Deriving fine-scale models of human mobility from aggregated origin-destination flow data

The spatial dynamics of epidemics are fundamentally affected by patterns of human mobility. Mobile phone call detail records (CDRs) are a rich source of mobility data, and allow semi-mechanistic models of movement to be parameterised even for resource-poor settings. While the gravity model typically reproduces human movement reasonably well at the administrative level spatial scale, past studies suggest that parameter estimates vary with the level of spatial discretisation at which models are fitted. Given that privacy concerns usually preclude public release of very fine-scale movement data, such variation would be problematic for individual-based simulations of epidemic spread parametrised at a fine spatial scale. We therefore present new methods to fit fine-scale mathematical mobility models (here we implement variants of the gravity and radiation models) to spatially aggregated movement data and investigate how model parameter estimates vary with spatial resolution. We use gridded population data at 1km resolution to derive population counts at different spatial scales (down to ∼ 5km grids) and implement mobility models at each scale. Parameters are estimated from administrative-level flow data between overnight locations in Kenya and Namibia derived from CDRs: where the model spatial resolution exceeds that of the mobility data, we compare the flow data between a particular origin and destination with the sum of all model flows between cells that lie within those particular origin and destination administrative units. Clear evidence of over-dispersion supports the use of negative binomial instead of Poisson likelihood for count data with high values. Radiation models use fewer parameters than the gravity model and better predict trips between overnight locations for both considered countries. Results show that estimates for some parameters change between countries and with spatial resolution and highlight how imperfect flow data and spatial population distribution can influence model fit.     

Occupancy versus colonization–extinction models for projecting population trends at different spatial scales

Understanding spatiotemporal population trends and their drivers is a key aim in population ecology. We further need to be able to predict how the dynamics and sizes of populations are affected in the long term by changing landscapes and climate. However, predictions of future population trends are sensitive to a range of modeling assumptions. Deadwood‐dependent fungi are an excellent system for testing the performance of different predictive models of sessile species as these species have different rarity and spatial population dynamics, the populations are structured at different spatial scales, and they utilize distinct substrates. We tested how the projected large‐scale occupancies of species with differing landscape‐scale occupancies are affected over the coming century by different modeling assumptions. We compared projections based on occupancy models against colonization–extinction models, conducting the modeling at alternative spatial scales and using fine‐ or coarse‐resolution deadwood data. We also tested effects of key explanatory variables on species occurrence and colonization–extinction dynamics. The hierarchical Bayesian models applied were fitted to an extensive repeated survey of deadwood and fungi at 174 patches. We projected higher occurrence probabilities and more positive trends using the occupancy models compared to the colonization–extinction models, with greater difference for the species with lower occupancy, colonization rate, and colonization:extinction ratio than for the species with higher estimates of these statistics. The magnitude of future increase in occupancy depended strongly on the spatial modeling scale and resource resolution. We encourage using colonization–extinction models over occupancy models, modeling the process at the finest resource‐unit resolution that is utilizable by the species, and conducting projections for the same spatial scale and resource resolution at which the model fitting is conducted. Further, the models applied should include key variables driving the metapopulation dynamics, such as the availability of suitable resource units, habitat quality, and spatial connectivity.     

Interplay between mobility,multi-seeding and lockdowns shapes COVID-19 local impact

Assessing the impact of mobility on epidemic spreading is of crucial importance for understanding the effect of policies like mass quarantines and selective re-openings. While many factors affect disease incidence at a local level, making it more or less homogeneous with respect to other areas, the importance of multi-seeding has often been overlooked. Multi-seeding occurs when several independent (non-clustered) infected individuals arrive at a susceptible population. This can lead to independent outbreaks that spark from distinct areas of the local contact (social) network. Such mechanism has the potential to boost incidence, making control efforts and contact tracing less effective. Here, through a modeling approach we show that the effect produced by the number of initial infections is non-linear on the incidence peak and peak time. When case importations are carried by mobility from an already infected area, this effect is further enhanced by the local demography and underlying mixing patterns: the impact of every seed is larger in smaller populations. Finally, both in the model simulations and the analysis, we show that a multi-seeding effect combined with mobility restrictions can explain the observed spatial heterogeneities in the first wave of COVID-19 incidence and mortality in five European countries. Our results allow us for identifying what we have called epidemic epicenter: an area that shapes incidence and mortality peaks in the entire country. The present work further clarifies the nonlinear effects that mobility can have on the evolution of an epidemic and highlight their relevance for epidemic control.     

