Evolution and Paradigm Shift in Forest Health Research: A Review of Global Trends and Knowledge Gaps
Abstract
:1. Introduction
2. Materials and Methods
2.1. Scientific Database Search
2.2. Descriptive Analysis of the Status of Forest Health Research
2.3. Temporal Evolution in Forest Health Issues
2.4. Clustering of Conceptual Subdomains and Temporal Trends
3. Results
3.1. Descriptive Analysis of Scientific Production in Forest Health Research
3.1.1. General Findings
3.1.2. Publication Impact by Country
3.1.3. Research Approaches and Issues
3.2. Temporal Keyword Analysis
3.3. Clustering and Trends in Forest Health Descriptors
4. Discussion
4.1. Scientific Production on Forest Health
4.2. Evolution of the Most Relevant Keywords on Forest Health Research
4.2.1. Integrating Keywords: Uncovering Patterns
4.2.2. Origins and Context of Paradigm Shifts
- The rise of global environmental problems linked to atmospheric pollution. At the beginning of the time series, we found monocausal approaches to forest health disturbances, where the most important drivers were “pests and diseases” as well as “air pollution”. This earliest cluster contains concepts, which are attributable to well-defined scientific disciplines: “nutrients”, “forest soils”, “fertilisation”, “ozone”, “seedlings”, “calcium”, etc. This pattern indicates the low level of discipline integration that the target concept experienced prior to 1990. Furthermore, it shows clear links with the first modern environmental movements worldwide that took place between the 1960s and 1970s, focusing on nature conservation and environmental protection. Predecessor events include the book “Silent Spring” by Rachel Carson (1962) [52], which denounced the harmful effects on the environment of the massive use of chemicals such as pesticides. The first “Earth Day” (1970), the United Nations Conference on the Human Environment in Stockholm (1972), and the “Energy Crisis of 1973” awakened awareness of the dependence on oil and the search for alternative sources. A central forest health topic in this cluster is “forest decline”, with strong links to acid deposition, air pollutants, and ozone. In fact, “forest decline” was a term commonly used to depict the research concern about forest deterioration due to air pollution, mostly in Northern Europe and North America [53]. This forest problem gained international relevance in the 1980s with “The Geneva Convention on Long-Range Transboundary Air Pollution” (1979), “The Vienna Convention for the Protection of the Ozone Layer” (1985), and the signing of “The Montreal Protocol on Substances that Deplete the Ozone Layer” (1987). This environmental problem kept research strongly active until the end of the 20th century. It is from the 1990s onwards that the evidence on the effects of pollution began to be related to human health and ecosystems, and although the global burden of pollutants has been increasing in the first two decades of the 21st century, efforts are being made to continue reducing them [51].
- Global environmental conservation. The second cluster is dominated by the decline and physiology of the forest, appearing in the late 1990s and early 2000s. Thus, the methodologies mainly used are related to ecophysiology and forest inventories. This period shows concepts with a higher level of integration among disciplines and knowledge bodies: “growth”, “competition”, “forest health”, “ecosystems”, “carbon sequestration”, etc. It also reflects the rise of current environmental problems such as carbon emissions and deforestation. This may be mainly due to the events that took place during the 1990s, where concerns with a more holistic and multidisciplinary view of nature conservation and the environment began to broaden. At this time, among others, the most famous world summits took place: “The United Nations Conference on Environment and Development”, or better known as “The Rio de Janeiro Summit” (1992), laid the first foundations for the signing of the United Nations Framework Convention on Climate Change and the signing of the treaty “The Convention on Biological Diversity”, being the first global agreement to promote aspects of international cooperation in the conservation and sustainable use of biodiversity. The famous “Kyoto Protocol” (1997) on the reduction of greenhouse gases that cause climate change is also approved in this period. Almost simultaneously, the FAO publishes a report that highlights the deforestation of large tracts of tropical forests in Latin America, Africa, and Asia [54]. At the same time, the achievements of the international policy on air pollutants reduced to some extent the pressure of air pollution on forest ecosystems [55], and consequently in the forest health research field. In summary, this temporal cluster represents the initial foundations of a more holistic and larger-scale view of the planet’s global problems, evidenced among the key words in scientific publications of the time, leading to the use of more multidisciplinary, integrative, and comprehensive concepts.
