Vol.: (0123456789) 1 3 Environ Monit Assess (2023) 195:815 https://doi.org/10.1007/s10661-023-11394-4 RESEARCH Future land‑use change predictions using Dyna‑Clue to support mosquito‑borne disease risk assessment Miarisoa Rindra Rakotoarinia · Ousmane Seidou · David R. Lapen · Patrick A. Leighton · Nicholas H. Ogden · Antoinette Ludwig Received: 1 October 2022 / Accepted: 15 May 2023 / Published online: 7 June 2023 © The Author(s) 2023 Abstract Mosquitoes are known vectors for viral diseases in Canada, and their distribution is driven by climate and land use. Despite that, future land-use changes have not yet been used as a driver in mosquito distribution models in North America. In this paper, we developed land-use change projections designed to address mosquito-borne disease (MBD) prediction in a 38 761 km2 area of Eastern Ontario. The landscape in the study area is marked by urbanization and inten- sive agriculture and hosts a diverse mosquito com- munity. The Dyna-CLUE model was used to project land-use for three time horizons (2030, 2050, and 2070) based on historical trends (from 2014 to 2020) for water, forest, agriculture, and urban land uses. Five scenarios were generated to reflect urbanization, agri- cultural expansion, and natural areas. An ensemble of thirty simulations per scenario was run to account for land-use conversion uncertainty. The simulation clos- est to the average map generated was selected to rep- resent the scenario. A concordance matrix generated using map pair analysis showed a good agreement between the simulated 2020 maps and 2020 observed map. By 2050, the most significant changes are pre- dicted to occur mainly in the southeastern region’s rural and forested areas. By 2070, high deforestation is expected in the central west. These results will be integrated into risk models predicting mosquito dis- tribution to study the possibility of humans’ increased exposure risk to MBDs. Keywords Land-use change · Dyna-CLUE · Scenario · Mosquitos · Eastern Ontario Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1007/ s10661- 023- 11394-4. M. R. Rakotoarinia (*) · P. A. Leighton  Département de Pathologie Et Microbiologie, Faculté de Médecine Vétérinaire, Université de Montréal, 3200 Sicotte, Saint-Hyacinthe, Québec J2S 2M2, Canada e-mail: miarisoar@gmail.com M. R. Rakotoarinia · P. A. Leighton · N. H. Ogden · A. Ludwig  Groupe de Recherche en Épidémiologie Des Zoonoses Et Santé Publique (GREZOSP), Faculté de Médecine Vétérinaire, Université de Montréal, 3200 Sicotte, Saint-Hyacinthe, Québec J2S 2M2, Canada O. Seidou  Department of Civil Engineering, University of Ottawa, 161 Louis Pasteur, Ottawa, ON K1N 6N5, Canada D. R. Lapen  Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, 960 Carling Ave, Ottawa, ON K1A 0C6, Canada N. H. Ogden · A. Ludwig  Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, 3190 Sicotte, Saint-Hyacinthe, Québec J2S 2M2, Canada http://crossmark.crossref.org/dialog/?doi=10.1007/s10661-023-11394-4&domain=pdf https://doi.org/10.1007/s10661-023-11394-4 https://doi.org/10.1007/s10661-023-11394-4 Environ Monit Assess (2023) 195:815 1 3 815 Page 2 of 20 Vol:. (1234567890) Introduction Over the last few years, projections of the range of pathogens, their reservoirs, and their vectors under global environmental change, have proven fruitful for informing on the epidemiology of different infectious diseases (Altizer et  al., 2013; Keeling & Rohani, 2011; Kraemer et  al., 2016; Vynnycky & White, 2010). It is possible to predict disease spread accord- ing to different scenarios and consider interventions to control disease spread or its impact. This is how, for example, vulnerable patients were identified and targeted for isolation measures during a Zika epi- demic in Martinique in 2016 (Cousien et al., 2019). Determinants of mosquito-borne diseases, one of the main types of vector-borne diseases (VBD), mostly include climate/weather and land use. In Canada, several arboviruses transmitted by mos- quitoes have become endemic. Climate is the most studied driver of the spread of these arboviruses geographically (Bartlow et al., 2019; Epstein et al., 1998; Lee et al., 2013; Reiter, 2001). However, the role of other drivers may be relevant and should also be accounted for in the modeling process, especially in projection approaches. Indeed, the elements gov- erning mosquito distribution are multifactorial; they include but are not limited to, aquatic habitat for lar- vae, the presence of flowers from plants that serve as food for adult mosquitoes, and the presence of vertebrate hosts that supply blood meals for gravid females to permit egg development. By consider- ing multiple drivers of VBD occurrence and spread, more realistic predictions of public health risk can be made (Keeling & Rohani, 2011; Vynnycky & White, 2010), and more efficient disease manage- ment actions can be designed. The effects of either climate or land-use changes on the future geographical distribution of mos- quito communities and associated VBD have been addressed independently in previous stud- ies (Altizer et  al., 2013; Bowden et  al., 2011; Lee et  al., 2013; Ogden et  al., 2014; Reiter, 2001). However, to our knowledge, few studies have explicitly investigated climate and land-use change implications together. Climate projections for a region are generally read- ily accessible and updated. But, this is not the case for simulations of land cover changes for a region of interest. Land-use change models are useful tools that can help project future land-use trajectories helping policymakers interpret causes and potential conse- quences of land-use dynamics on the target of interest (Verburg & Overmars, 2009; Verburg et  al., 2002). The range of disciplines using land-use projection can extend from economics (Sakayarote & Shrestha, 2019; Van der Sluis et al., 2019; Zhang et al., 2013) to ecology (Préau et  al., 2019; Trisurat et  al., 2010; Waddell et al., 2020). Each type of land-use modeling approach relies on different techniques (Meiyappan et al., 2014) that vary according to the research goal, geographical area, and data sources and types (Murray-Rust et al., 2014). Agent-based models, cellular automata, arti- ficial neural networks, economics-based models, and Markov chain approaches, for example, have been used in a dynamic land-use change capacity (Schro- jenstein Lantman et al., 2011). In this study, we used dynamic conversion of land- use and its effects (Dyna-CLUE), which generates land-use maps using scenario analysis. Dyna-CLUE is one of the different versions of the CLUE family of models (CLUE, CLUE-s, Dyna-CLUE, and CLUE- Scanner). It is among the most frequently and widely used land-use change models in the past decades (Das et al., 2019; Li et al., 2014; Price et al., 2017; Tizora et  al., 2018; Trisurat et  al., 2010; Verburg & Over- mars, 2009; Verburg et  al., 2002). Dyna-CLUE has proven to be broadly useful for decision-making in spatial management activities (Das et al., 2019; Wai- yasusri & Wetchayont, 2020). Our study region, eastern Ontario, Canada, land use is dynamic, and has been undergoing urbaniza- tion and intensive agricultural expansion for several decades. Czekajlo et  al. (2021) have shown that the average transition rate from natural to urban areas in the greater Ottawa region (eastern Ontario) can be as high as 5.8%. The average rate of change from agricultural to urban areas and from natural to agri- cultural areas in the greater Ottawa, Ontario, reaches 2.4% and 4%, respectively. Furthermore, the trends of agricultural change in Ontario are unique as it pos- sesses some of the most productive soils in Canada (Smith, 2015), accounting for 25% of Canada’s agri- cultural production. Several arboviruses that are transmitted by mos- quitoes are known to circulate endemically in Can- ada, especially in Ontario. West Nile Virus (WNV) was first detected in Ontario in 2002. Since then, Environ Monit Assess (2023) 195:815 1 3 Page 3 of 20 815 Vol.: (0123456789) Public Health Ontario (PHO) has initiated and main- tained surveillance for mosquitoes and mosquito- borne viruses revealing the presence of WNV and other arboviruses such as the Eastern Equine Enceph- alitis (EEEV), and Californian serogroup viruses (CSV) (Drebot, 2015) including Jamestown Canyon virus (JCV) and Snowshoe Hare virus (SSHV). These arboviruses circulate frequently in southeastern Can- ada where vector species include Aedes vexans and Culex pipiens-restuans (the main vectors of WNV), Culiseta melanura (the main vector for EEEV), and several potential vectors for CSV (belonging to the genera Ochlerotatus and Culiseta). There is a high number of mosquito species occurring in this region (67 species in Ontario) (Giordano et  al., 2015), and monitoring the evolution of these species in terms of interaction with their habitats (as determined by land use and climate), animal hosts and pathogens, may enhance our capacity to predict risk from arbovi- ruses. With a changing climate and, possibly land-use change, the risk of exposure to arboviruses is likely to increase (Beard et al., 2016; Franklinos et al., 2019; Patz et  al., 2008). Warmer temperatures associated with climate change can accelerate mosquito devel- opment, biting rates, and reduce the extrinsic incuba- tion of pathogens in the mosquito, while rainfall can impact occurrence of breeding sites for mosquitoes (Beard et  al., 2016; Franklinos et  al., 2019). Urban, agricultural areas, and natural habitats such as wood- lands and wetlands are novel ecosystems with varying capacity to support mosquito populations (Franklinos et  al., 2019; Norris, 2004; Ortiz et  al., 2022; Rako- toarinia et  al., 2022). Attempting to understand how these ecosystems influence composition and abun- dance of mosquitos is essential to understanding the ecology of mosquito