A multi-scale analysis of western spruce budworm outbreak dynamics

Context

Forest insect outbreaks are influenced by ecological processes operating at multiple spatial scales, including host-insect interactions within stands and across landscapes that are modified by regional-scale variations in climate. These drivers of outbreak dynamics are not well understood for the western spruce budworm, a defoliator that is native to forests of western North America.

Objectives

Our aim was to assess how processes across multiple spatial scales drive western spruce budworm outbreak dynamics. Our objective was to assess the relative importance and influence of a set of factors covering the stand, landscape, and regional scales for explaining spatiotemporal outbreak patterns in British Columbia, Canada.

Methods

We used generalized linear mixed effect models within a multi-model interference framework to relate annual budworm infestation mapped from Landsat time series (1996–2012) to sets of stand-, landscape-, and regional-scale factors derived from forest inventory data, GIS analyses, and climate models.

Results

Outbreak patterns were explained well by our model (R ² = 93%). The most important predictors of infestation probability were the proximity to infestations in the previous year, landscape-scale host abundance, and dry autumn conditions. While stand characteristics were overall less important predictors, we did find infestations were more likely amongst pure Douglas-fir stands with low site indices and high crown closure.

Conclusions

Our findings add to growing empirical evidence that insect outbreak dynamics are driven by multi-scaled processes. Forest management planning to mitigate the impacts of budworm outbreaks should thus consider landscape- and regional-scale factors in addition to stand-scale factors.

     

Influence of environmental factors and spatio-temporal covariates during the initial development of a spruce budworm outbreak

Recurrent and synchronous spruce budworm (SBW) outbreaks have important impacts in boreal and sub-boreal forest ecosystems of North America. This study examines the early phase of an outbreak that was developing across a 268,000 km² area over a period of 9 years (2003–2011). The territory was subdivided in 225 km² cells, and the relative influence of forest composition, elevation, forest age, average degree-days and soil drainage were examined during three development phases of the outbreak: initial epicenter location, relatively long-distance spread (cell-to-cell expansion), and expansion inside individual cells (within-cell expansion). The results indicate that elevation is the most determinant variable for initial epicenter location. Other variables that were identified as important for outbreak development by previous studies, such as forest composition and average degree-days, were not so important during this phase. However, forest composition and average degree-days were important factors during the cell-to-cell and within-cell expansion phases. Separating outbreak development in distinct phases also allowed to integrate phase-specific spatial and temporal covariates that were highly significant in the models, such as distance from previous year defoliations during the cell-to-cell expansion phase, and the proportion of defoliated stands during the preceding year for the within-cell expansion phase. Overall, this study provides limited evidence that patterns of SBW outbreak expansion could be altered by reducing host tree species abundance in the forest [mainly balsam fir (Abies balsamea) in this region]. More generally, this study suggests that the influence of environmental variables on SBW outbreak development is clearly phase-dependent, and that this landscape-level, process-based approach could be useful to forecast insect outbreak development in forest ecosystems.     

Spatial dynamics of a gypsy moth defoliation outbreak and dependence on habitat characteristics

Forest insects cause defoliation disturbances with complex spatial dynamics. These are difficult to measure but critical for models of disturbance risk that inform forest management. Understanding of spatial dynamics has lagged behind other disturbance processes because traditional defoliation sketch map data often suffered from inadequate precision or spatial resolution. We sought to clarify the influence of underlying habitat characteristics on outbreak patterns by combining forest plots, GIS data and defoliation intensity maps modeled from Landsat imagery. We quantified dependence of defoliation on spatial patterns of host abundance, phenology, topography, and pesticide spray for a recent gypsy moth outbreak (2000–2001), in a mixed deciduous forest in western Maryland, USA. We used semivariograms and hierarchical partitioning to quantify spatial patterns and variable importance. Habitat characteristics from plot data explained 21 % of defoliation variance in 2000 from tree density, phenological asynchrony, pesticide spray status, and landform index and 34 % of the variance in 2001 from previous-year defoliation, relative abundance of non-host species, phenological asynchrony, pesticide spray status, and relative slope position. Spatial autocorrelation in residual defoliation ranged over distances of 788 m in 2000 and 461 m in 2001, corresponding well with gypsy moth larval dispersal distances (100 m to 1 km). Un-measured processes such as predation, virus and pathogen occurrence likely contribute to unexplained variance. Because the spatial dynamics of these factors are largely unknown, our results support modeling gypsy moth defoliation as a function of dependence on significant exogenous characteristics and residual spatial pattern matching.     