- Multi-causality and tree mortality. The research that begins with the 21st century shows multi-causal thinking about the problems that occur due to the deterioration of forests and the environment. This group shows a wide range of concepts, where the words that stand out the most are “patterns”, “dynamics”, and “disturbances”. Now the forest problems are based on multi-causality, a more complex vision that can be studied not only at the tree level but at different scales and in a multidisciplinary way. The characterisation of ecosystem dynamics is based on classification, offering scales, intensities, or patterns that measure diversity, fragmentation, deforestation, succession, competition, susceptibility, and regeneration, among other processes [56,57,58]. Furthermore, other concepts such as “management” and “restoration” also emerge as a key concept suggesting a more applied vision in the forest health research agenda [59].At the end of this period, the research agenda converges on the topic of “tree mortality” with numerous links to a wide range of concepts. Other highly integrative concepts also appear (e.g., restoration and resilience) reinforcing the paradigm of multi-causality in forest health research [60,61]. “Wildfires”, their consequences, and some methods used to monitor them, are presented here to explain the environmental issues addressed in that time. It is no longer enough to quantify the causes of disturbances in the system, but rather the effects of the disturbances themselves, in search of solutions and to assess both the damage and the improvement in the global balance.
- Climate-change-driven research. The most recent cluster mainly contains concepts related to “climate change” (e.g., “vulnerability”, “adaptation”, “change impacts”, etc.). Interestingly, this cluster seems to reduce the degree of knowledge integration as scientists are focusing mostly on understanding the consequences of climate change on forests, although this challenge is much more complex than those described previously. This is evidence of the greater environmental awareness, both social and political, in the mitigation of climate change. One example is the approval of the “The Paris Agreement” (2016), which establishes a global framework on climate change focused on concrete aspects such as curbing global warming and achieving carbon neutrality before the end of the century, where the use of the best available science and technology are directly included to improve the conditions of the planet. In fact, “climate change” can be considered a so called wicked problem [62]: multifaceted problems with fuzzy definitions and elusive and complex solutions. This explains why the current distribution of words within the drivers becomes more equative. It is also the boom in technological development that drives part of these efforts in generating instruments, methods, and measurement and evaluation techniques that are increasingly more accurate, reliable, and accessible. The emergence of portable electronic equipment or geospatial technologies like remote sensing were a breakthrough to continuously and efficiently obtain data across different spatio-temporal scales. This idea is supported by the presence within this cluster of methods of assessment (e.g., carbon isotope and dendroecology) and a great number of forest condition variables (e.g., evapotranspiration, water use efficiency, stomatal conductivity, hydraulic failure, etc.) that are currently measured with sophisticated ecophysiological sensors (e.g., “gas exchange” related to Eddy covariance towers or photosynthesis sensors).
4.3. Trends, Topics, Concepts, and Methodologies on Forest Health Research
4.4. The Way Forward: Future Vision for the Forest Health Concept
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Correction Statement
References
- Camarero, J.J. The drought–dieback–death conundrum in trees and forests. Plant Ecol. Divers. 2021, 14, 1–12. [Google Scholar] [CrossRef]
- Deuffic, P.; Garms, M.; He, J.; Brahic, E.; Yang, H.; Mayer, M. Forest Dieback, a Tangible Proof of Climate Change? A Cross-Comparison of Forest Stakeholders’ Perceptions and Strategies in the Mountain Forests of Europe and China. Environ. Manag. 2020, 66, 858–872. [Google Scholar] [CrossRef] [PubMed]