species and predict risk from the diseases they transmit. This paper presents the first step of a project that aims to model and predict the combined effects of land-use change and climate change on the abun- dance of Culex pipiens-restuans, the main vector of West Nile virus in eastern Canada, and on the diver- sity of mosquitoes in Eastern Ontario. This first step comprises the development, simulation, and valida- tion of land-use change scenarios and maps that can then be combined with climate projections. The Dyna-CLUE model used in this study requires two main inputs: historical land-use change trends and spatial suitability equations. Historical land-use maps of the eastern Ontario region from 2014 to 2020 were used to parameterise the model to create possi- ble future land-use trends, according to different sce- narios for land-use change, for three future time peri- ods: 2030, 2050, and 2070. Methods Study region The study region (Fig. 1), covering an area of 38 761 km2, is located in eastern Ontario, Canada, in a mixed use but primarily agricultural landscape and includ- ing 12 counties: Prescott, Glengarry, Stormont, Dun- das, Russell, Carleton, Grenville, Lanark, Leeds, Ren- frew, Frontenac and Lennox, and Addington. The city of Ottawa is the major urban center in the study area. The eastern part of the study region is predominantly covered by agricultural land uses. Urban areas such as Ottawa and its suburbs are located in the northern areas. The western part is dominated by natural areas (forest and water). According to the Köppen-Geiger climate classification (Kottek et  al., 2006), eastern Ontario is characterized by a climate with snowfall, fully humid, and warm summers. Modeling approach Dyna-CLUE is a spatially explicit and scenario- based modeling approach that generates future land-use maps based on historical trends of a given region’s existing land-use classes. It integrates demand-driven changes in land area with locally determined conversion processes (Verburg & Over- mars, 2009). It comprises a non-spatial module and a spatial module. The non-spatial module calcu- lates the change of areas for all classes of land use from 1 year to another, called the demand. Differ- ent approaches can be used to calculate the demand ranging from simple linear trend extrapolations to complex economic models and need to be decided by the user for its specific situation (Verburg et al., 2002). The choice of the method calculation strictly relies on the nature of the most important land-use conversions occurring in the study area and the considered scenarios. In our study, we employed parsimonious linear relationships (lin- ear regression) between the surface areas of the Environ Monit Assess (2023) 195:815 1 3 815 Page 4 of 20 Vol:. (1234567890) two main land uses that undergo the most change (urban and agriculture) and time (years). The fitted relationships were used to calculate future land-use demand starting from the initial start year (2014). These demands are translated into land-use modi- fication in different areas starting from the initial state year (2014) using the Dyna-Clue spatial mod- ule. The spatial module allocates land-use classes on a pixel-by-pixel basis. The allocation process is fully described in (Verburg et  al., 2006) and sum- marized in Fig. 2. The calculation of the historical land-use trends (evolution of the area expressed in hectare corresponding to each land-use class over time) were conducted using logistic regression in R, version 4.0.2 (Team, 2020), the spatial module was carried out in the Dyna-CLUE software, and the simulation output was displayed in ArcMap 10.7.1 (ESRI). The full description of the Dyna- CLUE model is given by (Verburg & Overmars, 2009). The model includes several parameters pre- sented hereafter. Land-use data Agriculture and Agrifood Canada (AAFC) regularly delivers annual crop inventory maps 30-m spatial resolution (Agriculture & Agri-Food Canada Annual Crop Inventory, 2021). The annual crop inventory data between 2014 and 2020 were extracted from https:// www. canada. ca/ en/ trans paren cy/ open. html. The maps were resampled to a 330-m resolution to respect the maximum number of pixels allowed in the Dyna-CLUE model (Verburg & Overmars, 2009; Verburg et al., 2004). Fig. 1 Location of the study region in the eastern region of Ontario province. Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com https://www.canada.ca/en/transparency/open.html Environ Monit Assess (2023) 195:815 1 3 Page 5 of 20 815 Vol.: (0123456789) Based on previous studies, we assumed that water, forest, agriculture, and urban land uses were impor- tant with respect to mosquito abundance and diversity (Moua et al., 2021; Rakotoarinia et al., 2022). Water is an essential requirement for egg and larval develop- ment (Medlock et  al., 2018; Silver, 2007); however, the type of optimal breeding sites filled with water is not the same for every mosquito species. Some spe- cies prefer natural breeding sites while others prefer artificial ones (Medlock et  al., 2018; Silver, 2007). Agricultural development and urbanization are also common drivers of mosquito abundance or presence/ absence in literature by favoring, or not, their pres- ence and their development (e.g., deforestation and water management) (Norris, 2004). As an example, Anopheles quadrimaculatus and Culex erraticus affinity with agricultural wetlands is well established (Botello et al., 2013) and Culex pipiens is also often described as more abundant in urban environments (Andreadis et al., 2001; Jacob et al., 2009). Similarly, some species are restricted to forested areas such as Ochlerotatus triseriatus (Reiskind et  al., 2017). It would have been more relevant to separate “wetland” pixels from “water” given their role in mosquito ecol- ogy but unfortunately wetlands are not spatially fre- quent enough to constitute a distinct class of their Fig. 2 Flowchart of the Dyna-CLUE approach used in this study Table 1 Reclassification of land-use classes given in (Agricul- ture & Agri-Food Canada Annual Crop Inventory, 2021) Original land uses (Agriculture & Agri- Food Canada Annual Crop Inventory, 2021) Reclassified land uses (this study) Water Water Exposed land/Barren Agriculture Urban/developed Urban Greenhouses Agriculture Shrubland Forest Wetland Water Grassland Agriculture Pasture/forages Agriculture Barley Agriculture Oats Agriculture Winter wheat Agriculture Spring wheat Agriculture Corn Agriculture Soybean Agriculture Beans Agriculture Other crops Agriculture Forest (undifferentiated) Forest Coniferous Forest Broadleaf Forest Mixed wood Forest Environ Monit Assess (2023) 195:815 1 3 815 Page 6 of 20 Vol:. (1234567890) own (< 5% of the total surface). Thus, a reclassifica- tion was conducted by assigning the original land-use class designations to one of these chosen classes. The correspondence between the original and new classes is represented in Table 1. Conversion rules The conversion settings are implemented in two forms: the conversion matrix and the elasticity. Conversion matrix The conversion matrix (Table 2) is a set of conversion rules that allows the maximization of the probability of a grid pixel to be devoted to a given land-use class at a specific location (Verburg & Overmars, 2009; Verburg et  al., 2002). It indicates which conversions are possible for each land-use class. Code “0” indicates that the conver- sion is not allowed for the specific land-use class and code “1” indicates that the conversion is allowed. For example, conversion from agriculture to water is not possible. In contrast, conversion from agriculture to urban is allowed. Code “1” is sometimes followed by the “time to conversion,” defined as the number of years a given land use must remain stable before it is con- verted into another class. We assumed that agricul- ture remains stable for 15 years before any possible conversion to urban (Czekajlo et  al., 2021; Smith, 2015) and 10  years before any possible conver- sion to forest (Smith, 2015). The class forest was assumed to remain stable for 10  years before any possible conversion to agriculture or urban (Czeka- jlo et al., 2021). Elasticity The conversion elasticity is related to the resistance of a given land-use class to change (Verburg & Overmars, 2009; Verburg et  al., 2002). It measures the cost of conversion of a specific land-use class to another and is applied to the loca- tions where the land-use class is found at time t. The land uses with high capital investment, such as urban areas, will be more resistant to conversion (Duran- ton et  al., 2015; Environment and Climate Change Canada, 2021; Nuissl & Siedentop, 2021). Once an area is urban, it usually stays that way, but the exist- ing settlement areas grow outward, encroaching on surrounding areas such as forests, agricultural lands, and other natural areas. These pixel areas will have high elasticity values. Thus, high elasticity values are primarily applied to high-cost land-use classes such as urban areas with residential locations or planta- tions with permanent crops. In contrast, low values may be applied to grassland, bare and similar land- use classes. The values of the relative elasticity range from 0 (easy conversion) to 1 (irreversible change). It is set to 1, 0.4, 1, and 0.7 for water, agriculture, urban, and forest, respectively. The determination of the elasticity values in this study was based on the implementation of several tests in which different val- ues of elasticities were examined. The values where simulated maps were comparable to reference maps were selected. The selected values were consistent with those from relevant publications (Verburg et al., 2002; Xu et al., 2013). Logistic regression 1: location suitability The location suitability determines the probability of a specific land-use class