An invasive species’ relationship with environmental variables changes across multiple spatial scales

To further our understanding of invasive species?? novel distributions, knowledge of invasive species?? relationships with environmental variables at multiple spatial scales is paramount. Here, we investigate which environmental variables and which spatial scales best explain the invasive mute swan??s (Cygnus olor) distribution in southern Ontario (Canada). Specifically we model mute swan distribution changes according to ecologically-relevant spatial scales: average territory size radius, 140?m; median dispersal distance of cygnets, 3,000?m; and average activity distance of males, 8,000?m. For individual spatial scales, global models using variables measured at each particular scale result in the highest Akaike weights, AUC, and Cohen??s Kappa values. Yet composite models (models combining variables measured at different scales) elicit the best models, as determined by higher Akaike weights and high AUC and Cohen??s Kappa values. Overall, percent water, waterbody perimeter density, temperature, precipitation, and road density are positively correlated with mute swan distribution, while percent forest and elevation are negatively correlated at all scales of analysis. Only percent water and annual precipitation are more influential in determining mute swan distribution at the 3,000 and 8,000?m zone scales than the territory scale. While most species distribution models are performed at a single scale, the results of our study suggest that composite models reflecting a species?? ecological needs provide models of better fit with similar, if not better, predictive accuracy. When analyzing species distributions, we also recommend that ecologists consider the scale of the underlying landscape processes and the effect that this may have on their modelling outcomes.     

Quantifying the spatial relationship between bird species’ distributions and landscape feature boundaries in southern Ontario, Canada

Understanding what features of the landscape affect species distribution is critical to effectively implement conservation strategies. This study investigates how a boundary analysis framework can be used to characterize the spatial association between boundaries (i.e., spatial locations of high rates of change) in bird species?? distributions and landscape features at the regional scale. The study area covers 92,000?km² in southern Ontario (Canada) and extends from the Great Lakes-St. Lawrence biome to the southern Canadian Shield biome. Landcover composition was derived from Ontario Land Cover data (1991?C1998; 7 types) and elevation data were derived from the Canada3D digital elevation model. Bird distributions were estimated using indicator kriging based on point counts obtained from the Ontario Breeding Bird Atlas data (2001?C2005; 60 species). Boundaries were delineated for both data types using a 10?×?10?km cell resolution. Spatial boundary overlap statistics were used to quantify the spatial relationship between landscape features and bird boundaries and tested using a randomization procedure. There was significant positive association and spatial overlap between delineated landscape feature boundaries and bird boundaries. The number of spatially overlapping cells between the two boundary types was 67 out of 164 (41?%) and 76?% of cells were within 11.42?km of each other. These results were statistically significant (P?<?0.001) and suggest a strong spatial relationship between high rates of change in landscape features and bird species?? distributions at the regional scale. A boundary analysis framework could be used to identify boundary shifts in response to climate change and anticipate changes in species distributions.     

Spatial prediction of caterpillar (<Emphasis Type="Italic">Ormiscodes</Emphasis>) defoliation in Patagonian <Emphasis Type="Italic">Nothofagus</Emphasis> forests