- Bailin, A. Worlds in worlds: Assigning inferences to subdomains. J. Lit. Semant. 2004, 33, 93–109. [Google Scholar] [CrossRef]
- Cobo, M.J.; López-Herrera, A.G.; Herrera-Viedma, E.; Herrera, F. An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field. J. Informetric. 2011, 5, 146–166. [Google Scholar] [CrossRef]
- Teale, S.A.; Castello, J.D. The past as key to the future: A new perspective on forest health. In Forest Health: An Integrated Perspective; Castello, J.D., Teale, S.A., Eds.; Cambridge University Press: New York, NY, USA, 2011; pp. 3–16. [Google Scholar]
- Urquhart, J.; Marzano, M.; Potter, C. The Human Dimensions of Forest & Tree Health-Global Perspectives; Springer: Berlin/Heidelberg, Germany, 2018. [Google Scholar]
- Mack, P.; Kremer, J.; Kleinschmit, D. Forest dieback reframed and revisited? Forests (re)negotiated in the German media between forestry and nature conservation. For. Policy Econ. 2023, 147, 102883. [Google Scholar] [CrossRef]
- Ostry, M.E.; Venette, R.C.; Juzwik, J. Decline as a disease category: Is it helpful? Phytopathology 2011, 101, 404–409. [Google Scholar] [CrossRef]
- Hartmann, H.; Bastos, A.; Das, A.J.; Esquivel-Muelbert, A.; Hammond, W.M.; Martínez-Vilalta, J.; Mcdowell, N.G.; Powers, J.S.; Pugh, T.A.M.; Ruthrof, K.X.; et al. Climate Change Risks to Global Forest Health: Emergence of Unexpected Events of Elevated Tree Mortality Worldwide. Annu. Rev. Plant Biol. 2022, 73, 673–702. [Google Scholar] [CrossRef] [PubMed]
- Menezes-Silva, P.E.; Loram-Lourenço, L.; Alves, R.D.F.B.; Sousa, L.F.; Almeida, S.E.d.S.; Farnese, F.S. Different ways to die in a changing world: Consequences of climate change for tree species performance and survival through an ecophysiological perspective. Ecol. Evol. 2019, 9, 11979–11999. [Google Scholar] [CrossRef] [PubMed]
- Trumbore, S.; Brando, P.; Hartmann, H. Forest health and global change. Science 2015, 349, 814–818. [Google Scholar] [CrossRef]
- Edmonds, R.L.; Agee, J.K.; Gara, R.I. Forest Health and Protection, 2nd ed.; Waveland Press, Ed.; Waveland Press: Lake Zurich, IL, USA, 2011; ISBN 9781577666523. [Google Scholar]
- Tierney, G.L.; Faber-Langendoen, D.; Mitchell, B.R.; Shriver, W.G.; Gibbs, J.P. Monitoring and evaluating the ecological integrity of forest ecosystems. Front. Ecol. Environ. 2009, 7, 308–316. [Google Scholar] [CrossRef]
- Rempel, R.S.; Naylor, B.J.; Elkie, P.C.; Baker, J.; Churcher, J.; Gluck, M.J. An indicator system to assess ecological integrity of managed forests. Ecol. Indic. 2016, 60, 860–869. [Google Scholar] [CrossRef]
- Weingart, P. The moment of truth for science. The consequences of the “knowledge society” for society and science. EMBO Rep. 2002, 155–164. [Google Scholar] [CrossRef]
- Dobbertin, M. Tree growth as indicator of tree vitality and of tree reaction to environmental stress: A review. Eur. J. For. Res. 2005, 124, 319–333. [Google Scholar] [CrossRef]
- Randolph, K.D.C.; Dooley, K.; Shaw, J.D.; Morin, R.S.; Asaro, C.; Palmer, M.M. Past and present individual-tree damage assessments of the US national forest inventory. Environ. Monit. Assess. 2021, 193, 116. [Google Scholar] [CrossRef] [PubMed]
- Doblas-Miranda, E.; Martínez-Vilalta, J.; Lloret, F.; Álvarez, A.; Ávila, A.; Bonet, F.J.; Brotons, L.; Castro, J.; Curiel Yuste, J.; Díaz, M.; et al. Reassessing global change research priorities in mediterranean terrestrial ecosystems: How far have we come and where do we go from here? Glob. Ecol. Biogeogr. 2015, 24, 25–43. [Google Scholar] [CrossRef]
- Pyšek, P.; Richardson, D.M.; Pergl, J.; Jarošík, V.; Sixtová, Z.; Weber, E. Geographical and taxonomic biases in invasion ecology. Trends Ecol. Evol. 2008, 23, 237–244. [Google Scholar] [CrossRef]
- Abas, A. A systematic literature review on the forest health biomonitoring technique: A decade of practice, progress, and challenge. Front. Environ. Sci. 2023, 11, 64. [Google Scholar] [CrossRef]
- Shukla, P.; Skeg, J.; Buendia, E.; Masson-Delmotte, V. Climate Change and Land: An IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse. 2019. Available online: https://philpapers.org/rec/SHUCCA-2 (accessed on 9 January 2023).
