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This picture was taken at night in the coastal community of Dakenggu in Yilan County, which is just southeast of Taipei in Taiwan. I’m on the left, working with two other researchers to measure the body size of a red-clawed crab (Chiromantes haematocheir).An old man from the local community told me that years ago, during the breeding season, you could barely cross the road because of all the crabs. He said nobody knows where they all went. They’re an important memory for the local people, and part of the culture here.Habitat loss — especially resulting from the widespread use of concrete — seems to be driving the decline. I’m working with local people to create rocky microhabitats and artificial wetlands for the red-clawed crabs to live in. They’re important scavengers — eating dead animals and other organic matter, breaking it down and playing a key part in the nutrient cycle.Small organisms need our help — they can’t stand up for themselves. But in Taiwan, a lot of people think a coastal villa is more important than a few crabs. Corporations want to build luxury developments in our national parks, and authorities often approve them. I’ve seen so many intact habitats destroyed or covered in concrete.Crabs caught my interest because they were frequent visitors to my dormitory. National Sun Yat-sen University in Kaohsiung sits in a coastal buffer zone between a mountain and the ocean, and land hermit crabs (Coenobita cavipes) have to scurry through it on their way to breed.After watching habitat after habitat destroyed by overdevelopment, I’ve realized that just doing the science is not enough. It doesn’t matter how many papers you publish: you need to connect with people through education and communication. That’s why I decided to do my PhD in social science. And it’s why I believe conservation will be my life’s work.

Nature 603, 962 (2022)
doi: https://doi.org/10.1038/d41586-022-00810-3

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Marine Ecology Research Group and Centre for Integrative Ecology, School of Biological Sciences, University of Canterbury, Christchurch, New ZealandMads S. Thomsen, Luca Mondardini, David R. Schiel & Alfonso SicilianoDepartment of Bioscience, Aarhus University, 4000, Roskilde, DenmarkMads S. ThomsenSmithsonian Tropical Research Institute, Apartado, 0843-03092, Balboa, Ancon, Republic of PanamaAndrew H. Altieri, Viktoria M. M. Frühling, Seamus B. Harrison & Gerhard ZotzEnvironmental Engineering Sciences, University of Florida, Gainesville, FL, USAAndrew H. Altieri & Christine AngeliniDepartment of Biological Sciences, Macquarie University, Sydney, NSW, AustraliaMelanie J. Bishop & Semonn OleksynDipartimento di Biologia, Università di Pisa, CoNISMa, Via Derna 1, 56126, Pisa, ItalyFabio Bulleri & Joachim LangeneckMarine Sciences, University of Georgia, Athens, GA, USARoxanne FarhanCentre for Marine Science and Innovation, School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, NSW, AustraliaPaul E. Gribben & Brendan S. LanhamSydney Institute of Marine Science, Chowder Bay Road, Mosman, 2088, Sydney, NSW, AustraliaPaul E. Gribben & Brendan S. LanhamCoastal Ecology Lab, MOE Key Laboratory for Biodiversity Science and Ecological Engineering, School of Life Sciences, Fudan University, 2005 Songhu Road, 200438, Shanghai, ChinaQiang HeInstitute for Biology and Environmental Sciences, Carl von Ossietzky University Oldenburg, Oldenburg, GermanyMoritz Klinghardt, Tristan Schneider & Gerhard ZotzSchool of Biological Sciences and UWA Oceans Institute, University of Western Australia, Perth, WA, AustraliaYannick Mulders & Thomas WernbergDepartment of Biology and Marine Biology, University of North Carolina Wilmington, Wilmington, NC, USAAaron P. RamusNicholas School of the Environment, Duke University, 135 Duke Marine Lab Road, Beaufort, NC, USABrian R. Silliman & Stacy ZhangMarine Biological Association of the United Kingdom, The Laboratory, Citadel Hill, Plymouth, PL1 2PB, UKDan A. SmaleCawthron Institute, Nelson, New ZealandPaul M. South More