In the temperate forests of the southern Andes, southern beech species (Nothofagus), the dominant tree species of the region, experience severe defoliation caused by caterpillars of the Ormiscodes genus (Lepidoptera: Saturniidae). Despite the recent increase in defoliation frequency in some areas, there is no quantitative information on the spatial extent and dynamics of these outbreaks. This study examines the spatial patterns of O. amphimone outbreaks in relation to landscape heterogeneity. We mapped defoliation events caused by O. amphimone in northern (ca. 40–41°S) and southern Patagonian (ca. 49°S) Nothofagus forests from Landsat imagery and analyzed their spatial associations with vegetation cover type, topography (elevation, slope angle, aspect) and mean annual precipitation using overlay analyses. We used these data and relationships to develop a logistic regression model in order to generate maps of predicted susceptibility to defoliation by O. amphimone for each study area. Forests of N. pumilio are typically more susceptible to O. amphimone outbreaks than lower elevation forests of other Nothofagus species (N. dombeyi and N. antarctica). Stands located at intermediate elevations and on gentle slopes (<15°) are also more susceptible to defoliation than higher and lower elevation stands located on high angle slopes. Stands in areas with intermediate to high precipitation, relative to the distribution of Nothofagus along the precipitation gradient, are more susceptible to O. amphimone attack than are drier areas. Our study represents the first mapping and spatial analysis of insect defoliator outbreaks in Nothofagus forests in South America.     

Does the scale of our observational window affect our conclusions about correlations between endangered salmon populations and their habitat?

Differences in the strength of species-habitat relationships across scales provide insights into the mechanisms that drive these relationships and guidance for designing in situ monitoring programs, conservation efforts and mechanistic studies. The scale of our observation can also impact the strength of perceived relationships between animals and habitat conditions. We examined the relationship between geographic information system (GIS)-based landscape data and Endangered Species Act-listed anadromous Pacific salmon (Oncorhynchus spp.) populations in three subbasins of the Columbia River basin, USA. We characterized the landscape data and ran our models at three spatial scales: local (stream reach), intermediate (6th field hydrologic units directly in contact with a given reach) and catchment (entire drainage basin). We addressed three questions about the effect of scale on relationships between salmon and GIS representations of landscape conditions: (1) at which scale does each predictor best correlate with salmon redd density, (2) at which scale is overall model fit maximized, and (3) how does a mixed-scale model compare with single scale models (mixed-scale meaning models that contain variables characterized at different spatial scales)? We developed mixed models to identify relationships between redd density and candidate explanatory variables at each of these spatial scales. Predictor variables had the strongest relationships with redd density when they were summarized over the catchment scale. Meanwhile strong models could be developed using landscape variables summarized at only the local scale. Model performance did not improve when we used suites of potential predictors summarized over multiple scales. Relationships between species abundance and land use or intrinsic habitat suitability detected at one scale cannot necessarily be extrapolated to other scales. Therefore, habitat restoration efforts should take place in the context of conditions found in the associated watershed or landscape.     

Using Neutral Models to Identify Constraints on Low-severity Fire Regimes

Climate, topography, fuel loadings, and human activities all affect spatial and temporal patterns of fire occurrence. Because fire is modeled as a stochastic process, for which each fire history is only one realization, a simulation approach is necessary to understand baseline variability, thereby identifying constraints, or forcing functions, that affect fire regimes. With a suitable neutral model, characteristics of natural fire regimes estimated from fire history data can be compared to a “null hypothesis”. We generated random landscapes of fire-scarred trees via a point process with sequential spatial inhibition. Random ignition points, fire sizes, and fire years were drawn from uniform and exponential family probability distributions. We compared two characteristics of neutral fire regimes to those from five watersheds in eastern Washington that have experienced low-severity fire. Composite fire intervals (CFIs) at multiple spatial scales displayed similar monotonic decreases with increasing sample area in neutral vs. real landscapes, although patterns of residuals from statistical models differed. In contrast, parameters of the Weibull distribution associated with temporal trends in fire hazard exhibited different forms of scale dependence in real vs. simulated data. Clear patterns in neutral landscapes suggest that deviations from them in empirical data represent real constraints on fire regimes (e.g., topography, fuels). As with any null model, however, neutral fire-regime models need to be carefully tuned to avoid confounding these constraints with artifacts of modeling. Neutral models show promise for investigating low-severity fire regimes to separate intrinsic properties of stochastic processes from the effects of climate, fuel loadings, topography, and management.     

