, 10(12), 1149–1153. https://doi.org/10.1038/s41558-020-00908-4

Horseshoe Crabs Teach Us About Heterochrony

A new method for quantifying heterochrony in evolutionary lineages

James C. Lamsdell

Summarized by Anna Geldert

What data were used? A total of 20 traits that display heterochronic conditions for 54 species of horseshoe crabs (both living and extinct) were studied. 256 traits were examined and documented in these horseshoe crabs and 99 related species to make a character matrix. Of the 54 horseshoe crabs, environmental data from previous studies was also collected to determine the species’ habitat..

Methods: This paper presents a new method for quantifying heterochrony through a process called “heterochronic weighting.” Heterochrony is a process that alters the timing and length of developmental stages of organisms, and is characterized as either paedomorphism (retaining juvenile characteristics as an adult) or peramorphism (developing beyond what is seen in related species; more “adult-like”.) For each characteristic, paedomorphic traits were assigned a score of -1, peramorphic traits were assigned a score of +1, and neutral characteristics were assigned a score of 0. The heterochronic weighting of a species was then defined as the sum of all scores divided by the number of characteristics. The author also looked at heterochrony in an evolutionary context. He generated a probable evolutionary tree using a computer model that related species based on shared traits. He then used the tree to determine the heterochronic weighting of the clade (i.e., evolutionary group) by averaging those of the individual species. The differences in heterochronic wightings between habitat preferences (marine or nonmarine) and clades were tested for statistical significance. Lastly, the author tested to see if there were concerted trends towards paedomorphy or peramorphy in each clade.  The evolutionary tree was also tested to determine the most likely habitat for ancestry species of horseshoe crabs, which gave insight to when shifts from marine to nonmarine environments occurred.

The figure shows a diagram of the heterochronic conditions as seen in limb length. Three drawings of the underside of the head shield of a horseshoe crabs are shown side by side. There is one small pair of claw-like appendages towards the front of the head shield and ten longer walking limbs visible. The first diagram has the longest limbs, extending outside the shell. It is labeled “-1,” representing a paedomorphic condition. The second diagram, labeled “0” for a neutral condition, has shorter limbs that are all contained under the shell, though some extend nearly to the edge. Lastly, the final diagram is labeled “+1” for a peramorphic condition. The limbs of the crab in this diagram are the shortest, spanning only a third to a half the width of the shell.
Fig 1. Variations in limb length serve as an example of a heterochronic characteristic in horseshoe crabs. Paedomorphic (-1), neutral (0), and peramorphic (+1) conditions are shown.

Results: Overall, heterochronic weighting proved successful in quantifying the paedomorphic and peramorphic changes in horseshoe crab characteristics. Of the four clades studied, two (Bellinurina and Austrolimulidae) were found to have statistically significant occurrences of heterochrony, with Bellinurina trending towards paedomorphic characteristics and Austrolimulidae trending towards peramorphic characteristics. The Paleolimulidae clade was characterized as having non-significant  heterochronic weightings, while the Limulidae showed a slight peramorphic trend that could be explained by random evolution, not necessarily a concerted trend. More extreme heterochronic weightings (both positive and negative) were associated with the evolutionary transition to non-marine habitats, as was the case for both Bellinurina and Austrolimulidae clades.

Why is this study important? First and foremost, this study is important because it developed a method for quantifying instances of heterochrony, which has not been studied in a combined phylogenetic and ecological context. This gives insight into the interaction between ecology and heterochrony, especially as an evolutionary mechanism. For example, it is noteworthy that both clades that transitioned to non-ancestral nonmarine environments (Bellinurina and Austrolimulidae) experienced higher rates of heterochrony, suggesting that greater ecological change may correlate with increased likelihood for developmental changes in horseshoe crabs. However, it is also important to recognize that environmental affinity is not the only factor influencing heterochrony, or else Bellinurina and Austrolimulidae would have developed in the same way, trending towards either paedomorphic or peramorphic characteristics. The opposite trajectories of the two clades suggests that environmental pressures may increase heterochrony, but underlying genetic factors determine the direction of development.