Improved Coral Population Estimation Reveals Trends at Multiple Scales on Australia’s Great Barrier Reef

The global erosion of biodiversity presents unique challenges for identifying major changes in population dynamics, establishing their causes, and managing and conserving affected ecosystems at broad spatial scales. Adaptive learning approaches connecting different spatial scales through the transfer of hierarchical information are powerful tools to address such challenges. Here, we use a Semi-Parametric Bayesian Hierarchical (SPa-BaH) model to estimate coral cover trajectories using 16 years of a broad-scale survey on Australia’s Great Barrier Reef (GBR). The spatiotemporal variability of coral populations has been considered by separating three-tiered spatial scales and allowing for alternating phases of increasing and decreasing in the estimation of their trajectories. Model estimates revealed coral cover trajectories that were highly variable according to location but that fairly consistently declined at a regional spatial scale. Notwithstanding this general trend, individual reefs within subregions in the central part of the GBR often displayed different trajectory types between sites separated by only a few hundred meters. These coral dynamics were also associated with reduced recovery rates in the Cairns and Swain subregions. Our study highlights the importance of accounting for local variation in coral cover when estimating the spatiotemporal trends in coral cover trajectories, in this case, at the GBR scale. By retaining information at different hierarchical spatial scales, our SPa-BaH model supports better estimation of large-scale coral cover trajectories. The quantitative approaches developed here can also be applied to other species with complex dynamics thereby enhancing estimations of their trajectories at local- and larger-scales and options for their management.     

Differences in spatial synchrony and interspecific concordance inform guild‐level population trends for aerial insectivorous birds

Many animal species exhibit spatiotemporal synchrony in population fluctuations, which may provide crucial information about ecological processes driving population change. We examined spatial synchrony and concordance among population trajectories of five aerial insectivorous bird species: chimney swift Chaetura pelagica, purple martin Progne subis, barn swallow Hirundo rustica, tree swallow Tachycineta bicolor, and northern rough‐winged swallow Stelgidopteryx serripennis. Aerial insectivores have undergone severe guild‐wide declines that were considered more prevalent in northeastern North America. Here, we addressed four general questions including spatial synchrony within species, spatial concordance among species, frequency of declining trends among species, and geographic location of declining trends. We used dynamic factor analysis to identify large‐scale common trends underlying stratum‐specific annual indices for each species, representing population trajectories shared by spatially synchronous populations, from 46 yr of North American Breeding Bird Survey data. Indices were derived from Bayesian hierarchical models with continuous autoregressive spatial structures. Stratum‐level spatial concordance among species was assessed using cross‐correlation analysis. Probability of long‐term declining trends was compared among species using Bayesian generalized linear models. Chimney swifts exhibited declining trends throughout North America, with less severe declines through the industrialized Mid‐Atlantic and Great Lakes regions. Northern rough‐winged swallows exhibited declining trends throughout the west. Spatial concordance among species was limited, the proportion of declining trends varied among species, and contrary to previous reports, declining trends were not more prevalent in the northeast. Purple martins, barn swallows, and tree swallows exhibited synchrony across smaller spatial scales. The extensive within‐species synchrony and limited concordance suggest that population trajectories of these aerial insectivores are responding to large‐scale but complex and species‐ and region‐specific environmental conditions (e.g. climate, land use). A single driver of trends for aerial insectivores as a guild appears unlikely.     

Spatial Heterogeneity,Host Movement and Mosquito-Borne Disease Transmission

Mosquito-borne diseases are a global health priority disproportionately affecting low-income populations in tropical and sub-tropical countries. These pathogens live in mosquitoes and hosts that interact in spatially heterogeneous environments where hosts move between regions of varying transmission intensity. Although there is increasing interest in the implications of spatial processes for mosquito-borne disease dynamics, most of our understanding derives from models that assume spatially homogeneous transmission. Spatial variation in contact rates can influence transmission and the risk of epidemics, yet the interaction between spatial heterogeneity and movement of hosts remains relatively unexplored. Here we explore, analytically and through numerical simulations, how human mobility connects spatially heterogeneous mosquito populations, thereby influencing disease persistence (determined by the basic reproduction number R ₀), prevalence and their relationship. We show that, when local transmission rates are highly heterogeneous, R ₀ declines asymptotically as human mobility increases, but infection prevalence peaks at low to intermediate rates of movement and decreases asymptotically after this peak. Movement can reduce heterogeneity in exposure to mosquito biting. As a result, if biting intensity is high but uneven, infection prevalence increases with mobility despite reductions in R ₀. This increase in prevalence decreases with further increase in mobility because individuals do not spend enough time in high transmission patches, hence decreasing the number of new infections and overall prevalence. These results provide a better basis for understanding the interplay between spatial transmission heterogeneity and human mobility, and their combined influence on prevalence and R ₀.     