- Bayr, C.; Gallaun, H.; Kleb, U.; Kornberger, B.; Steinegger, M.; Winter, M. Satellite-based forest monitoring: Spatial and temporal forecast of growing index and short-wave infrared band. Geospat. Health 2016, 11, 31–42. [Google Scholar] [CrossRef]
- Lausch, A.; Erasmi, S.; King, D.J.; Magdon, P.; Heurich, M. Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics. Remote Sens. 2016, 8, 2019. [Google Scholar] [CrossRef]
- Ecke, S.; Dempewolf, J.; Frey, J.; Schwaller, A.; Endres, E.; Klemmt, H.-J.; Tiede, D.; Seifert, T. UAV-Based Forest Health Monitoring: A Systematic Review. Remote Sens. 2022, 14, 3205. [Google Scholar] [CrossRef]
- Pautasso, M.; Stenlid, J.; Oliva, J.; Menkis, A. Scientometrics of Forest Health and Tree Diseases: An Overview. Forests 2016, 7, 17. [Google Scholar] [CrossRef]
- Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 2021, 372, n71. [Google Scholar] [CrossRef] [PubMed]
- van Eck, N.J.; Waltman, L. Visualizing Bibliometric Networks; Springer International Publishing: Cham, Switzerland, 2014; ISBN 9783319103778. [Google Scholar]
- Aria, M.; Cuccurullo, C. bibliometrix: An R-tool for comprehensive science mapping analysis. J. Informetr. 2017, 11, 959–975. [Google Scholar] [CrossRef]
- Dervis, H. Bibliometric analysis using bibliometrix an R package. J. Scientometr. Res. 2019, 8, 156–160. [Google Scholar] [CrossRef]
- Iliescu, A.N. Conceptual Atlas of the Knowmad Literature: Visual Mapping with VOSviewer. Manag. Dyn. Knowl. Econ. 2021, 9, 379–392. [Google Scholar] [CrossRef]
- Riehmann, P.; Hanfler, M.; Froehlich, B. Interactive sankey diagrams. In Proceedings of the IEEE Symposium on Information Visualization, 2005. INFOVIS 2005, Minneapolis, MN, USA, 3–25 October 2005; pp. 233–240. [Google Scholar] [CrossRef]
- Statistica R Core Team. R: A Language and Environment for Statistical Computing; Statistica R Core Team: Vienna, Austria, 2021. [Google Scholar]
- Baldwin, H.I. Germination of the red spruce. Plant Physiol. 1934, 9, 491. [Google Scholar] [CrossRef] [PubMed]
- Allen, C.D.; Macalady, A.K.; Chenchouni, H.; Bachelet, D.; McDowell, N.; Vennetier, M.; Kitzberger, T.; Rigling, A.; Breshears, D.D.; Hogg, E.H.; et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manag. 2010, 259, 660–684. [Google Scholar] [CrossRef]
- Pautasso, M. Publication Growth in Biological Sub-Fields: Patterns, Predictability and Sustainability. Sustainability 2012, 4, 3234–3247. [Google Scholar] [CrossRef]
- Chavalarias, D. What’s wrong with Science?: Modeling the collective discovery processes with the Nobel game. Scientometrics 2017, 110, 481–503. [Google Scholar] [CrossRef]
- Bréda, N.; Peiffer, M. Vulnerability to forest decline in a context of climate changes: New prospects about an old question in forest ecology. Ann. For. Sci. 2014, 71, 627–631. [Google Scholar] [CrossRef]
- Pautasso, M.; Schlegel, M.; Holdenrieder, O. Forest Health in a Changing World. Microb. Ecol. 2015, 69, 826–842. [Google Scholar] [CrossRef] [PubMed]
- Wigand, M.E.; Timmermann, C.; Scherp, A.; Becker, T.; Steger, F. Climate Change, Pollution, Deforestation, and Mental Health: Research Trends, Gaps, and Ethical Considerations. GeoHealth 2022, 6, e2022. [Google Scholar] [CrossRef] [PubMed]
- FAO. Global Forest Resources Assessment 2020; FAO: Rome, Italy, 2020. [Google Scholar] [CrossRef]