occurring at a given location. The conversions are expected to occur at locations with the highest “preference” for the specific land use at that moment in time (Verburg & Overmars, 2009; Verburg et  al., 2002). This “preference” is based on a stepwise logistic regression. This analysis associ- ates the probability of occurrence of a given land- use class to geographic and demographic conditions (driving factors). Based on (El-Khoury et al., 2014), seven spatially explicit driving factors were selected, including one demographic variable (average popula- tion density) and six geographical variables (distance to the nearest cities, distance to the nearest major cit- ies, distance to the nearest small cities, distance to the nearest major roads, distance to the nearest paved roads, and distance to water). The driving factors were retrieved from different sources (Table 3). The logistic regression equation is: Table 2 Conversion matrix and time to conversion Land-use class Water Agriculture Urban Forest Water 1 0 0 0 Agriculture 0 1 115 110 Urban 0 0 1 0 Forest 0 110 110 1 Environ Monit Assess (2023) 195:815 1 3 Page 7 of 20 815 Vol.: (0123456789) where Pi is the probability of a grid pixel (i) for the occurrence of the considered land-use n, Xs are the driving factors, and βs are the regression coefficient of each driving factor. Logistic regression 2: neighborhood suitability The spatial neighborhood of the land use in a given pixel can partially influence the conversion of this land use (Verburg & Overmars, 2009; Verburg et al., 2002). If the neighborhood function is enabled in Dyna-CLUE, it places a preferential weighting on pixels within the neighborhood of the land-use class to allocate new pixels to that land-use class. The neighborhood suitability is also estimated by a step- wise logistic regression using the land-use class and a set of location parameters called enrichment factors log ( P loc,i 1 − P loc,i ) = �0 + �1X1,i + �1X2,i +⋯ + � n X n,i (F) (Verburg et  al., 2004). The enrichment factor is defined by the occurrence of a land-use class in the location’s neighborhood relative to the occurrence of this class in the whole study area as follows: where Fi,k,d is the enrichment of neighborhood (d) of a location ( i) for land-use class (k).nk,d,i is the number of pixels of land-use class (k) in the neighborhood with size (d) of pixel (i)nd,i is the total number of pix- els in the neighborhood Nk is the number of pixels with land-use class (k) in the study area. N is the total number of pixels in the study area. Similar to the location suitability, the equation defines the logit model corresponding to the neigh- borhood suitability: F i,kd = n k,d,i∕nd,i N k ∕N Table 3 Data sources from which the driving factors for land-use suitability were derived These drivers are illustrated in the Supplemental Fig. 1 Variables Data Providers Geographical cover Links Geographical • Distance to the nearest cities • Distance to the nearest major cities • Distance to the nearest small cities Dissemination areas (DA) of the 2016 census StatCan Canada https:// www12. statc an. gc. ca/ census- recen sement/ 2016/ dp- pd/ index- fra. cfmCensus subdivision of 2018 (CSD) StatCan Canada • Distance to the nearest major roads • Distance to the nearest paved roads Road network of Open Street Map (OSM) Open Street Map Canada http:// downl oad. geofa brik. de/ north- ameri ca/ canada/ ontar io. html National Highway System (NHS) National Highway System Global https:// ouvert. canada. ca/ data/ fr/ datas et/ 3d282 116- e556- 400c- 9306- ca1a3 cada7 7f Distance to water Annual crop inven- tory Agriculture and Agri-food Canada Canada https:// www. agr. gc. ca/ atlas/ apps/ metri cs/ index- en. html? appid= aci- iac Socio-economic Average population density Population density of 2018 Socio-economic data and application center (SEDAC), NASA Global https:// sedac. ciesin. colum bia. edu/ data/ set/ gpw- v4- popul ation- densi ty- rev11/ data- downl oad https://www12.statcan.gc.ca/census-recensement/2016/dp-pd/index-fra.cfm https://www12.statcan.gc.ca/census-recensement/2016/dp-pd/index-fra.cfm https://www12.statcan.gc.ca/census-recensement/2016/dp-pd/index-fra.cfm https://www12.statcan.gc.ca/census-recensement/2016/dp-pd/index-fra.cfm http://download.geofabrik.de/north-america/canada/ontario.html http://download.geofabrik.de/north-america/canada/ontario.html http://download.geofabrik.de/north-america/canada/ontario.html http://download.geofabrik.de/north-america/canada/ontario.html https://ouvert.canada.ca/data/fr/dataset/3d282116-e556-400c-9306-ca1a3cada77f https://ouvert.canada.ca/data/fr/dataset/3d282116-e556-400c-9306-ca1a3cada77f https://ouvert.canada.ca/data/fr/dataset/3d282116-e556-400c-9306-ca1a3cada77f https://ouvert.canada.ca/data/fr/dataset/3d282116-e556-400c-9306-ca1a3cada77f https://ouvert.canada.ca/data/fr/dataset/3d282116-e556-400c-9306-ca1a3cada77f https://www.agr.gc.ca/atlas/apps/metrics/index-en.html?appid=aci-iac https://www.agr.gc.ca/atlas/apps/metrics/index-en.html?appid=aci-iac https://www.agr.gc.ca/atlas/apps/metrics/index-en.html?appid=aci-iac https://www.agr.gc.ca/atlas/apps/metrics/index-en.html?appid=aci-iac https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-rev11/data-download https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-rev11/data-download https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-rev11/data-download https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-rev11/data-download https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-rev11/data-download Environ Monit Assess (2023) 195:815 1 3 815 Page 8 of 20 Vol:. (1234567890) In this present work, enrichment factors Fi,k,d were calculated for five different distances from the central pixel that were used by El-Khoury et  al. (2014); for their study region is part of ours: d = 330  m (1 × 1), 660 m (3 × 3), 990 m (5 × 5), 4950 m (29 × 29), and 9900 m (59 × 59), reminding that the size of a pixel is 330 m × 330 m. We have used square rings at the dis- tance d from the central grid pixel as neighborhoods as proposed by Verburg et al. (2004). Land-use requirements (demands) Dyna-CLUE projects the evolution of land use based partially on past land-use trends. The calculation of the land-use requirements is based on the extrapola- tion of land-use change trends from the recent past to the near future for the baseline scenario (see scenario log ( P enr,i 1 − P enr,i ) = �0 + �1F1,i + �2F2,i +⋯ + � n F n,i settings section). The time series of the historical land-use maps were generated from the data of the annual crop inventory from 2014 to 2020 (Fig. 3), and the trend was calculated as follows: Agriculture: a linear trend was estimated for agri- culture between 2014 and 2020. Urban: urban areas vary significantly over time from the observed data. For this reason, the trend related to urban was formulated as an average of the annual number of pixels of this class between 2014 and 2020. A straight horizontal line was con- sidered the baseline trajectory of this class, assum- ing no urban growth. Forest: this specific land-use class was estimated as any area which is not agriculture, water, or urban. Water: similar to the forest class, no trend line was developed for the class “water” as it was assumed that no change would apply to this class. The cor- Fig. 3 Historical trends between 2014 and 2020 derived from remote sensing data (annual crop inventory) (Agriculture & Agri- Food Canada Annual Crop Inventory, 2021) Environ Monit Assess (2023) 195:815 1 3 Page 9 of 20 815 Vol.: (0123456789) responding areas remain unchanged throughout the study period. Scenarios settings Simulations are based on scenarios that need to be con- sidered and decided by the user for the specific context of study (Verburg et al., 2002). To formulate our sce- narios, the evolution of all land classes was driven by the urban and the agricultural classes (El-Khoury et al., 2014; Price et al., 2017). First, we used the 2014 map (Fig. 4) as a starting point to simulate a 2020 map to validate our method. Then we used the 2020 map as an initial map to simulate future land uses. Several scenarios were considered to capture the uncertainty of the trajectories of the abovementioned driving forces. We have established scenarios that assume that the currently observed trend for agricul- ture will remain unchanged or increase in the future and that the urban areas will remain stable or increase over time. Other scenarios consist of taking into account possible changes in human behavior regard- ing urban densification, and awareness of the impor- tance of the conservation of natural areas. These scenarios predict little or no future increase in agri- cultural and urban areas. Agricultural scenarios were developed as follows. The trend of observed changes in historical land-use demand, between 2014 and 2020, was obtained by linear regression fitted on the historical values (2014 to 2020) of the given land-use area. The produced linear relationship was used to extrapolate the areas beyond 2020 to 2070. The obtained scenario was con- sidered the base scenario for agriculture. Two addi- tional trajectories of the agricultural area correspond- ing to a slowdown in agricultural intensification are proposed, one of which foresees a slowdown of 50% and the other of 75% (Fig. 5). Fig. 4 Observed changes between 2014 and 2020 in the eastern Ontario region Environ Monit Assess (2023) 195:815 1 3 815 Page 10 of 20 Vol:. (1234567890) An average of the total annual pixels over the 7 years of observed data was calculated for the urban class from which a straight line (of slope 0) was drawn, constituting the baseline scenario linked to urban areas. Two other trajectories assuming yearly increases of 1.5 (moderate urban growth) and 3% (strong urban growth) from this horizontal line were