Simulated village locations in Thailand: a multi-scale model including a neural network approach

The simulation of rural land use systems in general, and rural settlement dynamics in particular, has developed with synergies of theory and methods for decades. Three current issues are: linking spatial patterns and processes, representing hierarchical relations across scales, and considering nonlinearity to address complex non-stationary settlement dynamics. We present a hierarchical simulation model to investigate complex rural settlement dynamics in Nang Rong, Thailand. This simulation uses sub-models to allocate new villages at three spatial scales. Regional and sub-regional models, which involve a nonlinear space–time autoregressive model implemented in a neural network approach, determine the number of new villages to be established. A dynamic village niche model, establishing a pattern–process link, was designed to enable the allocation of villages into specific locations. Spatiotemporal variability in model performance indicates that the pattern of village location changes as a settlement frontier advances from rice-growing lowlands to higher elevations. Simulation experiments demonstrate that this simulation model can enhance our understanding of settlement development in Nang Rong and thus gain insight into complex land use systems in this area.     

Forecasting the spatial dynamics of gypsy moth outbreaks using cellular transition models

A series of cellular transition probability models that predict the spatial dynamics of gypsy moth (Lymantria dispar L.) defoliation were developed. The models consisted of four classes: Simple Markov chains, Rook's and Queen's move neighborhood models, and distance weighted neighborhood models. Historical maps of gypsy moth defoliation across Massachusetts from 1961 to 1991 were digitized into a binary raster matrix and used to estimate transition probabilities. Results indicated that the distance weighted neighborhood model performed better then the other neighborhood models and the simple Markov chain. Incorporation of interpolated counts of overwintering egg mass counts taken throughout the state and incorporation of historical defoliation frequencies increased the performance of the transition models.     

Scaling Local Species-habitat Relations to the Larger Landscape with a Hierarchical Spatial Count Model

Much of what is known about avian species-habitat relations has been derived from studies of birds at local scales. It is entirely unclear whether the relations observed at these scales translate to the larger landscape in a predictable linear fashion. We derived habitat models and mapped predicted abundances for three forest bird species of eastern North America using bird counts, environmental variables, and hierarchical models applied at three spatial scales. Our purpose was to understand habitat associations at multiple spatial scales and create predictive abundance maps for purposes of conservation planning at a landscape scale given the constraint that the variables used in this exercise were derived from local-level studies. Our models indicated a substantial influence of landscape context for all species, many of which were counter to reported associations at finer spatial extents. We found land cover composition provided the greatest contribution to the relative explained variance in counts for all three species; spatial structure was second in importance. No single spatial scale dominated any model, indicating that these species are responding to factors at multiple spatial scales. For purposes of conservation planning, areas of predicted high abundance should be investigated to evaluate the conservation potential of the landscape in their general vicinity. In addition, the models and spatial patterns of abundance among species suggest locations where conservation actions may benefit more than one species.     

Modelling land cover transitions: A solution to the problem of spatial dependence in data

Raster-based spatial land cover transition models (LCTMs) are widely used in landscape ecology. However, many LCTMs do not account for spatial dependence of the input data, which may artificially fragment the output spatial configuration. We demonstrate the consequences of ignoring spatial dependence, thus assigning probabilities randomly in space, using a simple LCTM. We ran the model from four different initial conditions with distinct spatial configurations and results indicated that, after 20 simulation steps, all of them converged towards the spatial configuration of the random data set. From an ecological perspective this is a serious problem because ecological data often exhibit distinct spatial configuration related to ecological processes. As a solution, we propose an approach (region approach) that accounts for spatial dependence of LCTM input data. Underlying spatial dependence was used to apply spatial bias to probability assignment within the model. As a case study we applied a region approach to a Vegetation Transition Model (VTM); a semi-Markovian model that simulates forest succession. The VTM was applied to approximately 500,000 ha of boreal forest in Ontario, at 1 ha pixel resolution. When the stochastic transition algorithms were applied without accounting for spatial dependence, spatial configuration of the output data became progressively more fragmented. When the VTM was applied using the region approach to account for spatial dependence output fragmentation was reduced. Accounting for spatial dependence in transition models will create more reliable output for analyzing spatial patterns and relating those patterns to ecological processes.This revised version was published online in May 2005 with corrections to the Cover Date.     