- Nuñez, M.A.; Barlow, J.; Cadotte, M.; Lucas, K.; Newton, E.; Pettorelli, N.; Stephens, P.A. Assessing the uneven global distribution of readership, submissions and publications in applied ecology: Obvious problems without obvious solutions. J. Appl. Ecol. 2019, 56, 4–9. [Google Scholar] [CrossRef]
- Stone, C.; Turner, R.; Verbesselt, J. Integrating plantation health surveillance and wood resource inventory systems using remote sensing. Aust. For. 2008, 71, 245–253. [Google Scholar] [CrossRef]
- Pause, M.; Schweitzer, C.; Rosenthal, M.; Keuck, V.; Bumberger, J.; Dietrich, P.; Heurich, M.; Jung, A.; Lausch, A. In Situ/Remote Sensing Integration to Assess Forest HealthA Review. Remote Sens. 2016, 8, 471. [Google Scholar] [CrossRef]
- Ruiz-Benito, P.; Vacchiano, G.; Lines, E.R.; Reyer, C.P.O.; Ratcliffe, S.; Morin, X.; Hartig, F.; Mäkelä, A.; Yousefpour, R.; Chaves, J.E.; et al. Available and missing data to model impact of climate change on European forests. Ecol. Modell. 2020, 416, 108870. [Google Scholar] [CrossRef]
- Abella, S.R.; Hausman, C.E.; Jaeger, J.F.; Menard, K.S.; Schetter, T.A.; Rocha, O.J. Fourteen years of swamp forest change from the onset, during, and after invasion of emerald ash borer. Biol. Invasions 2019, 21, 3685–3696. [Google Scholar] [CrossRef]
- Dudney, J.C.; Nesmith, J.C.B.; Cahill, M.C.; Cribbs, J.E.; Duriscoe, D.M.; Das, A.J.; Stephenson, N.L.; Battles, J.J. Compounding effects of white pine blister rust, mountain pine beetle, and fire threaten four white pine species. Ecosphere 2020, 11, e03263. [Google Scholar] [CrossRef]
- Baer, K.C.; Gray, A.N. Biotic predictors improve species distribution models for invasive plants in Western U.S. Forests at high but not low spatial resolutions. For. Ecol. Manag. 2022, 518, 120249. [Google Scholar] [CrossRef]
- Petit-Cailleux, C.; Davi, H.; Lefèvre, F.; Verkerk, P.J.; Fady, B.; Lindner, M.; Oddou-Muratorio, S. Tree Mortality Risks Under Climate Change in Europe: Assessment of Silviculture Practices and Genetic Conservation Networks. Front. Ecol. Evol. 2021, 9, 582. [Google Scholar] [CrossRef]
- Lowery, D.P. Ponderosa Pine (Pinus ponderosa Dougl. ex Laws.). 1984. Available online: https://www.fs.usda.gov/research/treesearch/32194 (accessed on 15 June 2024).
- Glitzenstein, J.S.; Brewer, J.S.; Masters, R.E.; Varner, J.M.; Hiers, J.K. Fire Ecology and Fire Management of Southeastern Coastal Plain Pine Ecosystems. In Fire Ecology and Management: Past, Present, and Future of US Forested Ecosystems; Greenberg, C.H., Collins, B., Eds.; Managing Forest Ecosystems; Springer: Dordrecht, The Netherlands, 2021; Volume 39, pp. 63–104. ISBN 978-3-030-73267-7. [Google Scholar]
- Fowler, D.; Brimblecombe, P.; Burrows, J.; Heal, M.R.; Grennfelt, P.; Stevenson, D.S.; Jowett, A.; Nemitz, E.; Coyle, M.; Lui, X.; et al. A chronology of global air quality. Philos. Trans. R. Soc. A 2020, 378, 20190314. [Google Scholar] [CrossRef] [PubMed]
- Carson, R. Silent Spring; Fawcett Crest: New York, NY, USA, 1962. [Google Scholar]
- Monserud, R.A. Simulation of Forest Tree Mortality. For. Sci. 1976, 22, 438–444. [Google Scholar] [CrossRef]
- FAO. Forest Resources Assessment 1990: Tropical Countries; FAO: Rome, Italy, 1993. [Google Scholar]
- Grennfelt, P.; Engleryd, A.; Forsius, M.; Hov, Ø.; Rodhe, H.; Cowling, E. Acid rain and air pollution: 50 years of progress in environmental science and policy. Ambio 2020, 49, 849–864. [Google Scholar] [CrossRef] [PubMed]
- Stephenson, N.L.; Van Mantgem, P.J.; Bunn, A.G.; Bruner, H.; Harmon, M.E.; O’Connell, K.B.; Urban, D.L.; Franklin, J.F. Causes and implications of the correlation between forest productivity and tree mortality rates. Ecol. Monogr. 2011, 81, 527–555. [Google Scholar] [CrossRef]