proposed (Czekajlo et al., 2021) (Fig. 6). Five final scenarios were developed based on dif- ferent combinations of the trajectories predicted for Fig. 5 Observed trends and scenarios for the agriculture land-use class Fig. 6 Averaged area of urban area between 2014 and 2020 maps (slope 0) and scenarios (increases of 1.5% and 3%) for the urban land-use class Environ Monit Assess (2023) 195:815 1 3 Page 11 of 20 815 Vol.: (0123456789) the two classes (urban and agriculture) above to sim- ulate land-use changes in the eastern Ontario region based on the original land-use map of 2014. These scenarios represent different urbanization degrees or agricultural expansion, with accompanying areas that are protected from development. The forest class absorbs the changes resulting from the modifications of the urban and agricultural classes. Scenario 1: Normal development This first sce- nario reflects a normal urban and agricultural devel- opment, corresponding to a moderate increase of 1.5% in the urban class combined with the base sce- nario for agricultural growth. In other words, urbani- zation occurs at a moderate speed, and agricultural practices continue to increase at the current rate with- out any restriction. Scenario 2: Reduced agriculture expansion For this scenario, urbanization continues to evolve nor- mally (slight increase), but agricultural growth is slowing down (− 50% from baseline). This scenario assumes measures to limit climate change and con- serve natural capital, and perhaps addresses a chang- ing workforce away from farming. Scenario 3: Major urban development A This scenario is associated with a significant increase in urban development at the expense of agricultural land. Strong urbanization (3%) but a drastic slowdown in agricultural growth (− 75%) are expected. Urban areas continue to spread at the current rate to meet the popu- lation’s needs, mainly at expense of agricultural areas. Scenario 4: Major urban development B This scenario reflects a major increase in urban develop- ment and normal increases in agricultural develop- ment at the expense of natural land. High urbaniza- tion (3%) and a continuation of agricultural spread at expense of forested areas. Scenario 5: Protection of natural capital In this scenario, stabilization of urban and agricultural devel- opment occurs, assuming a higher ecological aware- ness and urban intensification vs. extensification. Urban areas stop sprawling, and agricultural growth is strongly reduced (− 75%). This scenario could assume strong measures to limit (mitigate) climate change and reduced extensification of anthropogenic activities. Modeling assessment Assessment of the logistic regressions The goodness of fit of location suitability and the neighborhood equations derived from the stepwise logistic regressions was assessed by the values of ROC (receiver operator characteristics) (Pontius Jr & Schneider, 2001; Smith, 2015). This metric com- pares the observed values that are binary data over the whole range of predicted values. High area values under the ROC curve (AUC) indicate a good accu- racy of the model. Assessment of the land-use model In order to assess model accuracy, the 2020 simu- lated maps were compared to the observed 2020 map (also called the observed map). A visual vali- dation was adopted and was confirmed with two statistical indices (the overall accuracy and the Kappa coefficient: Foody, 2006; Tizora et al., 2018; Wang & Sun, 2016; Zhang et  al., 2016)). The sta- tistical approach aims to assess how well the simu- lated maps agreed with the observed map in terms of quantity and location of pixels in each land-use class. High overall accuracy and Kappa coefficient values indicate a good spatial agreement between the observed map and the simulated map. We assumed that the matrices give unbiased statistics related to the relationship between the reference map and the simulated map (Pontius & Millones, 2011). For the scenarios closest to observed for the year 2020, we computed additional performance measures, includ- ing errors of omission and commission and Pro- ducer’s and User’s accuracy (Pontius et  al., 2008). Errors of omission refer to pixels that were omitted from the correct class in the classified map, whereas errors of commission are computed from review- ing the classified pixels for incorrect classifications. The smaller their values are, the better the model fit. Producer’s and User’s accuracy denote the accuracy from the point of view of the map maker and the map user, respectively. The higher their values are, the better the model fit. Environ Monit Assess (2023) 195:815 1 3 815 Page 12 of 20 Vol:. (1234567890) Model output choice Given the stochastic component of the Dyna-Clue model occurring in the allocation process, we made the decision to run 30 simulations per scenario, an important consideration not previously conducted. A reference map was created by performing a major- ity analysis of the 30 simulations per scenario. In the selection of the simulation to be presented in the final results, we performed statistical analyses through a confusion matrix comparing each of the 30 simula- tions to the reference map resulting from the majority analysis. Results Modeling Logistic regression 1: location suitability Results from the suitability logistic stepwise regres- sion are reported in Table  4. This table shows that not all driving factors were included in the regres- sion equations and each factor contributed differently depending on the land-use class. For all land-use classes, almost all driving factors were important. Population density, distance to major roads, and dis- tance to paved roads were positively correlated with agriculture. In contrast, areas next to cities were iden- tified as presence determinants for agriculture. The population density positively influenced the urban class, and its area increased next to major roads and small cities. However, it was negatively related to distance to small cities and major roads. The spa- tial distributions of the five land-use classes were best explained by the selected demographic and geographic driving factors as indicated by the ROC (receiver operator characteristics) values. High, mod- erate, and low values of the area under the ROC curve (AUC) were found for agriculture (0.95), urban land (0.79), and forest (0.69). Logistic regression 2: neighborhood suitability Neighboring effects showed that each class of land use significantly influences the suitability of its neighborhood for water, agriculture, urban, and forest classes for at least one neighborhood size (Table 5). The presence of water in the neighborhood (330 m) is an explanatory factor for each of the other classes. For example, the presence of water at less than 330 m Table 4 Logistic regression coefficients and intercepts for the location suitability models for agriculture, urban, and forest land-use classes Driving factors Agriculture Urban Forest (Intercept) − 1.36400000 − 4.18500000 − 0.52050000 Density 0.00011690 0.00042490 0.00003231 Distance to nearest cities -0.00002717 0.00234400 − 0.00012710 Distance to major cities − 0.00008161 0.00003434 − 0.00001661 Distance to small cities − 0.00004075 − 0.00239100 0.00016430 Distance to major roads 0.00010500 − 0.00002077 0.00003592 Distance to paved roads 0.00125800 0.00015580 − 0.00011440 Distance to water 0.00087330 0.00229600 ROC values 0.95 0.79 0.69 Table 5 Logistic regression coefficients and intercepts for the neighborhood suitability equation for agriculture, urban, and wooded land-use classes Suitability factors Water Agriculture Urban Forest (Intercept) − 5.506903 − 5.253116 − 6.37608 − 5.697454 Water 330 m 1.097180 0.145411 0.25576 0.405746 Agriculture 330 m 0.286562 1.786378 0.67688 0.626255 Urban 330 m 0.070242 0.094928 0.30187 0.100921 Forest 330 m 1.436741 1.027627 1.17628 5.328474 ROC values 0.93 0.97 0.98 0.95 Environ Monit Assess (2023) 195:815 1 3 Page 13 of 20 815 Vol.: (0123456789) strongly increases the likelihood of having forests compared to the likelihood of having agriculture or urban land uses. ROC values presented in Table 5 also showed that the models were reasonable (ROC > 0.9). Based on these values, the prediction was satisfactory for all land-use classes. Land-use changes simulations according to the five scenarios (2030, 2050, and 2070) Simulated maps for 2030, 2050, and 2070 for each of the five scenarios are displayed in Fig. 7. Compared to the observed map of 2020, the simulated maps of 2030 for all five scenarios did not display substantial changes except for scenarios 2 and 4 (reduced agri- culture expansion and major urban development B). Changes became more visible from the year 2050 and after for all scenarios. For three scenarios (reduced agriculture expansion, major urban development A, and the protection of natural capital), the results do not reveal apparent changes in the patterns of each land-use class, especially for 2030 and 2050. In 2070, there will be a moderate expansion of agricultural areas at the expense of the forested regions for these three scenarios. Compared to normal development and the major urban development B scenarios, these three scenarios assumed less demand for agriculture leading to higher remaining forested land and natural areas in the coming years. The results of the normal development (scenario 1, current agricultural growth rate and normal urban development with an increase of 1.5%) and the major urban development B (scenario 4, urban and agricul- tural spread to the detriment of forested areas) sce- narios showed different land-use patterns, especially in the years 2050 and 2070. A more pronounced and progressive loss of wooded areas was observed in the central west of the study region, which were mostly converted into agricultural lands between 2030 and 2070. In 2070, the patch of