Effects of Landscape Pattern on Pollination, Pest Control, Water Quality, Flood Regulation, and Cultural Ecosystem Services: a Literature Review and Future Research Prospects
Purpose of Review
This review highlights recent progress on how landscape pattern (composition, configuration, landscape context, keystone landscape, scaling, and nonlinearity) affects pollination, pest control, water quality, flood control, and cultural ecosystem services (ES)—landscape esthetics and recreation.
Landscape composition and configuration showed ES-specific effects. Recent studies confirmed that pollination increased in complex, heterogeneous landscapes with more surrounding natural/semi-natural habitats. Landscape pattern could also interact with local factors to affect pollination, with stronger effects at smaller spatial scales. For pest control, a comprehensive synthesis revealed inconsistent effects of non-crop habitat composition, perhaps due to diverse responses from different enemies and pests and complex tri-trophic interactions. Spatial configuration of land-covers, connectivity, and edge effects also mattered for pest control ES. Moreover, recent studies showed that configuration of land-covers could sometimes trump composition as the primary driver for water quality. Comparing across scales (e.g., riparian vs. watershed), landscape pattern effects on water quality tended to be more pronounced at small spatial scales. For flood control, studies showed that larger and less fragmented natural covers reduced peak runoffs, with a compositional threshold ~ 30–40%. Spatial location also mattered where imperviousness concentrated closer to outlet tended to increase peak runoffs. For cultural ES, landscape esthetics and recreation showed positive correlations with naturalness composition and landscape heterogeneity.
Five overarching themes emerge for future research to advance understanding of landscape pattern effects on ES: (1) using social-ecological measures of ES; (2) assessing ES supply, flow, and demand; (3) considering interactions among multiple drivers across scales; (4) addressing ES interactions; and (5) enhancing predictive capacity of landscape models.
KeywordsLandscape structure Composition and configuration Landscape metrics Landscape heterogeneity Spatial pattern Natural capital
Many thanks to Lenore Fahrig and Sara Gagne for the opportunity to contribute to this review. I also acknowledge all the insightful discussions and ideas from the Turner lab at the University of Wisconsin—Madison over the years that shape the direction of this paper. Jiangxiao Qiu also acknowledges the USDA National Institute of Food and Agriculture, Hatch (FLA-FTL-005640) and McIntire-Stennis (1014703) projects, and National Science Foundation (ICER-1830036) for partial financial support of this work.
Compliance with Ethical Standards
Conflict of Interest
The author declares that he has no conflict of interest.
Human and Animal Rights and Informed Consent
This article does not contain any studies with human or animal subjects performed by any of the authors.
Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance
- 2.Ellis EC, Klein Goldewijk K, Siebert S, Lightman D, Ramankutty N. Anthropogenic transformation of the biomes, 1700 to 2000. Glob Ecol Biogeogr. 2010;19:589–606.Google Scholar
- 5.Scholes R, Hassan R, Ash NJ, Condition and Trends Working Group. Summary: ecosystems and their services around the year 2000. In: Ecosystems and human well-being: current state and trends, Millennium ecosystem assessment. Washington, DC: Island Press; 2005. p. 1–24.Google Scholar
- 10.•• Eigenbrod F. Redefining landscape structure for ecosystem services. Curr Landscape Ecol Rep. 2016;1:80–6. This review paper summarizes recent progress on landscape structure effects on ES, and calls for the need to consider social, biophysical and cultural drivers together to examine landscape effects on ES provision. It also provides a four-step procedure for conducting a landscape-scale ES study from a social-ecological perspective. CrossRefGoogle Scholar
- 11.•• Spake R, Lasseur R, Crouzat E, Bullock JM, Lavorel S, Parks KE, et al. Unpacking ecosystem service bundles: towards predictive mapping of synergies and trade-offs between ecosystem services. Glob Environ Chang. 2017;47:37–50. This paper evaluates the current methods used to analyze ES bundles, and provides an in-depth discussion on the strength and limitations of these approaches using an empirical case study. It also points to the importance of hypothesis-driven, as opposed to pattern detection, approaches to better understand the mechanisms and improve predictions on ES relationships. CrossRefGoogle Scholar
- 14.Lovett GM, Jones CG, Turner MG, Weathers KC. Ecosystem function in heterogeneous landscapes. Ecosystem function in heterogeneous landscapes. Springer; 2005. p. 1–4.Google Scholar
- 16.• Duarte GT, Santos PM, Cornelissen TG, Ribeiro MC, Paglia AP. The effects of landscape patterns on ecosystem services: meta-analyses of landscape services. Landsc Ecol. 2018;33:1247–57. A recent meta-analysis of 121 research articles that examines effects of landscape composition and configuration (e.g., percent natural cover, landscape connectivity, fragmentation, aggregation, and complexity) on five ES: pollination, pest control, water quality, disease control, and aesthetic value. CrossRefGoogle Scholar
- 17.•• Bennett EM. Research frontiers in ecosystem service science. Ecosystems. 2017;20:31–7. This paper highlights three key challenges in ES science: (1) nonlinearities, feedbacks and legacies in the sustainable and resilient provision of ES; (2) the role and interplay of ecological and social components in the ES provision; (3) co-design research with stakeholders for better decision-makings. CrossRefGoogle Scholar