The big picture: The process of heterochronic weighting developed in this study has the potential to advance the field of paleobiology, as the author was now able to quantify paedomorphy and peramorphy throughout evolutionary history. This allows for a deeper understanding of the relationship between an evolutionary mechanism and other factors, such as ecological affinity or evolutionary relatedness. However, as this study is so far the only study to have employed heterochronic weighting so far, the success rate of this process is limited to horseshoe crabs. Therefore, further research is needed to determine the effectiveness of this method for heterochrony in other species groups.

Citation: Lamsdell, J. C. (2020). A new method for quantifying heterochrony in evolutionary lineages. Paleobiology, 47(2), 363–384. https://doi.org/10.1017/pab.2020.17

Trees Combat Climate Change in China by Reducing CO2 Levels

Forest management in southern China generates short term extensive carbon sequestration

By: Xiaowei Tong, Martin Brandt, Yuemin Yue, Philippe Ciais, Martin Rudbeck Jepsen, Josep Penuelas, Jean-Pierre Wigneron, Xiangming Xiao, Xiao-Peng Song, Stephanie Horion, Kjeld Rasmussen, Sassan Saatchi, Lei Fan, Kelin Wang, Bing Zhang, Zhengchao Chen, Yuhang Wang, Xiaojun Li and Rasmus Fensholt

Summarized by Anna Geldert

What data were used? Researchers collected data on carbon storage (long-term carbon stocks) and sequestration levels (new uptake of carbon gasses) by forest type. Data was recorded between 2002 and 2017, and the area of study focused on eight provinces in southern China. This data was compared with existing published data on soil moisture levels and national CO2 emissions.

Methods: Researchers used satellite imagery data from MODIS (Moderate Resolution Imaging Spectroradiometer) for the basis of this study. Using approximately 10,000 MODIS images, they divided the area into a grid with a scale of 0.25km2. Using “training points” of known land cover, they trained a computer to estimate the probability of forest cover in each grid cell, as well as the level of change in forest cover over time. Based on this information, grid cells were classified into eight categories of forest types, including dense forest (probability of forest cover ≥ 0.8, with low change), persistent forest (probability ≥ 0.5, with low change), persistent non-forest (probability ≤ 0.5, with low change), recovery (regrowth of deforested areas, causing a gradual shift from non-forest to forest), afforestation (tree plantation in previously unforested areas, causing a rapid shift from non-forest to forests), deforestation (shift from forest to non-forest), rotation (harvested area, causing fluctuation between forest and non-forest) and rotationL (harvested area, causing fluctuations and low forest recovery). Researchers then estimated the carbon density of each forest type using data from a previous study 2015 from GLAS (Geoscience Laser Altimeter System, i.e., a satellite machine designed to measure the vertical structure of forests). MODIS data from this study was cross-referenced with existing passive microwave data from SMOS (soil moisture and ocean salinity), which also measured carbon density from this region, though on a broader scale. SMOS data were also used to determine the average soil moisture in the studied region.

Results: Both tree cover and fossil fuel emissions increased considerably between 2002 and 2017. Using the MODIS data, researchers estimates a carbon sink of 0.11 Pg C year-1 (i.e., petagrams of carbon per year, the equivalent of 0.11 billion metric tons per year) in the region studied. This accounted for roughly 33% of yearly carbon emissions since the year 2012. Unmanaged dense forest had the highest carbon density, accounting for 20.5% of carbon storage despite only occupying 8.8% of the land. However, dense forests had low levels of carbon sequestration, accounting for only 4% of the total uptake. Comparatively, persistent non-forests and managed forests (recovering, afforestation, and rotation areas) all had low levels of carbon storage but accounted for 65% the total carbon sequestration. Persistent forest areas lay somewhere in the middle, with moderate storage and sequestration levels. Heavily harvested forests (deforested and rotationL areas) had much lower sequestration rates, and served as carbon sources rather than sinks. Finally, SMOS data revealed that soil moisture levels tended to be lower in regions with lots of managed forests.