Base run simulationFigure 6 shows the results of the base run simulation. In this scenario, strong vegetation declines over time, while the empty area and flammable vegetation have increasing trends. As such, more fuel would be available for burning, and the wildfire can burn broader areas. Panel (a) shows an oscillatory trend for the burn rate with an average upward trend (To make sure the oscillatory behavior of the model does not fade, Appendix 4 shows the simulation result for 100 years). The observed pattern in the burn rate can be traced back to the patterns of human ignition (Panel b), and the growing trend of vulnerable properties (Panel c). In addition, the results show the long-term declining trend of strong vegetation in our base line simulation (Panel d); over time, stronger vegetation is replaced by flammable vegetation which can lead to more fire. This change in vegetation composition effectively increases the average burn rate. Over time, with more flammable vegetation and with the expansion of vulnerable properties, the likelihood of human-made ignition increases.Figure 6Base run simulation for a 20-year run of the model.Full size imageCoupling effectsFigure 7 shows how the relation between perceived fire risk and the burn rate influences the system. The black line is the base run simulation for comparison. The blue dashed line depicts the condition in which risk perception changes extremely slowly, and the human system is almost disconnected from the natural system. In this situation, if humans underestimate the fire potential, the system burns down nature, resulting in a catastrophic environmental outcome as depicted in panel (a). Panel (a) shows that the burn rate overshoots in the short term but relatively declines due to less remaining natural resources to burn.Figure 7Coupling effect analysis for 20 years. Human ignition unit is Ignition/year, and vulnerable property unit is a million hectares. Strong vegetation and flammable vegetation are provided as the ratio that each occupied the forest area.Full size imagePanel (b) displays the total burn rate throughout the study time to cast further insight into the burn rate sensitivity to perceived risk. The overall burn rate does not significantly change when the risk perception changes from 0.5 to 2, indicating the difference among burn rates in panel (a) is more about the fluctuation timing, but not the size. However, an additional rise in the sense of risk greatly raises the overall burn rate, as seen in panel (a).In the case of prolonged change in risk perception, human ignition continues to increase (panel c) as the perceived risk changes slowly. Furthermore, vulnerable properties are being built faster than their demolition (panel d). A slighter delay in perception leads to a higher frequency of oscillation as depicted in the graphs by the red dashed lines and a longer delay in a lower frequency oscillation, as shown by the purple graphs. Overall, the results are not much different from the base run. We are losing forests (panel e) and have periodic burn rates of increasing magnitude over time.Policy experimentsHere we examine the impact of implementing four proposed policies introduced in Table 2. To prevent the initial condition and transition periods affecting our comparison of proposed policies, we imposed each policy at the fifth year and compared the total burn rates between 10 and 20 years. Figure 8 shows the effect of these policies on different variables. Figure 8Policy implementation. Note: P1: limits vulnerable property development; P2: prescribed burning; P3: effective firefighting; and P4: Clear cutting. Human ignition unit is Ignition/year, and vulnerable property unit is a million hectares. Strong vegetation and flammable vegetation are provided as the ratio that each occupied the forest area.Full size imagePanels (a) and (b) show the burn rate over time and cumulative, respectively. All four policies reduce the burn-rate magnitude compared to the base run. P3 is more effective in early burning-rate reduction compared to other policies, but they ultimately result in similar behavior. It is worth noticing that P1 has the most effect on long-run fluctuation reduction, although its total effect in the time span is less than P3. It seems that firefighting is more effective in the short run, but it fails to dampen the fluctuation and instead limits its growth. This is partly because of the increase in human ignition and settlement due to the success of firefighting in the short run. As a result, people perceive less fire danger and continue to engage in high-risk activities and expand housing in the WUI. The result is further fluctuation in the burn rate even when P3 is implemented. On the other hand, the WUI expansion limitation policy can effectively reduce the burn-rate fluctuation in a timely manner. Implementing P4 causes a reduction in strong vegetation, which leads to flammable vegetation increase. As flammable vegetation is the main fuel for wildfire, this policy cause increase in fuel availability and an increase in the burning rate.Change in human ignition is provided in panel (c). Different levels of human-made ignition are observable, and the reason is that people adjust their high-risk behavior with burn rate, and not with the number of fires. In the firefighting policy, as for a given level of ignition, the burn rate declines, we observe more risky behavior and more human-made ignition. It is interesting to note that, as panel (c) shows, we end up with more WUI under policies 2, 3, and 4. In fact, the reason is that the firefighting, prescribed burning and clear cutting only affect natural sector of the model, decrease burn rate, which decreases risk perception and in turn result in more WUI development. On the other hand, P1 directly targets WUIs.Panel (e) displays the change in strong vegetation, which shows that P4 causes the most reduction in forest tree cover as it directly removes strong vegetation. P2 