Scale dependency of metapopulation models used to predict climate change impacts on small mammals

To investigate potential range shifts in a changing climate it is becoming increasingly common to develop models that account for demographic processes. Metapopulation models incorporate the spatial configuration of occupied habitat (i.e. arrangement, size and quality), population demographics, and inter‐patch dispersal making them suitable for investigating potential threats to small mammal range and abundance. However, the spatial scale (resolution) used to represent species–environment dynamics may affect estimates of range shift and population resilience by failing to realistically represent the spatial configuration of suitable habitat, including stepping stones and refugia. We aimed to determine whether relatively fine‐scale environmental information influenced predictions of metapopulation persistence and range shift. Species distribution models were constructed for four small terrestrial mammals from southern Australia using environmental predictors measured at 0.1 × 0.1 km (0.01 km²) or 1.0 × 1.0 km (1 km²) resolution, and combined with demographic information to parameterise coupled niche‐population models. These models were used to simulate population dynamics projected over 40‐yr under a stable and changing climate. Initial estimates of the area of available habitat were similar at both spatial scales. However, at the fine‐scale, habitat configuration comprised a greater number of patches (ca 12 times), that were more irregular in shape (ca 8 times the perimeter:area), and separated by a tenth of the distance than at the coarse‐scale. While small patches were not more prone to extinction, populations generally declined at a higher rate and were associated with a lower expected minimum abundance. Despite increased species vulnerability at the fine‐scale, greater range shifts were measured at the coarse‐scale (for species illustrating a shift at both scales). These results highlight the potential for range shifts and species vulnerability information to be misrepresented if advanced modelling techniques incorporating species demographics and dispersal inadequately represent the scale at which these processes occur.     

Epidemiological tracking and population assignment of the non-clonal bacterium, Burkholderia pseudomallei

Rapid assignment of bacterial pathogens into predefined populations is an important first step for epidemiological tracking. For clonal species, a single allele can theoretically define a population. For non-clonal species such as Burkholderia pseudomallei, however, shared allelic states between distantly related isolates make it more difficult to identify population defining characteristics. Two distinct B. pseudomallei populations have been previously identified using multilocus sequence typing (MLST). These populations correlate with the major foci of endemicity (Australia and Southeast Asia). Here, we use multiple Bayesian approaches to evaluate the compositional robustness of these populations, and provide assignment results for MLST sequence types (STs). Our goal was to provide a reference for assigning STs to an established population without the need for further computational analyses. We also provide allele frequency results for each population to enable estimation of population assignment even when novel STs are discovered. The ability for humans and potentially contaminated goods to move rapidly across the globe complicates the task of identifying the source of an infection or outbreak. Population genetic dynamics of B. pseudomallei are particularly complicated relative to other bacterial pathogens, but the work here provides the ability for broad scale population assignment. As there is currently no independent empirical measure of successful population assignment, we provide comprehensive analytical details of our comparisons to enable the reader to evaluate the robustness of population designations and assignments as they pertain to individual research questions. Finer scale subdivision and verification of current population compositions will likely be possible with genotyping data that more comprehensively samples the genome. The approach used here may be valuable for other non-clonal pathogens that lack simple group-defining genetic characteristics and provides a rapid reference for epidemiologists wishing to track the origin of infection without the need to compile population data and learn population assignment algorithms.     

Host ecotype generates evolutionary and epidemiological divergence across a pathogen metapopulation

The extent and speed at which pathogens adapt to host resistance varies considerably. This presents a challenge for predicting when—and where—pathogen evolution may occur. While gene flow and spatially heterogeneous environments are recognized to be critical for the evolutionary potential of pathogen populations, we lack an understanding of how the two jointly shape coevolutionary trajectories between hosts and pathogens. The rust pathogen Melampsora lini infects two ecotypes of its host plant Linum marginale that occur in close proximity yet in distinct populations and habitats. In this study, we found that within-population epidemics were different between the two habitats. We then tested for pathogen local adaptation at host population and ecotype level in a reciprocal inoculation study. Even after controlling for the effect of spatial structure on infection outcome, we found strong evidence of pathogen adaptation at the host ecotype level. Moreover, sequence analysis of two pathogen infectivity loci revealed strong genetic differentiation by host ecotype but not by distance. Hence, environmental variation can be a key determinant of pathogen population genetic structure and coevolutionary dynamics and can generate strong asymmetry in infection risks through space.     