The figure shows a bar graph comparing the type of land to the level of sequestration of CO2 emissions. The x-axis is labeled “Type of land use,” and is numbered 1 through 8. A legend on the right of the graph indicates that each number corresponds to a type of forest or non-forest area: 1 represents dense forest, 2 represents forest, 3 is non-forest, 4 is recovery, 5 is afforestation, 6 is deforestation, 7 is rotation, and 8 in rotationL. A different y-axis is present on either side of the graph, so that both the relative percent of CO2 emissions sequestered and the numerical quantity of carbon sequestered per year are represented. The left axis represents the percentages, and spans from 0 to 7.5, increasing at increments of 1.25. The right axis represents the quantity of carbon sequestered in petagrams per year, spanning from 0.00 to 0.03 and increasing by a factor of 0.05. A separate legend on the bottom of the graph indicates that the dark orange portions of the bars represent the percentage/fraction of carbon sequestered compared to CO2 emissions from China as a whole, while light orange portions correspond to emissions from the eight provinces alone. The land use type with the highest percentage of carbon sequestered was non-forest, which accounted for approximately 6.5% of annual emissions for the eight provinces, or 0.026 total Pg of carbon per year. Non-forest was followed by forest and afforestation (both accounting for 5.2% of emissions and 0.21 total Pg), recovery (4% and 0.016 Pg), rotation (3.5% and 0.013 Pg) and dense forest (1% and 0.009 Pg). Deforestation and rotationL were the only types of land use to represent a negative percentage and quantity of carbon sequestered, indicating that they served as a carbon source rather than a carbon sink. Deforestation accounted for approximately -0.2% (or 0.001 Pg) of carbon sequestration, while rotationL was nearly negligible. The percentages of carbon sequestered when compared to national emissions (dark orange) were all about one fifth of the percentages when compared to the eight provinces alone.
Fig. 1. Average percent of CO2 emissions sequestered annually by each forest type. CO2 emissions from the eight provinces in the study region, as well as emissions from China as a whole, are both shown.

Why is this study important? This study compares the effectiveness of different types of forests in mitigating the impacts of climate change. While natural, dense forests were the best at storing carbon long-term, managed forests were most effective at rapidly removing CO2 from the atmosphere on a shorter timescale. Harvested forests, especially those classified as “rotation,” were especially successful as they were able to sequester relatively high levels of carbon while still providing significant economic revenue from timber for the region. Overall, changes in forest management policies in China in 2002 led to an impressive reduction in carbon emission levels (33%). However, it is important to note that an additional 3 million km2 of forested land would be needed to reach net zero carbon emissions, a number which is unreachable in this region. Likewise, reduced levels of soil moisture indicate that heavily managed forests may not be sustainable in the long run, and will likely be less effective during periods of drought. More research is needed to determine if these forest management policies have already reached maximum effectiveness, or if other adjustments can be made to further increase sequestration.

The big picture: As the main drivers of climate change, fossil fuel emissions continue to threaten our planet. Forestation and forest management policies, such as those established in China at the turn of the century, are a way to mitigate the impact of greenhouse gasses. Modeling future policies after these could help increase carbon sequestration worldwide, especially until renewable energy becomes available. However, as was revealed in this study, it is nearly impossible at current emission levels to reach net zero carbon emissions through forest management alone; in the long term, forest management will likely need to be combined with other policies to ensure a sustainable future.