- 22.•• Karp DS, Chaplin-Kramer R, Meehan TD, Martin EA, DeClerck F, Grab H, et al. Crop pests and predators exhibit inconsistent responses to surrounding landscape composition. Proc Natl Acad Sci. 2018;115:E7863–70. A synthesis of 132 studies and >6,700 sites challenges the long-held idea non-crop habitats can lead to win-wins with pest control, biodiversity and crop yields, and reveals that non-crop habitats does not consistently improve pest management, where local farming contexts may interact with surrounding landscape patterns to affect pest control ES. PubMedCrossRefGoogle Scholar
- 28.• Chaplin-Kramer R, Hamel P, Sharp R, Kowal V, Wolny S, Sim S, et al. Landscape configuration is the primary driver of impacts on water quality associated with agricultural expansion. Environ Res Lett. 2016;11:074012. Using a modeling approach, this paper reveals that landscape configuration (by controlling the same amount of habitat conversion) serves as the primary driver of water quality, and can override other physical factors such as soil type, slope and climate. CrossRefGoogle Scholar
- 31.• Lamy T, Liss KN, Gonzalez A, Bennett EM. Landscape structure affects the provision of multiple ecosystem services. Environ Res Lett. 2016;11:124017. This paper develops a multivariate framework to analyze the role of composition and configuration of land use/cover on the provision and bundling of ES, and found that the relative contribution of composition vs. configuration is ES-dependent. CrossRefGoogle Scholar
- 37.• Nicholson CC, Koh I, Richardson LL, Beauchemin A, Ricketts TH. Farm and landscape factors interact to affect the supply of pollination services. Agric Ecosyst Environ. 2017;250:113–22. This paper demonstrates the interactive effects of local farming management practices and landscape patterns on bee biodiversity and associated supply of pollination ES. CrossRefGoogle Scholar
- 44.Viana BF, Boscolo D, Mariano Neto E, Lopes LE, Lopes AV, Ferreira PA, et al. How well do we understand landscape effects on pollinators and pollination services? J Pollinat Ecol. 2012;7:31-41.Google Scholar
- 45.Boscolo D, Tokumoto PM, Ferreira PA, Ribeiro JW, dos Santos JS. Positive responses of flower visiting bees to landscape heterogeneity depend on functional connectivity levels. Perspect Ecol Conserv. 2017;15:18–24.Google Scholar
- 50.Rader RA, Bartomeus IB, Garibaldi LA, Garratt MPD, Howlett BG, Winfree RG, et al. Non-bee insects are important contributors to global crop pollination. Proc Natl Acad Sci. 2016;113:146–51.Google Scholar
- 55.•• Tscharntke T, Karp DS, Chaplin-Kramer R, Batáry P, DeClerck F, Gratton C, et al. When natural habitat fails to enhance biological pest control—five hypotheses. Biol Conserv. 2016;204:449–58. This paper lays out five hypotheses on when and why natural habitats may not enhance biocontrol ES, including (1) no natural enemies for pests in the region; (2) natural habitat better supports pest than enemies; (3) crop provides more resources for natural enemies than natural habitats; (4) insufficient amount, proximity, composition and configuration of natural habitats to support large population of natural enemies for controlling pests; (5) counteractive effects from agricultural practices. CrossRefGoogle Scholar
- 78.•• Motew M, Chen X, Carpenter SR, Booth EG, Seifert J, Qiu J, et al. Comparing the effects of climate and land use on surface water quality using future watershed scenarios. Sci Total Environ. 2019;693:133484. A comprehensive biophysical modeling study that compared the relative importance of climate vs. land use on surface water quality across different spatial scales. PubMedCrossRefPubMedCentralGoogle Scholar
- 105.MEA. Ecosystems and human well-being: synthesis. Washington, DC: Island Press; 2005.Google Scholar
- 109.• Cottet M, Vaudor L, Tronchere H, Roux-Michollet D, Augendre M, Brault V. Using gaze behavior to gain insights into the impacts of naturalness on city dwellers’ perceptions and valuation of a landscape. J Environ Psychol. 2018;60:9–20. This paper uses a novel approach and integrates technology to track eyeball movement for quantifying landscape perception as a cultural ES. CrossRefGoogle Scholar
- 116.• van der Plas F, Allan E, Fischer M, Alt F, Arndt H, Binkenstein J, et al. Towards the development of general rules describing landscape heterogeneity-multifunctionality relationships. J Appl Ecol. 2019;56:168–79. Combining evidence from model simulations and empirical data in Germany grasslands, this paper shows general principal that the heterogeneity in land-use intensity (LUI) could promote landscape multifunctionality. Google Scholar
- 121.•• Qiu J, Game ET, Tallis H, Olander LP, Glew L, Kagan JS, et al. Evidence-based causal chains for linking health, development, and conservation actions. BioScience. 2018;68:182–93. This study presents the foundational concepts and guidance of causal chains for linking different disciplines and sectors that can be adopted to elucidate the effects of actions (e.g., altering landscape patterns) on ecosystems and society through effects on ecosystem services. PubMedPubMedCentralCrossRefGoogle Scholar
- 131.•• Dee LE, Allesina S, Bonn A, Eklöf A, Gaines SD, Hines J, et al. Operationalizing network theory for ecosystem service assessments. Trends Ecol Evol. 2017;32:118–30. This paper provides an operationalize framework to use network theory and methods to understand the interlinkages among social-ecological drivers and ecosystem services. PubMedCrossRefPubMedCentralGoogle Scholar