The cost of gypsy moth sex in the city

Since its introduction in the 1860s, gypsy moth, Lymantria dispar (L.), has periodically defoliated large swaths of forest in the eastern United States. Prior research has suggested that the greatest costs and losses from these outbreaks accrue in residential areas, but these impacts have not been well quantified. We addressed this lacuna with a case study of Baltimore City. Using two urban tree inventories, we estimated potential costs and losses from a range of gypsy moth outbreak scenarios under different environmental and management conditions. We combined outbreak scenarios with urban forest data to model defoliation and mortality and based the costs and losses on the distribution of tree species in different size classes and land uses throughout Baltimore City. In each outbreak, we estimated the costs of public and private suppression, tree removal and replacement, and human medical treatment, as well as the losses associated with reduced pollution uptake, increased carbon emissions and foregone sequestration. Of the approximately 2.3 M trees in Baltimore City, a majority of the basal area was primary or secondary host for gypsy moth. Under the low outbreak scenario, with federal and state suppression efforts, total costs and losses were $5.540 M, much less than the $63.666 M estimated for the high outbreak scenario, in which the local public and private sectors were responsible for substantially greater tree removal and replacement costs. The framework that we created can be used to estimate the impacts of other non-native pests in urban environments.     

Boundaries and scales in shrublands of the Doñana Biological Reserve,southwest Spain

To verify that the stabilized sand dunes of Doñana, southwest Spain, are hierarchically nested, vegetation was sampled along topographic gradients at three spatial scales and Split Moving Window Boundary Analysis was applied to identify vegetation boundaries and ecotones. At small scale, only one window width was used, while for boundaries detection at upper scales the information from five windows was pooled. Environmental factors controlling plant composition were studied along topographic gradients, and diversity was estimated within the boundaries. According to several theoretical frameworks, I discuss the types of boundaries produced at different scales. Lower level boundaries are characterized by transitory gradients linked to local exchanges; intermediate boundaries are symmetric and very stable over the time; the large scale boundary is asymmetric with strong inherent abiotic constraints reinforced by strong biotic feedbacks. In spite of a similar plant composition, a plant community, the mixed shrub, works as an ecocline or an ecotone depending on the spatial scale considered. A certain parallelism exists between shrub composition along dune slopes and dune generations; however, processes at upper scale constraint plant composition at lower scale resulting in different mature formations.     

Species Dynamics in Disturbed Landscapes: When does a Shifting Habitat Mosaic Enhance Connectivity?

Although landscape ecology emphasizes the effects of spatial pattern on ecological processes, most neutral models of species–habitat relationships have treated habitat as a static constraint. Do the working hypotheses derived from these models extend to real landscapes where disturbances create a shifting mosaic? A spatial landscape simulator incorporating vegetation dynamics and a metapopulation model was used to compare species in static and dynamic landscapes with identical habitat amounts and spatial patterns. The main drivers of vegetation dynamics were stand-replacing disturbances, followed by gradual change from early-successional to old-growth habitats. Species dynamics were based on a simple occupancy model, with dispersal simulated as a random walk. As the proportion of available habitat (p) decreased from 1.0, species occupancy generally declined more rapidly and reached extinction at higher habitat levels in dynamic than in static landscapes. However, habitat occupancy was sometimes actually higher in dynamic landscapes than in static landscapes with similar habitat amounts and patterns. This effect was most pronounced at intermediate amounts of habitat (p = 0.3?0.6) for mobile species that had high colonization rates, but were unable to cross non-habitat patches. Differences between static and dynamic landscapes were contingent upon the initial metapopulation size and the shapes of disturbances and the resulting habitat patterns. Overall, the results demonstrate that dispersal-limited species exhibit more pronounced critical behavior in dynamic landscapes than is predicted by simple neutral models based on static landscapes. Thus, caution should be exercised in extending generalizations derived from static landscape models to disturbance-driven landscape mosaics.     