Citation: Tong, X., Brandt, M., Yue, Y., Ciais, P., Rudbeck Jepsen, M., Penuelas, J., … Fensholt, R. (2020). Forest management in southern China generates short term extensive carbon sequestration. Nature Communications, 11(1). https://doi.org/10.1038/s41467-019-13798-8

How Mushrooms Could Help Clean Up Pollution

Mycoremediation of heavy metals: processes, mechanism and affecting factors

Vinay Kumar and Shiv Kumar Dwivedi

Summarized by Anna Geldert

What data were used? In this review, researchers assessed data from over 300 previous studies on mycoremediation, a process which uses fungi to remove pollutants such as heavy metals from the environment. These studies included findings on the mycoremediation potential of 62 living species of fungi, and 21 dead species. In total, the review considered 11 types of heavy metal pollutants (mercury, cadmium, lead, chromium, copper, arsenic, manganese, nickel, cobalt, zinc and iron) as well as data on drinking water standards, and health impacts of each heavy metal from the World Health Organization (WHO).

Methods: The goal of this review was to synthesize data from existing research, and to identify which factors most affect fungi mycoremediation potential. The authors looked for trends and patterns from previous studies, and summarized findings related to the health impacts of heavy metal exposure to fungal species, as well as the biological, chemical, and physical processes that are used for the absorption of pollutants. They also identified the most important factors affecting the rate of absorption for both living fungi and dead fungal biomass. 

Results: In general, results demonstrate that both heavy metal tolerance and absorption potential differs greatly among species of fungi. Species belonging to the class ascomycete were found to tolerate higher concentrations of heavy metal pollutants, though the explanation for this is still unclear. Both living and dead fungal biomasses were able to absorb heavy metals through a variety of biological processes in the cell wall, and this absorption may be increased further through physical and chemical treatments. In regard to factors that impact absorption rate, the review found that lower pH levels, high agitation (water disturbance) rates, and low flow rates all consistently increased the absorption rate of tested fungi. Factors such as temperature, time, and heavy metal concentration varied based on the species of fungi. Lastly, this study concludes that dead fungal biomass will most likely work better than living fungi for mycoremediation, since varying pH levels, temperatures, and heavy metal concentrations are not limiting factors as dead fungal masses do not need to be kept alive.

A flow chart which starts at a light pink box titled Mycoremediation of heavy metals, which has two main paths, represented by thin black arrows. From this point of origin on the left is a gray box titled By Growing Fungi. This continues to a pink box on the left titled Genetically modified and across from it on the right is a gray box titled Non-Modified. Non-modified continues the chart on its downward path to two more boxes. The left hand box is light pink labeled Indirect Application which below it in parenthese states “By production of siderophores and secondary metabolites”. Across from the light pink box is a gray one with the label Direct Application. Both Direct and Indirect Application continue the chart downward to two light blue boxes with the titles Specific Action of a single fungus and Synergistic Action of more than one fungi, on the left and right respectively. The chart continues from these two, and again are two boxes both in an orange color. They are titled Immobilized form and Free form. Finally this side of the chart ends with two purple boxes labeled Continuous mode and Batch mode. Returning to the beginning of the chart but on the right side, is a blueish gray box titled By Fungal Biomass. This branches into a light blue box on the left titled Activation and across from it, in the same previous blueish gray color is a box labeled Direct Application. From Activation on the left the chart splits into 3 boxes. On the left is a light pink box titled Physical Activation with parentheses stating “heat, magnetic modified, etc”. In the center is a pink box titled Chemical Activation with parentheses stating “acetone, NaOH, ether, etc”. On the right is a light blue box titled Physico-chemical Activation. All 3 boxes continue the chart to a blue box titled Characterization and Application. This light blue box continues to a final box within the chart, and from the left-hand side the chart converges onto this box. This large gray box titled Factors involve in HMS Remediation process. Below it is a list that states Time, pH of the soln, Temperature, Adsorbent conc, Adsorbent dose, Aggitation rate, Medium composition, and Adsorbent type in descending order.
Fig. 1 Flowchart of mycoremediation in wastewater heavy metal treatment methods, comparing the growth of fungi and fungal biomass.

Why is this study important? This study is useful because it draws conclusions from a large body of existing work on mycoremediation, and recognizes important trends in related findings. This allows for comparisons on the mycoremediation potential of various fungal species, treatment methods, and  treatment conditions, which would be much more difficult without a cohesive summary paper such as this one. This study will enable future researchers, and engineers to create novel and efficient methods for treatment of heavy metal wastewater with fungus. 