Evolution of host resistance: looking for coevolutionary hotspots at small spatial scales

Natural plant populations are often found to be extremely diverse in their resistance to pathogens. While the potential of pathogens in driving the evolution of resistance in hosts has been widely recognized, empirical evidence linking disease dynamics to host population genetic structure has remained scarce. Here I show that current coevolutionary selection for resistance can be divergent even on a very fine spatial scale. In a natural plant-pathogen metapopulation, disease occurrence patterns were highly aggregated over space and time within host populations. A laboratory inoculation experiment showed higher resistance within areas of the host populations where encounter rates with the pathogen have been high. Higher resistance to sympatric than to allopatric strains of the pathogen suggests that this change has taken place as a response to local selection. These results constitute evidence of adaptive microevolution of resistance resulting from disease epidemics in natural plant-pathogen associations, and highlight the importance of finding the relevant scale at which to address questions of current coevolutionary selection.     

Spatial (a)synchrony in population fluctuations of five plant species in fragmented habitats

The spatial scale at which populations show synchronous temporal fluctuations in abundance, relative to the spatial scale over which they can disperse, may influence the persistence of local and regional populations. There have been frequent demonstrations of spatial synchrony in population dynamics of animal populations. But few studies have investigated the degree of spatial synchrony in less mobile taxa, e.g. plants, where life history, dispersal and interaction with the environment would be different due to a sessile phase. This study has during three years investigated the synchrony in local population size changes in four short-lived species, and during a nine-year period for one long-lived species, in a semi-natural grassland landscape in southern Sweden. The spatial scale of this study was less than 15 km, which is quite small in comparison with other studies, but the temporal scale was of similar magnitude as the few studies on plant abundances and synchrony. When using detrended estimates of population size change, a significant pattern of decreasing synchrony with increasing distance was found for the two short-lived species that were most confined to manage semi-natural grasslands. Spatial synchrony was detected up to a few km. However, the species displayed synchrony in different years. The degree of synchrony can thus vary considerably across years and among species. Spatially autocorrelated weather conditions could partly explain the spatial scale of synchrony found during certain time intervals. However, the prevailing asynchrony suggests that local factors dominate the dynamics of the populations at the investigated scale.     

Invasion patterns across multiple scales by Hieracium species over 25 years in tussock grasslands of New Zealand's South Island

Studying patterns of species invasions over time at multiple spatial scales may help us to elucidate important factors driving those patterns and how they change according to temporal or spatial resolution. Here we provide a large, long‐term, landscape‐scale study of the invasion of three Hieracium species using a dataset that encompasses vegetation change on 124 transects over 25 years across the lower eastern South Island of New Zealand. We investigated the relationships between key environmental and ecological factors and the invasion trajectories of H. lepidulum, H. pilosella and H. praealtum, at two spatial scales: (i) among‐transect colonization and (ii) within‐transect changes in frequency and per cent cover. Our results show that the colonization and spread of Hieracium species among and within transects reflect (i) the importance of initial environmental and biological conditions, (ii) that our sampling captured different periods of the invasion trajectories of each of the three species, and (iii) the effects of differences in life histories of the three species.     

An ensemble forecast system for tracking dynamics of dengue outbreaks and its validation in China

As a common vector-borne disease, dengue fever remains challenging to predict due to large variations in epidemic size across seasons driven by a number of factors including population susceptibility, mosquito density, meteorological conditions, geographical factors, and human mobility. An ensemble forecast system for dengue fever is first proposed that addresses the difficulty of predicting outbreaks with drastically different scales. The ensemble forecast system based on a susceptible-infected-recovered (SIR) type of compartmental model coupled with a data assimilation method called the ensemble adjusted Kalman filter (EAKF) is constructed to generate real-time forecasts of dengue fever spread dynamics. The model was informed by meteorological and mosquito density information to depict the transmission of dengue virus among human and mosquito populations, and generate predictions. To account for the dramatic variations of outbreak size in different seasons, the effective population size parameter that is sequentially updated to adjust the predicted outbreak scale is introduced into the model. Before optimizing the transmission model, we update the effective population size using the most recent observations and historical records so that the predicted outbreak size is dynamically adjusted. In the retrospective forecast of dengue outbreaks in Guangzhou, China during the 2011–2017 seasons, the proposed forecast model generates accurate projections of peak timing, peak intensity, and total incidence, outperforming a generalized additive model approach. The ensemble forecast system can be operated in real-time and inform control planning to reduce the burden of dengue fever.     