- Zhang, X.; Cao, Q.V.; Duan, A.; Zhang, J. Modeling tree mortality in relation to climate, initial planting density, and competition in Chinese fir plantations using a Bayesian logistic multilevel method. Can. J. For. Res. 2017, 47, 1278–1285. [Google Scholar] [CrossRef]
- McDowell, N.G. Deriving pattern from complexity in the processes underlying tropical forest drought impacts. New Phytol. 2018, 219, 841–844. [Google Scholar] [CrossRef] [PubMed]
- Diggins, C.; Fulé, P.Z.; Kaye, J.P.; Covington, W.W.; Diggins, C.; Fulé, P.Z.; Kaye, J.P.; Covington, W.W. Future climate affects management strategies for maintaining forest restoration treatments. Int. J. Wildl. Fire 2010, 19, 903–913. [Google Scholar] [CrossRef]
- Fan, C.; Tan, L.; Zhang, P.; Liang, J.; Zhang, C.; Wang, J.; Zhao, X.; von Gadow, K. Determinants of mortality in a mixed broad-leaved Korean pine forest in northeastern China. Eur. J. For. Res. 2017, 136, 457–469. [Google Scholar] [CrossRef]
- Muzika, R.M. Opportunities for silviculture in management and restoration of forests affected by invasive species. Biol. Invasions 2017, 19, 3419–3435. [Google Scholar] [CrossRef]
- Crowley, K.; Head, B.W. The enduring challenge of ‘wicked problems’: Revisiting Rittel and Webber. Policy Sci. 2017, 50, 539–547. [Google Scholar] [CrossRef]
- Fitts, L.A.; Domke, G.M.; Russell, M.B. Comparing methods that quantify forest disturbances in the United States’ national forest inventory. Environ. Monit. Assess. 2022, 194, 304. [Google Scholar] [CrossRef]
- Klockow, P.A.; Edgar, C.B.; Windmuller-Campione, M.A.; Baker, F.A. Stand Inventories as an Early Detection System for Forest Health Threats. For. Sci. 2023, 69, 1–9. [Google Scholar] [CrossRef]
- Lorenz, M.; Fischer, R. Pan-European Forest Monitoring: An Overview. Dev. Environ. Sci. 2013, 12, 19–32. [Google Scholar] [CrossRef]
- Tkacz, B.; Riitters, K.; Percy, K.E. Forest Monitoring Methods in the United States and Canada: An Overview. Dev. Environ. Sci. 2013, 12, 49–73. [Google Scholar] [CrossRef]
- Benner, P.; Sabel, P.; Wild, A. Photosynthesis and transpiration of healthy and diseased spruce trees in the course of three vegetation periods. Trees 1988, 2, 223–232. [Google Scholar] [CrossRef]
- Ibanez, I.; Katz, D.S.W.; Peltier, D.; Wolf, S.M.; Barrie, B.T.C. Assessing the integrated effects of landscape fragmentation on plants and plant communities: The challenge of multiprocess–multiresponse dynamics. J. Ecol. 2014, 102, 882–895. [Google Scholar] [CrossRef]
- Hyink, D.M.; Zedaker, S.M. Stand dynamics and the evaluation of forest decline. Tree Physiol. 1987, 3, 17–26. [Google Scholar] [CrossRef] [PubMed]
- Curtis, P.S.; Wang, X.Z. A meta-analysis of elevated CO2 effects on woody plant mass, form, and physiology. Oecologia 1998, 113, 299–313. [Google Scholar] [CrossRef]
- Millar, D.J.; Ewers, B.E.; Mackay, D.S.; Peckham, S.; Reed, D.E.; Sekoni, A. Improving ecosystem-scale modeling of evapotranspiration using ecological mechanisms that account for compensatory responses following disturbance. Water Resour. Res. 2017, 53, 7853–7868. [Google Scholar] [CrossRef]
- Fasanella, M.; Suarez, M.L.; Hasbun, R.; Premoli, A.C. Individual-based dendrogenomic analysis of forest dieback driven by extreme droughts. Can. J. For. Res. 2021, 51, 420–432. [Google Scholar] [CrossRef]