The big picture: Pollution is one of several environmental challenges facing our planet today, with heavy metal pollutants being one of the most hazardous, due to its negative impacts on human health. Current methods for treating heavy metal contaminants in wastewater are often not economically or environmentally sustainable. Mycoremediation may provide a sustainable solution to this problem, due to fungi’s inherent ability to absorb environmental pollutants, such as heavy metals. This review provides guidance on what fungal species, treatment methods, and treatment conditions would make this remediation process most effective and efficient. 

Citation: Kumar, V., & Dwivedi, S. K. (2021). Mycoremediation of heavy metals: processes, mechanisms, and affecting factors. Environmental Science and Pollution Research, 28(9), 10375–10412. https://doi.org/10.1007/s11356-020-11491-8

Ecologically diverse clades dominate the oceans via extinction resistance

Ecologically diverse clades dominate the oceans via extinction resistance

Matthew L. Knope, Andrew M. Bush, Luke O. Frishkoff, Noel A. Heim, and Jonathan L. Payne

Summarized by Anna Geldert

What data were used? Researchers examined taxonomic data of marine organisms across 444 million years of geologic time. Taxonomic data relates to the level of biodiversity of organisms, and classifies them under different evolutionary categories (domain, kingdom, phylum, class, order, family, genus, and species). On the whole, this study examined 19,992 genera (species groups) from the fossil record and 30,074 genera of living marine species..

Methods: This study examined speciation (origination) and extinction rates of marine species over the past 444 million years. Speciation refers to the evolution of new species, while extinction occurs when a species dies out; both factors impact the overall level of biodiversity. Net diversification rates (i.e., the difference between speciation and extinction rates) were calculated for each period  of geologic time. Additionally, researchers graphed a relationship between the species richness and ecological diversity at different points in geological time. Species richness refers simply to the number of species in a group, while ecological diversity indicates the number of “modes of life” present, such as varying habitats, levels of mobility, and feeding methods.

Results: An examination of the fossil record found that a high biodiversity among species groups could be reached in two primary ways: firstly, by a relatively short period of high speciation, and secondly, by a gradual increase over time due to average speciation and low extinction. While the first category tended to reach high biodiversity faster, they were more vulnerable to mass extinctions than the second group. Most species groups alive today, therefore, evolved via the second route. With respect to the relationship between species richness and ecological diversity, this study found a positive correlation between the two factors, meaning that a variety of life modes can be tied to having more species. 

The figure compares ecological diversity and species richness over the past 444 million years of geologic time. Species richness is graphed as the log10 of the number of genera on the x-axis, while ecological diversity (in log10 of the number of modes of life) is on the x-axis. The x-axis spans from 0 to 4 in increments of 1, while the y-axis spans from 0.0 to 1.5 in increments of 0.5. Several slopes in different colors are shown, with a legend indicating the geologic time to which the slope corresponds. The geologic stages of time included are: Silurian to Devonian (443.4 to 358.9 million years ago), Carboniferous to Permian (358.9 to 252.2 mya), Triassic (252.2 to 201.3 mya), Jurassic to Cretaceous (201.3 to 66.0 mya), and Paleogene to Neogene (66.0 to 0.0117 mya). The slope of the modern relationship between species richness and ecological diversity is also shown. Slope values range from approximately 0.20 to 0.32 and appear generally to increase steadily over time, with some overlap between geologic stages. The modern slope is approximately 0.30, and lies in the middle of the range of slope values for the Paleogene to Neogene category.
Fig 1. Relationship between species richness and ecological diversity of marine species from 444 million years ago to present.