forest has substantially decreased by 50% for both scenarios. These two sce- narios also saw the most significant increase in the agricultural class in 2070 reaching 147% (Table  6). An increase of urban areas around 2070 demonstrates the strong influence of urban neighborhoods. How- ever, agricultural areas expand rapidly in the major Fig. 7 Land-use patterns in 2030, 2050, and 2070 simulated by the Dyna-CLUE model for eastern Ontario, Canada. Nor- mal development: urbanization continues at a moderate rate, and agricultural practices continue to increase at the current rate. Reduced agriculture expansion: this scenario assumes a slowdown in urbanization and agricultural practices. Major urban development A: urban areas continue to spread at the current rate at expense of agricultural areas. Major urban development B: urban areas continue to spread out as well as agricultural areas to the detriment of forest areas. Protection of natural capital: urbanization becomes stable although the pop- ulation continues to increase. Agricultural practices continue to increase but in a moderate way Environ Monit Assess (2023) 195:815 1 3 815 Page 14 of 20 Vol:. (1234567890) development B (scenario 4, urban and agricultural spread to the detriment of forested areas), resulting in a more substantive forest loss. In contrast, around 2070, the deforestation trend around greater Ottawa is expected to reverse. Forested areas are predicted to appear around the city of Ottawa at expense of agri- culture for both scenarios (scenarios 1 and 4). None of the five scenarios has shown a substantial change in the urban areas (Table 6), even for scenarios that assumed higher amounts of urban class increase. Thus, the total urban area remained quite stable, and the percentage of change was comparable between the five scenarios (an increase of 102.2%). Enlargements of Fig.  7 for different parts of the study area are shown in Supplemental Fig. 2. Modeling assessment The statistical comparison derived from the pixel-to- pixel confusion matrix of the simulated maps of 2020 and the observed map of the same year reference map suggests a relative agreement between the two sets of maps (Table 7). The accuracy was higher for normal development with a Kappa coefficient of 0.98, reflect- ing the continuation of the historical trend observed from 2014 to 2020. The other scenarios showed lower accuracy values as they assumed different tra- jectories of land use compared to the observed trend. The most insufficient accuracy corresponds to the reduced agriculture expansion scenario (Kappa coef- ficient = 0.372). The Kappa coefficient was similar for the three remaining scenarios (major urban develop- ments A and B, and protection of natural capital) with a value of 0.51. The normal development scenario is then consid- ered the baseline scenario reflecting the continuation of the observed trends. As such, we computed addi- tional statistics (error of omission, error of commis- sion, User’s and Producer’s accuracy) resulting from the 30 simulations to denote the spatial accuracy (Table 8). Omission and commission errors were less than 4%. Only 4% of the 2020 real map agriculture class was omitted from the classified map, and 1% of the urban class was incorrectly classified. Moreover, Table 6 Land-use changes across scenarios for 2070 (resulting from the majority analysis) and percentage of change compared to the 2020 observed map Scenarios Agriculture (ha) Urban (ha) Forest (ha) 2020 real map 828,195.39 144,989.46 2,434,503.06 Normal development 2,044,727.63 (+ 146.89%) 149,293.97 (+ 102.97%) 1,200,166.23 (− 50.7%) Reduced agriculture expansion 1,440,705.09 (+ 73.96%) 149,293.97 (+ 102.97%) 1,804,188.78 (− 25.89%) Major urban development A 1,138,693.81 (+ 37.49%) 150,405.34 (+ 102.74%) 2,105,088.68 (− 13.53%) Major urban development B 2,044,727.63 (+ 146.89%) 150,405.34 (+ 102.74%) 1,199,054.86 (− 50.75%) Protection of natural capital 1,138,693.81 (+ 29.38%) 148,182.60 (+ 102.2%) 2,107,311.42 (− 13.44%) Table 7 Overall accuracy, Kappa coefficient, and other accuracy statistics of the simulated maps of the five scenarios Scenarios Statistics Minimum Median Average Standard deviation Maximum Normal development Overall accuracy 0.9906 0.9909 0.9909 0.0001 0.9912 Kappa coefficient 0.9827 0.9831 0.9831 0.0002 0.9837 Reduced agriculture expansion Overall accuracy 0.6439 0.7123 0.7015 0.0238 0.7244 Kappa coefficient 0.3720 0.4590 0.4460 0.0301 0.4784 Major urban development A Overall accuracy 0.7379 0.7381 0.7381 0.0001 0.7384 Kappa coefficient 0.5100 0.5105 0.5105 0.0003 0.5111 Major urban development B Overall accuracy 0.7377 0.7381 0.7381 0.0001 0.7383 Kappa coefficient 0.5097 0.5105 0.5105 0.0003 0.5110 Protection of natural capital Overall accuracy 0.7379 0.7380 0.7380 0.0001 0.7384 Kappa coefficient 0.5100 0.5104 0.5104 0.0003 0.5111 Environ Monit Assess (2023) 195:815 1 3 Page 15 of 20 815 Vol.: (0123456789) the User’s and Producer’s accuracies for all classes were more than 99%, indicating a very good model simulation. Discussion This study resulted in projected land-use maps for the Eastern Ontario region to year 2070, that differed according to scenarios for land-use change. These results provide possible future land-use projections, but, of course they should be considered with caution particularly in the context of socio-economic and/ or political upheavals (Bucała-Hrabia, 2017; Price et al., 2017), such as we have seen recently (e.g., the COVID-19 pandemic, war in Ukraine). Different sce- narios provided snapshots of possible changes under more extreme and less extreme drivers of land-use change. The more extreme scenarios (normal develop- ment and major urban development B) mainly predict an extension of the agricultural zones into the for- est zones in the center of our study area, stretching towards the west of the study area. Whereas for the less extreme scenarios, changes will occur, but they are less pronounced and will be more visible in 2070. Extensification of agriculture is also predicted, but it will be concentrated in the central areas of the study area. The western forested areas will remain intact. In all the types of scenarios established in this study, the evolution of the urban class remains relatively stable, even assuming increases of this class in cer- tain demand. Urban sprawl is expected, especially in 2070, but this sprawl remains condensed around existing cities. We suspect that this small change in urban areas is due to the low slope linked to urban growth compared to the agricultural growth scenar- ios. However, the territories occupied by urban areas are a minority in comparison with agricultural areas. To balance this observation, predictions related to the expansion of the urban environment may be less reliable than those of other land-use types because of the assumptions made about the historical trend of the “urban” class. As a reminder, urban areas varied significantly over time from the observed data due to possible bias (ex. change of rules for assigning urban pixels in the annual crop inventories). And we made the decision to use the average value of the num- ber of urban pixels as the historical trend. It would be important to consider other data (such as aerial photography) to improve predicting of the urban environment trend as this type of land use is known to be favorable for certain species of mosquitoes of major interest for public health such as Culex pipiens- restuans and Aedes albopictus. To our knowledge, there are no data of better quality than those of the annual crop inventories and which are available over a reasonable historical period to serve as input to the Dyna-Clue model. In this study, the limited use of historical data was due to attempts to maintain consistency in data sources and disposition. We acknowledge that artifi- cial land-use trends can result from use of different data of differing sources, resolution and types when determining historical trends. Data from annual crop inventories provide some reliability in the classifica- tion of agricultural pixels, and the study area is pre- dominantly agricultural. This is, therefore, important since the “agricultural” class represents an impor- tant determinant for mosquitoes. Yet it is known, for example, that Anopheles quadrimaculatus, Culex erraticus and Psorophora columbiae are associated with temporary wetlands present in agricultural envi- ronments (Botello et al., 2013; Kovach & Kilpatrick, 2018), and that Culex tarsalis and C. quinquefascia- tus can breed in agricultural habitats (such as rice Table 8 Accuracy assessment results of the 2020 simulated map corresponding to the normal development scenario Class Ground truth (ha) Simulated (ha) Omission Commission User’s accuracy Producer’s accuracy Water 5,270,034 5,270,034 0 0 1 1 Agriculture 8,793,041.77 9,186,683 0.04 0 1 0.96 Forest 27,405,197.23 27,011,556 0 0.01 0.99 1 Urban 1,599,257 1,599,257 0 0 1 1 Environ Monit Assess (2023) 195:815 1 3 815 Page 16 of 20 Vol:. (1234567890) fields) (Chuang et  al., 2011; Kovach & Kilpatrick, 2018). Thus, even if we had moderate confidence in the classification of urban pixels, the data sources we used remain relevant. The links that we advance between land-use projec- tions and the abundance and diversity of mosquitoes remain speculative in the context of this work. For more details on this subject, it will be necessary to refer to the next stage of this study which consists of modeling and predicting the combined effects of land- use change and climate change on the abundance of Culex pipiens-restuans and the diversity of mosquitoes in Eastern Ontario. Many studies have studied habitat preferences of Culex pipiens-restuans and results are not converging. However, deforested areas appear to be positively associated with this species in some stud- ies (Gardner et  al., 2014; Rakotoarinia et  al., 2022). For this reason, we can expect in this future work that the more extreme scenarios (normal development and major urban development) are likely to increase the numbers of these mosquito species as some mosquito species are more favored in deforested areas, and in agricultural areas. As discussed a priori, agricultural areas can also offer conditions conducive to the devel- opment of other mosquito species (presence of irriga- tion, surrounding wetlands) and can potentially serve as major producers of mosquito vectors of WNV, for example, C. tarsalis. This study underlines the rel- evance of focusing more on these types of landscapes in order to better understand their influence on mosqui- toes and the diseases they transmit. In order to respect the software constraints, the spatial resolution of model input/output was 330  m. Although the spatial resolution of 330 m would not ade- quately simulate changes in mosquito “micro-habitats” (Okogun et al., 2003; Roberts & Irving-Bell, 1997), the home range of certain mosquito species such as Culex pipiens-restuans, for example, is 1 km (Verdonschot & Besse-Lototskaya, 2014). Hence, we did not consider this 330-m resolution a considerable constraint overall. It is also important to note that the models we used performed well, ensuring their applicability to other research agendas. The assessment of the agree- ments for specific land-use classes between simulated land-use maps and observed maps, measures of accu- racy, reveal a good agreement. Although the choice of drivers could be more complicated (El-Khoury et  al., 2014), the results of the suitability modeling suggest that the demographic (population density) and geographical (distances to cities, to roads, and to water) driving factors have good explanatory power for the presence of agriculture, urban, and forested areas. An additional novelty of our approach is the use of a probabilistic approach in the land-use projections. The land-use projection algorithm in Dyna-CLUE is partly probabilistic as some parameters are sampled in a range provided by the user, meaning that two runs from the same initial map will lead to projec- tions that are slightly different. To account for the randomness in the return, 30 projections were gener- ated and a majority analysis was used to convert the ensemble of projections into a representative map for each time horizon. We are not aware of a land-use projection study which used a similar approach. Conclusion In this study, we set five land-use demand scenarios to project future patterns of water, agriculture, urban, and forestland covers (2030, 2050, and 2070) in eastern Ontario, Canada. The suitability and the neighborhood characteristics were estimated by using stepwise logistic regression. The methodology was validated by statisti- cal comparison between simulated maps and reference maps through a concordance matrix. By applying a new approach to control the uncertainties generated by the random component of the Dyna-CLUE model, which consists of generating several simulations, we could choose maps that adequately illustrate each scenario for future predictions. Analysis of land-use changes in eastern Ontario has reflected two major land conver- sions: agriculture and urbanization. Results show that the normal development scenario (scenario 1) and the major urban development B (scenario 4) scenarios will allow forest cover loss and agricultural extensification, especially in long term unless strict control measures are undertaken. The results also suggest that the natu- ral environments, including mainly forest areas, can be preserved in the next 30 years if the reduced agriculture expansion (scenario 2), the major urban development A (scenario 3), or the protection of natural capital (sce- nario 4) occur. Our study has allowed the visualization of different evolutions of agriculture and urbanization in the future which progress by favoring deforestation. The output of this study may thus be used in various contexts where it will be possible to evaluate their consequence on environmental, animal, and public health endpoints. Environ Monit Assess (2023) 195:815 1 3 Page 17 of 20 815 Vol.: (0123456789) It can also be combined with climate change scenarios. This is the approach that will be taken in our next step to predict the future of both the abundance of the main vector for West Nile virus and the diversity of mosquito. Acknowledgements This work was jointly supported by the Public Health Agency of Canada, the Agriculture and Agri-food Canada. We would like to thank Peter Verburg for providing advice and support on the use of the software and the Dyna-Clue model; Julie Allostry for providing support in extracting data; Stéphanie Brazeau, Serge Olivier Kotchi and Maxime Rioux-Rousseau for their support in geomatics. Author contribution Miarisoa Rindra Rakotoarinia: study conceptualization and implementation, data collection, formal analysis, visualization, writing the original draft, reviewing and editing the manuscript; Ousmane Seidou: study conceptualiza- tion, supervision, methodology, reviewing and editing the man- uscript; David Lapen: study conceptualization, supervision, methodology, resources, funding acquisition, reviewing, and editing the manuscript; Nicholas H. Ogden: study conceptual- ization, supervision, methodology, reviewing, and editing the manuscript; Patrick Leighton: study conceptualization, meth- odology, supervision, reviewing the manuscript; Antoinette Ludwig: study conceptualization, methodology, resources, funding acquisition, supervision, visualization, reviewing, and editing the manuscript. Funding Open Access provided by Public Health Agency of Canada. This work was funded, in part, by the Public Health Agency of Canada, the Agriculture and Agri-food Canada under project number J-002305 Environmental Change One Health Observatory (ECO2). Data availability The datasets used in this study are free of access. The sources of these data are given in the method section. Declarations All authors have read, understood, and have complied as ap- plicable with the statement on “Ethical responsibilities of Au- thors” as found in the Instructions for Authors and are aware that with minor exceptions, no changes can be made to author- ship once the paper is submitted. Conflict of interest The authors declare no competing interests. Open Access This article is licensed under a Creative Com- mons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Crea- tive Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. References Agriculture and Agri-Food Canada Annual Crop Inven- tory. (2021). Annual SpaceBased Crop Inventory for Canada, 2009-2021, Agroclimate, Geomatics and Earth Observation Division, Science and Technology Branch. Retrieved April 19, 2022 fromhttps:// open. canada. ca/ data/ en/ datas et/ ba264 5d5- 4458- 414d- b196- 6303a c06c1 c9 Altizer, S., Ostfeld, R. S., Johnson, P. T., Kutz, S., & Har- vell, C. D. (2013). Climate change and infectious dis- eases: From evidence to a predictive framework. Sci- ence, 341(6145), 514–519. https:// doi. org/ 10. 1126/ scien ce. 12394 01 Andreadis, T. G., Anderson, J. F., & Vossbrinck, C. R. (2001). Mosquito surveillance for West Nile virus in Connecticut, 2000: Isolation from Culex pipiens, Cx. restuans, Cx. salinarius, and Culiseta melanura. Emerg- ing Infectious Diseases, 7(4), 670. https:// doi. org/ 10. 3201/ eid07 04. 010413 Bartlow, A. W., Manore, C., Xu, C., Kaufeld, K. A., Del Valle, S., Ziemann, A., Fairchild, G., & Fair, J. M. (2019). Fore- casting zoonotic infectious disease response to climate change: Mosquito vectors and a changing environment. Veterinary Sciences, 6(2), 40. https:// doi. org/ 10. 3390/ vetsc i6020 040 Beard, C. B., Eisen, R. J., Barker, C., Garofalo, J., Hahn, M., Hayden, M., Monaghan, A., Ogden, N., & Schramm, P. (2016). Ch. 5: Vectorborne diseases. Retrieved May 5, 2022 from https:// healt h2016. globa lchan ge. gov/ Botello, G., Golladay, S., Covich, A., & Blackmore, M. (2013). Immature mosquitoes in agricultural wetlands of the coastal plain of Georgia, USA: Effects of landscape and environmental habitat characteristics. Ecological Indica- tors, 34, 304–312. https:// doi. org/ 10. 1016/j. ecoli nd. 2013. 05. 018 Bowden, S. E., Magori, K., & Drake, J. M. (2011). Regional differences in the association between land cover and West Nile virus disease incidence in humans in the United States. The American Journal of Tropical Medicine Hygiene, 84(2), 234–238. https:// doi. org/ 10. 4269/ ajtmh. 2011. 10- 0134 Bucała-Hrabia, A. (2017). Long-term impact of socio-eco- nomic changes on agricultural land use in the Polish Car- pathians. Land Use Policy, 64, 391–404. https:// doi. org/ 10. 1016/j. landu sepol. 2017. 03. 013 Chuang, T.-W., Hildreth, M. B., Vanroekel, D. L., & Wimberly, M. C. (2011). Weather and land cover influences on mos- quito populations in Sioux Falls, South Dakota. Journal of Medical Entomology, 48(3), 669–679. https:// doi. org/ 10. 1603/ ME102 46 Cousien, A., Abel, S., Monthieux, A., Andronico, A., Calmont, I., Cervantes, M., Césaire, R., Gallian, P., De Lambal- lerie, X., & Laouénan, C. (2019). Assessing Zika virus transmission within households during an outbreak in http://creativecommons.org/licenses/by/4.0/ https://open.canada.ca/data/en/dataset/ba2645d5-4458-414d-b196-6303ac06c1c9 https://open.canada.ca/data/en/dataset/ba2645d5-4458-414d-b196-6303ac06c1c9 https://open.canada.ca/data/en/dataset/ba2645d5-4458-414d-b196-6303ac06c1c9 https://doi.org/10.1126/science.1239401 https://doi.org/10.1126/science.1239401 https://doi.org/10.3201/eid0704.010413 https://doi.org/10.3201/eid0704.010413 https://doi.org/10.3390/vetsci6020040 https://doi.org/10.3390/vetsci6020040 https://health2016.globalchange.gov/ https://doi.org/10.1016/j.ecolind.2013.05.018 https://doi.org/10.1016/j.ecolind.2013.05.018 https://doi.org/10.4269/ajtmh.2011.10-0134 https://doi.org/10.4269/ajtmh.2011.10-0134 https://doi.org/10.1016/j.landusepol.2017.03.013 https://doi.org/10.1016/j.landusepol.2017.03.013 https://doi.org/10.1603/ME10246 https://doi.org/10.1603/ME10246 Environ Monit Assess (2023) 195:815 1 3 815 Page 18 of 20 Vol:. (1234567890) Martinique, 2015–2016. American Journal of Epidemi- ology, 188(7), 1389–1396. https:// doi. org/ 10. 