also causes a decrease in strong vegetation compared to the base run. The reason is that burning flammable vegetation damages young trees and prevents them from developing into solid vegetation. On the other hand, P3 has the least effect on strong vegetation by slowing the damage to young trees and confining the fire. Panel (f) shows the flammable vegetation dynamic after imposing each policy. P3 and P2 reduce flammable vegetation more than P1. However, there is an important difference in how these policies cause the reduction in flammable vegetation. In comparing panels (a) and (b), we see that while P3 causes further increases in the strong vegetation, P2 causes an increase in the empty area. P4 is the only policy that increases flammable vegetation by removing the strong vegetation and providing an empty area to be filled with young vegetation.Overall, it looks like each policy has some marginal effect on containing wildfire, though the magnitudes of effect are not considerable.Replication of United States dataFor model validation, we investigate its ability to fit a single case, United States’ wildfires from 1996 to 2015. We utilize the United States Department of Agriculture’s wildfire database for the conterminous United States (Short, 2017). The results are shown in Fig. 9. In this figure, simulation of burning rate and human ignition (continuous lines, in black) closely follows the real-world data (dotted lines, in red), and the model fairly replicates the historical trends.Figure 9Burning rate and human ignition per unit of forest area. The black line represents the model result, and the red dotted line represents the historical wildfire activity in the conterminous United States.Full size imageCombination policy implementation analysisTo better understand the impacts of our policies, we run different pairs of policies simultaneously. The results illustrate the nonlinear incremental impacts between policies. Simply put, it appears that the impact of several policies is enforced when combined synergistically. In other words, applying several policies might have a greater overall impact than the sum of the policies’ individual effects and suggests that policymakers should avoid searching for a panacea and adopt a broad range of approaches thoughtfully.The results of multiple policy implementations along with single ones are presented in Fig. 10. For example, P1 and P2 each reduce the total burn rate by 4.9% and 4.5%, respectively. While the summation of these effects is 9.4%, simultaneously implementing P1 and P2 lead to a 13.6% burn-rate reduction—P1 controls the human ignition, and P2 reduces the flammable vegetation stock—together, the burn rate is more affected than if implemented separately. The case is more interesting when P1 and P3 are imposed together. The result is a 38% burn-rate reduction compared to 13.9%, which is the sum of solely implementing each policy. The synergic effect happens because P3 lets the flammable vegetation (mainly young trees) age and become strong vegetation. Furthermore, the P1 also prevents human ignition from growing as fast as a single P3 implementation.Figure 10The nonlinear effect of policies. The benefits of implementing multiple policies differ from the sum of the effect of policies. The figure shows the percent of burn rate reduction. Note: P1: limit vulnerable property development; P2: prescribed burning; P3: effective firefighting; and P4: Clear cutting.Full size imageAn interesting case happens when P2 and P3 are implemented together. The synergic effect is less than the sum of separate implementation, mainly because both policies affect the vegetation dynamic and not the human factor in the wildfire. P2 and P3 both cause a lower initial burn rate, but due to the reduction in perceived risk of wildfire and expansion of WUI, this effect quickly disappears. This is another evidence for the importance of considering the problem as an interconnected natural and human system, where effective policies should address both sides.Finally, an interesting result emerges when all policies impose together. Surprisingly, imposing all policies together does not have the most impact on the total burn rate (32.5%), which is less than the P1 and P3 effect (38.0%). The reason relates mainly to the fact P2 and P4 both cause increase in flammable vegetation after empty area filled, which lead to more burning rate after a delay.Sensitivity analysisWe conducted a series of sensitivity analysis to check the model’s robustness to our assumptions. Specifically, we conducted a Monte-Carlo analysis and changed several parameter values to determine the range of outcomes. The results are reported in Appendix 2. In summary, the focus was on parameters that can take on substantially different values from those assumed in the model, including parameters used for risk perception formulation, its effect on human behavior, such as time to perceive risk and time to change behavior, in addition to fractional burning rate per ignition, average s burning, initial flammable vegetation, initial strong vegetation, human ignition multiplier, and initial vulnerable property. As described in the Appendix, for most of these variables, we changed the corresponding variable up to double its base run value. Moreover, we test different values for initial strong vegetation and initial flammable vegetation changing them between zero and their base run values. Each sensitivity test is the outcome of 2000 simulation runs using a uniformly distributed random distribution of the parameters within the specified intervals. The results are qualitatively robust, and their variability is within reasonable limits (See Figure A1). More

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doi: https://doi.org/10.1038/d41586-022-00888-9

ReferencesLeBrun, E. G., Jones, M., Plowes, R. M. & Gilbert, L. E. Proc. Natl Acad. Sci. USA 119, e2114558119 (2022).PubMed 
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