Why is this study important? The results from this study reveal that, in the long run, rapid diversification within a species group is not sustainable because the majority of this species group is likely to be wiped out during a mass extinction event. On the other hand, gradual diversification in species groups that are able to survive mass extinctions is a more probable explanation for modern levels of marine biodiversity. These species were most likely able to survive mass extinctions due to higher levels of ecological diversity, a theory which would also explain why ecological diversity has been increasing compared to species richness over more recent eras. This study is important because it calls into question an accepted theory that directly links ecological diversity to speciation rates. While the results from this study likewise recognizes a correlation between these factors, it also implies that the relationship between the two factors may be more complex. It is only because species groups with high ecological diversity were able to survive mass extinction events that this correlation is seen so clearly today.

The big picture: This study is important in the larger field of evolutionary ecology because it impacts our understanding of how species evolve and respond to extinction pressures over time. Researchers should not assume that the tight correlation between species richness and ecological biodiversity implies a direct causational relationship, because as this study reveals, in many cases the relationship is more complicated than that. Further research is needed to fully analyze the role that ecological diversity plays in survival of mass extinctions.

Citation: Knope, M. L., Bush, A. M., Frishkoff, L. O., Heim, N. A., & Payne, J. L. (2020). Ecologically diverse clades dominate the oceans via extinction resistance. Science, 367(6481), 1035–1038. https://doi.org/10.1126/science.aax6398


A data-driven evaluation of lichen climate change indicators in Central Europe

Matthew P. Nelson and H. Thorsten Lumbsch

Summarized by Anna Geldert

What data were used? For this study, researchers obtained collection data on 35 of the 45 lichen species designated as climate change indicators from the Global Biodiversity and Information Facility (GBIF). Data for this study focused on patterns found in Central Europe, and most specifically, Germany.

Methods: GBIF data on the lichen species were categorized into two age groups: before 1970, and 1970 to present. 1970 marked the year where reductions in the use of sulfur dioxide pollutants was implemented in Europe. Because pollution levels also play a role in the survival of lichen populations, it was important to create this distinction to separate this variable from other population changes due to climate change. Lichen species with fewer than 10 historical records were deemed unreliable and excluded from further analysis, leaving only 17 out of the 35 species. To determine the lichen’s preferred habitat, researchers combined historical distribution records of where the different species of lichen were found over time with a map of climate variables (temperature, humidity, soil composition, etc.). Using a computer model, they were able to predict the lichens’ preferred habitats with 95% accuracy, and generate a map to represent these predictions spatially. The map was compared to modern data to evaluate potential changes due to climate change.

Results: The results of this study revealed that approximately half of the 17 primary species studied were found in significant numbers outside their historical range, while the other half still resides primarily in the same regions as they did prior to 1970. Species other than the primary 17 did not have sufficient historical data to recognize specific trends in geographic distribution. However, researchers noted that only one third of these additional species saw an increase in abundance in recent years, while the other two thirds saw equal or reduced numbers compared to the limited historical records.

The map shows the distribution of Opergrapha vermicellifera over time. The map spans 10° of latitude, from 45° North to 55° North, and 20° of longitude, from 5° East to 25° East. The map is divided into suitable and unsuitable habitat for Opegrapha vermicellifera, which are shaded in dark green and light green respectively. The suitable habitat makes up only about 10% of the image, and is composed of a narrow, uneven band running from 46° North, 5° East to 55° North, 14° East. Approximately 11 historical (before 1970s) records of lichen distribution are marked by yellow triangles on the map, and all are contained within or along the border of the area denoted as “suitable” habitat. Approximately 30 modern (1970 to present) records of lichen distribution are shown, and are marked with purple circles. While some modern lichen records lie within the “suitable” habitat, approximately two thirds lie in the “unsuitable” area; the majority of these points lie 5° to 10° East and a few degrees South of the “suitable” range.
Fig 1. Distribution of Opegrapha vermicellifera is shown as an example of one of the maps created to analyze changes in lichen distribution over time. The map compares historical records from prior to 1970 (orange triangles) and modern records from after 1970 (purple circles). Habitat deemed suitable/unsuitable was determined using a computer model of climate variables based on pre-1970 habitat. For Opegrapha vermicellifera, over 30% of modern records lie outside historically suitable habitat.