1093/ aje/ kwz091 Czekajlo, A., Coops, N. C., Wulder, M. A., Hermosilla, T., White, J. C., & van den Bosch, M. (2021). Mapping dynamic peri-urban land use transitions across Canada using Landsat time series: Spatial and temporal trends and associations with socio-demographic factors. Computers, Environment Urban Systems, 88, 101653. https:// doi. org/ 10. 1016/j. compe nvurb sys. 2021. 101653 Das, P., Behera, M., Pal, S., Chowdary, V., Behera, P., & Singh, T. (2019). Studying land use dynamics using decadal satellite images and Dyna-CLUE model in the Mahanadi River basin. India. Environmen- tal Monitoring, 191(3), 804. https:// doi. org/ 10. 1007/ s10661- 019- 7698-3 Drebot, M. (2015). Vector-borne diseases in Canada: emerging mosquito-borne bunyaviruses in Canada. Canada Com- municable Disease Report, 41(6), 117. https:// doi. org/ 10. 14745/ ccdr. v41i0 6a01 Duranton, G., Henderson, V., & Strange, W. (2015). Handbook of regional and urban economics. Elsevier. El-Khoury, A., Seidou, O., Lapen, D. R., Sunohara, M., Zhen- yang, Q., Mohammadian, M., & Daneshfar, B. (2014). Prediction of land-use conversions for use in watershed- scale hydrological modeling: A Canadian case study. The Canadian Geographer/le Géographe Canadien, 58(4), 499–516. https:// doi. org/ 10. 1111/ cag. 12105 Environment and Climate Change Canada. (2021). Canadian environmental sustainability indicators: Land-use change. Retrieved June 6, 2022 from https:// www. canada. ca/ en/ envir onment- clima te- change/ servi ces/ envir onmen tal- indic ators/ land- use- change. html Epstein, P. R., Diaz, H. F., Elias, S., Grabherr, G., Graham, N. E., Martens, W. J., MosIey-Thompson, E., & Susskind, J. (1998). Biological and physical signs of climate change: Focus on mosquito-borne diseases. Bulletin of the Ameri- can Meteorological Society, 79(3), 409–418. https:// doi. org/ 10. 1175/ 1520- 0477(1998) 079% 3c0409: BAPSOC% 3e2.0. CO;2 Foody, G. M. (2006). What is the difference between two maps? A remote senser’s view. Journal of Geographi- cal Systems, 8(2), 119–130. https:// doi. org/ 10. 1007/ s10109- 006- 0023-z Franklinos, L. H., Jones, K. E., Redding, D. W., & Abubakar, I. (2019). The effect of global change on mosquito-borne disease. The Lancet Infectious Diseases, 19(9), e302– e312. https:// doi. org/ 10. 1016/ S1473- 3099(19) 30161-6 Gardner, A. M., Lampman, R. L., & Muturi, E. J. (2014). Land use patterns and the risk of West Nile virus transmission in central Illinois. Vector-Borne Zoonotic Diseases, 14(5), 338–345. Giordano, B. V., Gasparotto, A., & Hunter, F. F. (2015). A checklist of the 67 mosquito species of Ontario, Canada. Journal of the American Mosquito Control Association, 31(1), 101–103. https:// doi. org/ 10. 2987/ 14- 6456R.1 Jacob, B. G., Lampman, R. L., Ward, M. P., Muturi, E. J., Morris, J. A., Caamano, E. X., & Novak, R. J. (2009). Geospatial variability in the egg raft distribution and abundance of Culex pipiens and Culex restuans in Urbana- Champaign, Illinois. International Journal of Remote Sensing, 30(8), 2005–2019. https:// doi. org/ 10. 1080/ 01431 16080 25491 95 Keeling, M. J., & Rohani, P. (2011). Modeling infectious dis- eases in humans and animals. Princeton University Press. https:// doi. org/ 10. 2307/j. ctvcm 4gk0 Kottek, M., Grieser, J., Beck, C., Rudolf, B., & Rubel, F. (2006). World map of the Köppen-Geiger climate classi- fication updated. Meteorologische Zeitschrift, 15(3), 259– 263. https:// doi. org/ 10. 1127/ 0941- 2948/ 2006/ 0130 Kovach, T. J., & Kilpatrick, A. M. (2018). Increased human incidence of West Nile virus disease near rice fields in California but not in Southern United States. The Ameri- can Journal of Tropical Medicine and Hygiene, 99(1), 222. https:// doi. org/ 10. 4269/ ajtmh. 18- 0120 Kraemer, M. U., Hay, S. I., Pigott, D. M., Smith, D. L., Wint, G. W., & Golding, N. (2016). Progress and challenges in infectious disease cartography. Trends in Parasitology, 32(1), 19–29. https:// doi. org/ 10. 1016/j. pt. 2015. 09. 006 Lee, S. H., Nam, K. W., Jeong, J. Y., Yoo, S. J., Koh, Y.-S., Lee, S., Heo, S. T., Seong, S.-Y., & Lee, K. H. (2013). The effects of climate change and globalization on mos- quito vectors: Evidence from Jeju Island, South Korea on the potential for Asian tiger mosquito (Aedes albopictus) influxes and survival from Vietnam rather than Japan. PLoS One, 8(7), e68512. https:// doi. org/ 10. 1371/ journ al. pone. 00685 12 Li, W., Wu, C., & Zang, S. (2014). Modeling urban land use conversion of Daqing City, China: A comparative analy- sis of “top-down” and “bottom-up” approaches. Stochas- tic Environmental Research and Risk Assessment, 28(4), 817–828. https:// doi. org/ 10. 1007/ s00477- 012- 0671-0 Medlock, J., Balenghien, T., Alten, B., Versteirt, V., & Schaf- fner, F. (2018). Field sampling methods for mosquitoes, sandflies, biting midges and ticks: VectorNet project 2014–2018. 15(6), 1435E. https:// doi. org/ 10. 2903/ sp. efsa. 2018. EN- 1435 Meiyappan, P., Dalton, M., O’Neill, B. C., & Jain, A. K. (2014). Spatial modeling of agricultural land use change at global scale. Ecological Modelling, 291, 152–174. https:// doi. org/ 10. 1016/j. ecolm odel. 2014. 07. 027 Moua, Y., Kotchi, S. O., Ludwig, A., & Brazeau, S. J. R. S. (2021). Mapping the habitat suitability of West Nile virus vectors in Southern Quebec and Eastern Ontario, Canada, with species distribution modeling and satellite earth observation data. 13(9), 1637. https:// doi. org/ 10. 3390/ rs130 91637 Murray-Rust, D., Robinson, D. T., Guillem, E., Karali, E., & Rounsevell, M. (2014). An open framework for agent based modelling of agricultural land use change. Environ- mental Modelling Software, 61, 19–38. https:// doi. org/ 10. 1016/j. envso ft. 2014. 06. 027 Norris, D. E. (2004). Mosquito-borne diseases as a conse- quence of land use change. EcoHealth, 1(1), 19–24. https:// doi. org/ 10. 1007/ s10393- 004- 0008-7 https://doi.org/10.1093/aje/kwz091 https://doi.org/10.1093/aje/kwz091 https://doi.org/10.1016/j.compenvurbsys.2021.101653 https://doi.org/10.1016/j.compenvurbsys.2021.101653 https://doi.org/10.1007/s10661-019-7698-3 https://doi.org/10.1007/s10661-019-7698-3 https://doi.org/10.14745/ccdr.v41i06a01 https://doi.org/10.14745/ccdr.v41i06a01 https://doi.org/10.1111/cag.12105 https://www.canada.ca/en/environment-climate-change/services/environmental-indicators/land-use-change.html https://www.canada.ca/en/environment-climate-change/services/environmental-indicators/land-use-change.html https://www.canada.ca/en/environment-climate-change/services/environmental-indicators/land-use-change.html https://doi.org/10.1175/1520-0477(1998)079%3c0409:BAPSOC%3e2.0.CO;2 https://doi.org/10.1175/1520-0477(1998)079%3c0409:BAPSOC%3e2.0.CO;2 https://doi.org/10.1175/1520-0477(1998)079%3c0409:BAPSOC%3e2.0.CO;2 https://doi.org/10.1007/s10109-006-0023-z https://doi.org/10.1007/s10109-006-0023-z https://doi.org/10.1016/S1473-3099(19)30161-6 https://doi.org/10.2987/14-6456R.1 https://doi.org/10.1080/01431160802549195 https://doi.org/10.1080/01431160802549195 https://doi.org/10.2307/j.ctvcm4gk0 https://doi.org/10.1127/0941-2948/2006/0130 https://doi.org/10.4269/ajtmh.18-0120 https://doi.org/10.1016/j.pt.2015.09.006 https://doi.org/10.1371/journal.pone.0068512 https://doi.org/10.1371/journal.pone.0068512 https://doi.org/10.1007/s00477-012-0671-0 https://doi.org/10.2903/sp.efsa.2018.EN-1435 https://doi.org/10.2903/sp.efsa.2018.EN-1435 https://doi.org/10.1016/j.ecolmodel.2014.07.027 https://doi.org/10.3390/rs13091637 https://doi.org/10.3390/rs13091637 https://doi.org/10.1016/j.envsoft.2014.06.027 https://doi.org/10.1016/j.envsoft.2014.06.027 https://doi.org/10.1007/s10393-004-0008-7 Environ Monit Assess (2023) 195:815 1 3 Page 19 of 20 815 Vol.: (0123456789) Nuissl, H., & Siedentop, S. (2021). Urbanisation and land use change. In Sustainable Land Management in a European Context (pp. 75–99). Springer, Cham. https:// doi. org/ 10. 1007/ 978-3- 030- 50841-8_5 Ogden, N. H., Milka, R., Caminade, C., & Gachon, P. (2014). Recent and projected future climatic suitability of North America for the Asian tiger mosquito Aedes albopictus. Parasites and Vectors, 7(1), 1–14. https:// doi. org/ 10. 1186/ s13071- 014- 0532-4 Okogun, G. R., Anosike, J. C., Okere, A., Nwoke, B., & Esekhegbe, A. (2003). Epidemiological implications of preferences of breeding sites of mosquito speciesin Mid- western Nigeria. Annals of Agricultural Environmental Medicine, 10(2), 217–222. Ortiz, D. I., Piche-Ovares, M., Romero-Vega, L. M., & Wag- man, J. (2022). & Troyo, a. https:// doi. org/ 10. 3390/ insec ts130 10020. Insec ts,13(1) ,20 Patz, J. A., Olson, S. H., Uejio, C. K., & Gibbs, H. K. (2008). Medical Clinics of North America, 92(6), 1473–1491. https:// doi. org/ 10. 1016/j. mcna. 2008. 07. 007. Pontius, R. G., & Millones, M. (2011). Death to Kappa: Birth of quantity disagreement and allocation disagree- ment for accuracy assessment. International Journal of Remote Sensing, 32(15), 4407–4429. https:// doi. org/ 10. 1080/ 01431 161. 2011. 552923 Pontius, R. G., Jr., & Schneider, L. C. (2001). Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agriculture, Ecosystems Environment, 85(1–3), 239–248. https:// doi. org/ 10. 1016/ S0167- 8809(01) 00187-6 Pontius, R. G., Boersma, W., Castella, J.