Spatial and temporal factors affecting parasite genotypes encountered by hosts: Empirical data from American dog ticks (Dermacentor variabilis) parasitising raccoons (Procyon lotor)

The American dog tick (Dermacentor variabilis) is an important vector of numerous pathogens of humans and animals. In this study, we analysed population genetic patterns in D. variabilis at scales of the host individual (infrapopulation) and population (component population) to elucidate fine-scale spatial and temporal factors influencing transmission dynamics. We genotyped D. variabilis collected from raccoons (Procyon lotor) trapped in two habitat patches (located in Indiana, USA) which were spatially proximate (5.9 km) and limited in size (10.48 Ha and 25.47 Ha, respectively). Despite the fine spatial sampling scale, our analyses revealed significant genetic differentiation amongst component populations and infrapopulations (within each component population), indicating a non-random pattern of encountering tick genotypes by raccoons at both scales evaluated. We found evidence for male-biased dispersal in the ticks themselves (in one component population) and an age-bias in spatial scales at which raccoons encountered ticks in the environment. At the scale of the component population, our analyses revealed that raccoons encountered ticks from a limited number of D. variabilis family groups, likely due to high reproductive variance amongst individual ticks. Finally, we found evidence for a temporal effect with raccoons encountering ticks in the environment as “clumps” of related individuals. While the genetic structure of parasite populations are increasingly being investigated at small spatial scales (e.g. the infrapopulation), our data reveal that genetic structuring can originate at scales below that of the infrapopulation, due to the interaction between temporal and biological factors affecting the encounter of parasites by individual hosts. Ultimately, our data indicate that genetic structure in parasites must be viewed as a consequence of both spatial and temporal variance in host-parasite interactions, which in turn are driven by demographic factors related to both the host and parasite.     

Point pattern analysis of Phyllostachys bissetti ramet population in West China Rainy Area

In this paper, point pattern analysis was conducted to study the spatial distribution of Phyllostachys bissetii ramet population and the spatial association between different age-class P. bissetii ramet populations in West China Rainy Area. The ramet population had a clumped distribution at the scale 0-0.32 m, a regular distribution at the scale 0.64-4.48 m, and a random distribution at the scale > 4.48 m. Different age-class ramet populations mainly had a random distribution at the scale 0-8.00 m, though a slight difference was observed among different age-classes. The spatial association between age-class I and age-classes II and III at the scale 1.76 - 4.16 m and 0.32-4.16 m approached to or reached to negative, respectively, while the spatial association between age-classes I and IV at the scale 0.32-3.04 m was significantly negative, indicating that the spatial negative association between younger and elder ramet populations increased with enlarged age-class difference. The spatial pattern of P. bissetii ramet population and the spatial association between different age-class ramet populations were depended on spatial scale, ramet age, and environmental factors.     

华西雨屏区白夹竹分株种群的点格局分析

采用点格局分析方法对华西雨屏区白夹竹分株种群的分布格局以及不同龄级分株之间的相互关系进行分析.结果表明：白夹竹分株种群在0～0.32 m空间尺度上呈集群分布,0.64～4.48 m空间尺度上呈均匀分布,>4.48 m空间尺度上呈随机分布.各龄级分株种群在0～8.00 m空间尺度上主要呈随机分布,龄级间略有差别.其中,Ⅰ龄级与Ⅱ、Ⅲ龄级分别在1.76～4.16 m、0.32～4.16 m尺度上接近或达到空间负关联,与Ⅳ龄级在0.32～3.04 m尺度上呈显著空间负关联,表现为随着龄级差距的加大,幼龄分株与高龄级分株的空间负关联增加.白夹竹分株种群的空间格局及不同龄级分株之间的相互关系由尺度、分株龄级及环境因素共同决定.     