- Hirons, S.; Matilda Collins, C.; Singh, M. Assessing variation in the effectiveness of IUCN protected area categorisation. What remotely sensed forest integrity and human modification reveals across the major tropical forest biomes. Ecol. Indic. 2022, 143, 109337. [Google Scholar] [CrossRef]
- Lausch, A.; Borg, E.; Bumberger, J.; Dietrich, P.; Heurich, M.; Huth, A.; Jung, A.; Klenke, R.; Knapp, S.; Mollenhauer, H.; et al. Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches. Remote Sens. 2018, 10, 1120. [Google Scholar] [CrossRef]
- Ehrlich, P.R. and Ehrlich, A.H. Extinction: The Causes and Consequences of the Disappearance of Species; Random House: New York, NY, USA, 1981. [Google Scholar]
- Ehrlich, P.R.; Mooney, H.A. Extinction, Substitution, and Ecosystem Services. Bioscience 1983, 33, 248–254. [Google Scholar] [CrossRef]
- Patel, A.; Rapport, D.J.; Vanderlinden, L.; Eyles, J. Forests and societal values: Comparing scientific and public perception of forest health. Environmentalist 1999, 19, 239–249. [Google Scholar] [CrossRef]
- Atlas, R.M. One Health: Its Origins and Future. Curr. Top Microbiol. Immunol. 2013, 365, 1–13. [Google Scholar] [CrossRef] [PubMed]
- Couto, R.d.M.; Brandespim, D.F. A review of the one health concept and its application as a tool for policy-makers. Int. J. One Health 2020, 6, 83–89. [Google Scholar] [CrossRef]
- Schneider, M.C.; Munoz-Zanzi, C.; Min, K.; Aldighieri, S. “One Health” From Concept to Application in the Global World. In Oxford Research Encyclopedia of Global Public Health; Oxford University Press: Oxford, UK, 2019. [Google Scholar] [CrossRef]
- Slovik, S. Early needle senescence and thinning of the crown structure of Picea abies as induced by chronic SO2 pollution.1. Model deduction and analysis. Glob. Chang. Biol. 1996, 2, 443–458. [Google Scholar] [CrossRef]
- Burger, J.A. Soils biology and tree growth|Soil and its Relationship to Forest Productivity and Health. Encycl. For. Sci. 2004, 1189–1195. [Google Scholar] [CrossRef]
- Gauthier, S.; Bernier, P.; Kuuluvainen, T.; Shvidenko, A.Z.; Schepaschenko, D.G. Boreal forest health and global change. Science 2015, 349, 819–822. [Google Scholar] [CrossRef]
- Millar, C.I.; Stephenson, N.L. Temperate forest health in an era of emerging megadisturbance. Science 2015, 349, 823–826. [Google Scholar] [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Acosta-Muñoz, C.; Navarro-Cerrillo, R.M.; Bonet-García, F.J.; Ruiz-Gómez, F.J.; González-Moreno, P. Evolution and Paradigm Shift in Forest Health Research: A Review of Global Trends and Knowledge Gaps. Forests 2024, 15, 1279. https://doi.org/10.3390/f15081279
Acosta-Muñoz C, Navarro-Cerrillo RM, Bonet-García FJ, Ruiz-Gómez FJ, González-Moreno P. Evolution and Paradigm Shift in Forest Health Research: A Review of Global Trends and Knowledge Gaps. Forests. 2024; 15(8):1279. https://doi.org/10.3390/f15081279
Chicago/Turabian StyleAcosta-Muñoz, Cristina, Rafael M. Navarro-Cerrillo, Francisco J. Bonet-García, Francisco J. Ruiz-Gómez, and Pablo González-Moreno. 2024. "Evolution and Paradigm Shift in Forest Health Research: A Review of Global Trends and Knowledge Gaps" Forests 15, no. 8: 1279. https://doi.org/10.3390/f15081279
APA StyleAcosta-Muñoz, C., Navarro-Cerrillo, R. M., Bonet-García, F. J., Ruiz-Gómez, F. J., & González-Moreno, P. (2024). Evolution and Paradigm Shift in Forest Health Research: A Review of Global Trends and Knowledge Gaps. Forests, 15(8), 1279. https://doi.org/10.3390/f15081279