Why is this study important? This study calls into question the usefulness of lichen as climate change indicator species. For one, the study found that there is very little data, especially historical data, on these species and the habitat they lived in originally. Therefore, it is somewhat difficult to draw conclusions regarding the degree of the lichen’s response to climate change. The study also found that, even among species with sufficient data, only about half were found outside their historical range. If climate change was truly impacting lichen populations as much as was originally thought, researchers would expect to find all populations outside of this range because they would have migrated to better suit their traditional habitat. These results pose the question as to whether other factors may be impacting the distribution of lichen even more so than climate change. For example, the rise and fall of sulfur dioxide pollutants before and after 1970 may be more significant.

The big picture: This study serves as a warning for climate change scientists, who may tend to jump to conclusions regarding migration, geographic distribution, and local extinction of many species of lichen in recent years. For many species of lichen, there is not enough data to determine whether the geographic distribution of lichen has changed, as well as whether these changes were due to climate change instead of other factors. More research and collection of historical data is needed in order to confirm the usefulness of these species as climate change indicators in future studies.

Citation: Nelsen, M. P., & Lumbsch, H. T. (2020). A data-driven evaluation of lichen climate change indicators in Central Europe. Biodiversity and Conservation, 29(14), 3959–3971. https://doi.org/10.1007/s10531-020-02057-8

Anna Geldert (she/her), Geobiology Undergraduate Student

background: greenery with trees and leaves and grassy area. foreground: Anna hugging a tree trunk and smiling. Tell us a little bit about yourself. Hi! My name is Anna Geldert (she/her). I’m from Minnesota, but I’ve spent the past year living in Vermont where I’m working toward my undergraduate degree at Middlebury College. In my free time, I enjoy reading, writing, practicing music, and playing volleyball on my college’s club team. I’m also a huge outdoor enthusiast, and I always look forward to camping, hiking, canoeing, or skiing with friends and family. Spending so much time outdoors as a kid is one of the factors that sparked my interest in the natural sciences in the first place, and the main reason I am so passionate about sustainability today. 

What kind of scientist are you and what do you do? Currently, I’m working toward a joint undergraduate degree in Biology and Geology. I’m fascinated by the way Earth’s natural systems function, and how they’ve evolved around the world and across geologic time. While I’m not totally sure what direction I want to go in this field, I’m ultimately hoping to pursue a career doing field research in relation to ecosystem response to climate and other anthropogenic change. 

What is your favorite part about being a scientist, and how did you get interested in science? In many ways, my interest in science developed long before I took any classes or considered a career in the field. One of my biggest supporters is my dad, who is a physics teacher. Growing up, he always encouraged me to stay curious and frequently used me as a guinea pig for demonstrations he planned to do in class the following day. I also spent a lot of time camping and hiking as a kid, which sparked my interest in the natural sciences. My favorite part about science is that it allows me to spend time outside with lots of hands-on experiences. Seeing first-hand how something we learned in class presents itself in the real world is really gratifying and reminds me why I wanted to study science in the first place.

background: light blue sky with clouds and darker tree line. Foreground: Anna rowing a canoe on a calm lake

How does your work contribute to the betterment of society in general? I hope my work will be used to help human societies coexist with the Earth in a way that makes sense for both parties. For example, last year I studied the potential of using fungal mycelium as a sustainable option for treating acid mine drainage. I think Earth’s natural systems have a lot to offer, and studying them can help us better understand how to act sustainably in our own life. 

background: trail in a forest with bright green leaves and a brown trail. foreground: Anna dressed in hiking gear with binoculars.What advice do you have for up and coming scientists? Science can be whatever you want to make of it. It is such a broad field, and there are so many opportunities to tailor your education and research to something you’re passionate about. Personally, I wasn’t super interested in science until I was able to do more hands-on experiments and independent research.. That was when I realized I could apply interests I already had – such as sustainability and the outdoors – to actual scientific study in Geo-Biology. I would encourage future scientists to keep an open mind and use science as a means to explore whatever sparks their curiosity.