Fine-scale analysis reveals cryptic landscape genetic structure in desert tortoises

Characterizing the effects of landscape features on genetic variation is essential for understanding how landscapes shape patterns of gene flow and spatial genetic structure of populations. Most landscape genetics studies have focused on patterns of gene flow at a regional scale. However, the genetic structure of populations at a local scale may be influenced by a unique suite of landscape variables that have little bearing on connectivity patterns observed at broader spatial scales. We investigated fine-scale spatial patterns of genetic variation and gene flow in relation to features of the landscape in desert tortoise (Gopherus agassizii), using 859 tortoises genotyped at 16 microsatellite loci with associated data on geographic location, sex, elevation, slope, and soil type, and spatial relationship to putative barriers (power lines, roads). We used spatially explicit and non-explicit Bayesian clustering algorithms to partition the sample into discrete clusters, and characterize the relationships between genetic distance and ecological variables to identify factors with the greatest influence on gene flow at a local scale. Desert tortoises exhibit weak genetic structure at a local scale, and we identified two subpopulations across the study area. Although genetic differentiation between the subpopulations was low, our landscape genetic analysis identified both natural (slope) and anthropogenic (roads) landscape variables that have significantly influenced gene flow within this local population. We show that desert tortoise movements at a local scale are influenced by features of the landscape, and that these features are different than those that influence gene flow at larger scales. Our findings are important for desert tortoise conservation and management, particularly in light of recent translocation efforts in the region. More generally, our results indicate that recent landscape changes can affect gene flow at a local scale and that their effects can be detected almost immediately.     

Spatial Structure of Above-Ground Biomass Limits Accuracy of Carbon Mapping in Rainforest but Large Scale Forest Inventories Can Help to Overcome

Precise mapping of above-ground biomass (AGB) is a major challenge for the success of REDD+ processes in tropical rainforest. The usual mapping methods are based on two hypotheses: a large and long-ranged spatial autocorrelation and a strong environment influence at the regional scale. However, there are no studies of the spatial structure of AGB at the landscapes scale to support these assumptions. We studied spatial variation in AGB at various scales using two large forest inventories conducted in French Guiana. The dataset comprised 2507 plots (0.4 to 0.5 ha) of undisturbed rainforest distributed over the whole region. After checking the uncertainties of estimates obtained from these data, we used half of the dataset to develop explicit predictive models including spatial and environmental effects and tested the accuracy of the resulting maps according to their resolution using the rest of the data. Forest inventories provided accurate AGB estimates at the plot scale, for a mean of 325 Mg.ha^-1. They revealed high local variability combined with a weak autocorrelation up to distances of no more than10 km. Environmental variables accounted for a minor part of spatial variation. Accuracy of the best model including spatial effects was 90 Mg.ha^-1 at plot scale but coarse graining up to 2-km resolution allowed mapping AGB with accuracy lower than 50 Mg.ha^-1. Whatever the resolution, no agreement was found with available pan-tropical reference maps at all resolutions. We concluded that the combined weak autocorrelation and weak environmental effect limit AGB maps accuracy in rainforest, and that a trade-off has to be found between spatial resolution and effective accuracy until adequate “wall-to-wall” remote sensing signals provide reliable AGB predictions. Waiting for this, using large forest inventories with low sampling rate (<0.5%) may be an efficient way to increase the global coverage of AGB maps with acceptable accuracy at kilometric resolution.     

Joint Effects of Habitat Heterogeneity and Species’ Life-History Traits on Population Dynamics in Spatially Structured Landscapes

Both habitat heterogeneity and species’ life-history traits play important roles in driving population dynamics, yet there is little scientific consensus around the combined effect of these two factors on populations in complex landscapes. Using a spatially explicit agent-based model, we explored how interactions between habitat spatial structure (defined here as the scale of spatial autocorrelation in habitat quality) and species life-history strategies (defined here by species environmental tolerance and movement capacity) affect population dynamics in spatially heterogeneous landscapes. We compared the responses of four hypothetical species with different life-history traits to four landscape scenarios differing in the scale of spatial autocorrelation in habitat quality. The results showed that the population size of all hypothetical species exhibited a substantial increase as the scale of spatial autocorrelation in habitat quality increased, yet the pattern of population increase was shaped by species’ movement capacity. The increasing scale of spatial autocorrelation in habitat quality promoted the resource share of individuals, but had little effect on the mean mortality rate of individuals. Species’ movement capacity also determined the proportion of individuals in high-quality cells as well as the proportion of individuals experiencing competition in response to increased spatial autocorrelation in habitat quality. Positive correlations between the resource share of individuals and the proportion of individuals experiencing competition indicate that large-scale spatial autocorrelation in habitat quality may mask the density-dependent effect on populations through increasing the resource share of individuals, especially for species with low mobility. These findings suggest that low-mobility species may be more sensitive to habitat spatial heterogeneity in spatially structured landscapes. In addition, localized movement in combination with spatial autocorrelation may increase the population size, despite increased density effects.