-C., Clarke, K., de Nijs, T., Dietzel, C., Duan, Z., Fotsing, E., Goldstein, N., & Kok, K. (2008). Comparing the input, output, and validation maps for several models of land change. The Annals of Regional Science, 42(1), 11–37. https:// doi. org/ 10. 1007/ s00168- 007- 0138-2 Préau, C., Isselin-Nondedeu, F., Sellier, Y., Bertrand, R., & Grandjean, F. (2019). Predicting suitable habitats of four range margin amphibians under climate and land- use changes in southwestern France. Regional Environ- mental Change, 19(1), 27–38. https:// doi. org/ 10. 1007/ s10113- 018- 1381-z Price, B., Kaim, D., Szwagrzyk, M., Ostapowicz, K., Kolecka, N., Schmatz, D. R., Wypych, A., & Kozak, J. (2017). Legacies, socio-economic and biophysical processes and drivers: The case of future forest cover expansion in the Polish Carpathians and Swiss Alps. Regional Environmental Change, 17(8), 2279–2291. https:// doi. org/ 10. 1007/ s10113- 016- 1079-z Rakotoarinia, M. R., Blanchet, F. G., Gravel, D., Lapen, D. R., Leighton, P. A., Ogden, N. H., & Ludwig, A. (2022). Effects of land use and weather on the presence and abundance of mosquito-borne disease vectors in a urban and agricultural landscape in Eastern Ontario, Canada. . PLoS One, 17(3). https:// doi. org/ 10. 1371/ journ al. pone. 02623 76 Reiskind, M., Griffin, R., Janairo, M., Hopperstad, K. J. M., & Entomology, V. (2017). Mosquitoes of field and forest: The scale of habitat segregation in a diverse mos- quito assemblage. 31(1), 44-54.https:// doi. org/ 10. 1111/ mve. 12193 Reiter, P. (2001). Climate change and mosquito-borne dis- ease. Environmental Health Perspectives, 109(suppl 1), 141–161. https:// doi. org/ 10. 1289/ ehp. 01109 s1141 Roberts, D., & Irving-Bell, R. (1997). Salinity and microhab- itat preferences in mosquito larvae from southern Oman. Journal of Arid Environments, 37(3), 497–504. https:// doi. org/ 10. 1006/ jare. 1997. 0291 Sakayarote, K., & Shrestha, R. P. (2019). Simulating land use for protecting food crop areas in northeast Thailand using GIS and Dyna-CLUE. Journal of Geographi- cal Sciences, 29(5), 803–817. https:// doi. org/ 10. 1007/ s11442- 019- 1629-7 Schrojenstein Lantman, J. V., Verburg, P. H., Bregt, A., & Geertman, S. (2011). Core principles and concepts in land-use modelling: A literature review. Land-use Mod- elling in Planning Practice, 35–57. https:// doi. org/ 10. 1007/ 978- 94- 007- 1822-7_3 Silver, J. B. (2007). Mosquito ecology: Field sampling meth- ods. Springer Science & Business Media. Springer Science & Business Media. https:// doi. org/ 10. 1007/ 978-1- 4020- 6666-5 Smith, P. G. (2015). Long-term temporal trends in agri-envi- ronment and agricultural land use in Ontario, Canada: Transformation, transition and significance. Journal of Geography and Geology, 7(2), 32. https:// doi. org/ 10. 5539/ JGG. V7N2P 32 Team, R. C. (2020). R: A language and environment for sta- tistical computing. R Foundation for Statistical Comput- ing, Vienna, Austria. Retrieved November 10, 2021 from https:// www.R- proje ct. org Tizora, P., Le Roux, A., Mans, G., & Cooper, A. (2018). Adapt- ing the Dyna-CLUE model for simulating land use and land cover change in the Western Cape Province. South African Journal of Geomatics, 7(2), 190–203. https:// doi. org/ 10. 4314/ sajg. v7i2.7 Trisurat, Y., Alkemade, R., & Verburg, P. H. (2010). Project- ing land-use change and its consequences for biodiversity in Northern Thailand. Environmental Management, 45(3), 626–639. https:// doi. org/ 10. 1007/ s00267- 010- 9438-x Van der Sluis, T., Pedroli, B., Frederiksen, P., Kristensen, S. B., Busck, A. G., Pavlis, V., & Cosor, G. L. (2019). The impact of European landscape transitions on the provi- sion of landscape services: An explorative study using six cases of rural land change. Landscape Ecology, 34(2), 307–323. https:// doi. org/ 10. 1007/ s10980- 018- 0765-2 Verburg, P. H., & Overmars, K. P. (2009). Combining top- down and bottom-up dynamics in land use modeling: Exploring the future of abandoned farmlands in Europe with the Dyna-CLUE model. Landscape Ecology, 24(9), 1167. https:// doi. org/ 10. 1007/ s10980- 009- 9355-7 Verburg, P. H., Soepboer, W., Veldkamp, A., Limpiada, R., Espaldon, V., & Mastura, S. S. (2002). Modeling the spa- tial dynamics of regional land use: The CLUE-S model. Environmental Management, 30(3), 391–405. https:// doi. org/ 10. 1007/ s00267- 002- 2630-x https://doi.org/10.1007/978-3-030-50841-8_5 https://doi.org/10.1007/978-3-030-50841-8_5 https://doi.org/10.1186/s13071-014-0532-4 https://doi.org/10.1186/s13071-014-0532-4 https://doi.org/10.3390/insects13010020.Insects,13(1),20 https://doi.org/10.3390/insects13010020.Insects,13(1),20 https://doi.org/10.1016/j.mcna.2008.07.007 https://doi.org/10.1080/01431161.2011.552923 https://doi.org/10.1080/01431161.2011.552923 https://doi.org/10.1016/S0167-8809(01)00187-6 https://doi.org/10.1016/S0167-8809(01)00187-6 https://doi.org/10.1007/s00168-007-0138-2 https://doi.org/10.1007/s00168-007-0138-2 https://doi.org/10.1007/s10113-018-1381-z https://doi.org/10.1007/s10113-018-1381-z https://doi.org/10.1007/s10113-016-1079-z https://doi.org/10.1371/journal.pone.0262376 https://doi.org/10.1371/journal.pone.0262376 https://doi.org/10.1111/mve.12193 https://doi.org/10.1111/mve.12193 https://doi.org/10.1289/ehp.01109s1141 https://doi.org/10.1006/jare.1997.0291 https://doi.org/10.1006/jare.1997.0291 https://doi.org/10.1007/s11442-019-1629-7 https://doi.org/10.1007/s11442-019-1629-7 https://doi.org/10.1007/978-94-007-1822-7_3 https://doi.org/10.1007/978-94-007-1822-7_3 https://doi.org/10.1007/978-1-4020-6666-5 https://doi.org/10.1007/978-1-4020-6666-5 https://doi.org/10.5539/JGG.V7N2P32 https://doi.org/10.5539/JGG.V7N2P32 https://www.R-project.org https://doi.org/10.4314/sajg.v7i2.7 https://doi.org/10.4314/sajg.v7i2.7 https://doi.org/10.1007/s00267-010-9438-x https://doi.org/10.1007/s10980-018-0765-2 https://doi.org/10.1007/s10980-009-9355-7 https://doi.org/10.1007/s00267-002-2630-x https://doi.org/10.1007/s00267-002-2630-x Environ Monit Assess (2023) 195:815 1 3 815 Page 20 of 20 Vol:. (1234567890) Verburg, P. H., de Nijs, T. C., van Eck, J. R., Visser, H., & de Jong, K. (2004). A method to analyse neighbourhood characteristics of land use patterns. Computers, Environ- ment Urban Systems, 28(6), 667–690. https:// doi. org/ 10. 1016/j. compe nvurb sys. 2003. 07. 001 Verburg, P. H., Veldkamp, T., & Lesschen, J. P. (2006). Exer- cises for the CLUE-S model. Retrieved February 2, 2022 from http:// spinl ab. vu. nl/ wp- conte nt/ uploa ds/ 2016/ 09/ Exerc iseCl ues. pdf Verdonschot, P. F., & Besse-Lototskaya, A. A. (2014). Flight distance of mosquitoes (Culicidae): A metadata analysis to support the management of barrier zones around rewet- ted and newly constructed wetlands. Limnologica, 45, 69–79. https:// doi. org/ 10. 1016/j. limno. 2013. 11. 002 Vynnycky, E., & White, R. (2010). An introduction to infec- tious disease modelling. New York: OUP Oxford. Waddell, E. H., Banin, L. F., Fleiss, S., Hill, J. K., Hughes, M., Jelling, A., Yeong, K. L., Ola, B. B., Sailim, A. B., & Tan- gah, J. (2020). Land-use change and propagule pressure promote plant invasions in tropical rainforest remnants. Landscape Ecology, 35(9), 1891–1906. https:// doi. org/ 10. 1007/ s10980- 020- 01067-9 Waiyasusri, K., & Wetchayont, P. (2020). Assessing long-term deforestation in nam san watershed, loei province, thai- land using a dyna-clue model. Geography, Environment, Sustainability, 13(4), 81–97. https:// doi. org/ 10. 24057/ 2071- 9388- 2020- 14 Wang, M., & Sun, X. (2016). Potential impact of land use change on ecosystem services in China. J Environmental Monitoring Assessment, 188(4), 248. https:// doi. org/ 10. 1007/ s10661- 016- 5245-z Xu, L., Li, Z., Song, H., & Yin, H. (2013). Land-use planning for urban sprawl based on the clue-s model: A case study of Guangzhou, China. Entropy, 15(9), 3490–3506. https:// doi. org/ 10. 3390/ e1509 3490 Zhang, P., Liu, Y., Pan, Y., & Yu, Z. (2013). Land use pattern optimization based on CLUE-S and SWAT models for agricultural non-point source pollution control. Math- ematical Computer Modelling, 58(3–4), 588–595. https:// doi. org/ 10. 1016/j. mcm. 2011. 10. 061 Zhang, L., Nan, Z., Yu, W., & Ge, Y. (2016). Hydrologi- cal responses to land-use change scenarios under con- stant and changed climatic conditions. Environmental Management, 57(2), 412–431. https:// doi. org/ 10. 1007/ s00267- 015- 0620-z Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. https://doi.org/10.1016/j.compenvurbsys.2003.07.001 https://doi.org/10.1016/j.compenvurbsys.2003.07.001 http://spinlab.vu.nl/wp-content/uploads/2016/09/ExerciseClues.pdf http://spinlab.vu.nl/wp-content/uploads/2016/09/ExerciseClues.pdf https://doi.org/10.1016/j.limno.2013.11.002 https://doi.org/10.1007/s10980-020-01067-9 https://doi.org/10.1007/s10980-020-01067-9 https://doi.org/10.24057/2071-9388-2020-14 https://doi.org/10.24057/2071-9388-2020-14 https://doi.org/10.1007/s10661-016-5245-z https://doi.org/10.1007/s10661-016-5245-z https://doi.org/10.3390/e15093490 https://doi.org/10.3390/e15093490 https://doi.org/10.1016/j.mcm.2011.10.061 https://doi.org/10.1016/j.mcm.2011.10.061 https://doi.org/10.1007/s00267-015-0620-z https://doi.org/10.1007/s00267-015-0620-z Future land-use change predictions using Dyna-Clue to support mosquito-borne disease risk assessment Abstract Introduction Methods Study region Modeling approach Land-use data Conversion rules Logistic regression 1: location suitability Logistic regression 2: neighborhood suitability Land-use requirements (demands) Scenarios settings Modeling assessment Assessment of the logistic regressions Assessment of the land-use model Model output choice Results Modeling Logistic regression 1: location suitability Logistic regression 2: neighborhood suitability Land-use changes simulations according to the five scenarios (2030, 2050, and 2070) Modeling assessment Discussion Conclusion Acknowledgements Anchor 26 References