Hierarchical relationships between landscape structure and temperature in a managed forest landscape

Management may influence abiotic environments differently across time and spatial scale, greatly influencing perceptions of fragmentation of the landscape. It is vital to consider a priori the spatial scales that are most relevant to an investigation, and to reflect on the influence that scale may have on conclusions. While the importance of scale in understanding ecological patterns and processes has been widely recognized, few researchers have investigated how the relationships between pattern and process change across spatial and temporal scales. We used wavelet analysis to examine the multiscale structure of surface and soil temperature, measured every 5 m across a 3820 m transect within a national forest in northern Wisconsin. Temperature functioned as an indicator – or end product – of processes associated with energy budget dynamics, such as radiative inputs, evapotranspiration and convective losses across the landscape. We hoped to determine whether functional relationships between landscape structure and temperature could be generalized, by examining patterns and relationships at multiple spatial scales and time periods during the day. The pattern of temperature varied between surface and soil temperature and among daily time periods. Wavelet variances indicated that no single scale dominated the pattern in temperature at any time, though values were highest at finest scales and at midday. Using general linear models, we explained 38% to 60% of the variation in temperature along the transect. Broad categorical variables describing the vegetation patch in which a point was located and the closest vegetation patch of a different type (landscape context) were important in models of both surface and soil temperature across time periods. Variables associated with slope and microtopography were more commonly incorporated into models explaining variation in soil temperature, whereas variables associated with vegetation or ground cover explained more variation in surface temperature. We examined correlations between wavelet transforms of temperature and vegetation (i.e., structural) pattern to determine whether these associations occurred at predictable scales or were consistent across time. Correlations between transforms characteristically had two peaks; one at finer scales of 100 to 150 m and one at broader scales of ^>300 m. These scales differed among times of day and between surface and soil temperatures. Our results indicate that temperature structure is distinct from vegetation structure and is spatially and temporally dynamic. There did not appear to be any single scale at which it was more relevant to study temperature or this pattern-process relationship, although the strongest relationships between vegetation structure and temperature occurred within a predictable range of scales. Forest managers and conservation biologists must recognize the dynamic relationship between temperature and structure across landscapes and incorporate the landscape elements created by temperature-structure interactions into management decisions.     

Linking vegetation patterns to environmental gradients and human impacts in a mediterranean-type island ecosystem

Vegetation patterns at the landscape scale are shaped by myriad processes and historical events, and understanding the relative importance of these processes aids in predicting current and future plant distributions. To quantify the influence of different environmental and anthropogenic patterns on observed vegetation patterns, we used simultaneous autoregressive modeling to analyze data collected by the Carnegie Airborne Observatory over Santa Cruz Island (SCI; California, USA). SCI is a large continental island, and its limited suite of species and well documented land use history allowed us to consider many potential determinants of vegetation patterns, such as topography, substrate, and historical grazing intensity. As a metric of vegetation heterogeneity, we used the normalized difference vegetation index (NDVI) stratified into three vegetation height classes using LiDAR (short, medium, and tall). In the SAR models topography and substrate type were important controls, together explaining 8–15 % of the total variation in NDVI, but historical grazing and spatial autocorrelation were also key components of the models, together explaining 17–21 % of the variation in NDVI. Optimal spatial autocorrelation distances in the short and medium height vegetation models (600–700 m) were similar to the home range sizes of two crucial seed dispersers on the island– the island fox (Urocyon littoralis santacruzae) and the island scrub-jay (Aphelocoma insularis)—suggesting that these animals may be important drivers of the island’s vegetation patterns. This study highlights the importance of dynamic processes like dispersal limitation and disturbance history in determining present-day vegetation patterns.     

Habitat variables explain Loggerhead Shrike occurrence in the northern Chihuahuan Desert,but are poor correlates of fitness measures

Conservation efforts should be based on habitat models that identify areas of high quality and that are built at spatial scales that are ecologically relevant. In this study, we developed habitat models for the Loggerhead Shrike (Lanius ludovicianus) in the Chihuahuan Desert of New Mexico to answer two questions: (1) are highly used habitats of high quality for shrikes in terms of individual fitness? and (2) what are the spatial scales of habitat associations relevant to this species? Our study area was Fort Bliss Army Reserve (New Mexico). Bird abundance was obtained from 10 min point counts conducted at forty-two 108 ha plots during a 3-year period. Measures of fitness were obtained by tracking a total of 73 nests over the 3 years. Habitat variables were measured at spatial scales ranging from broad to intermediate to local. We related habitat use and measures of fitness to habitat variables using Bayesian model averaging. We found a significant relationship between bird abundance and measures of fitness averaged across nesting birds in each plot (correlation up to 0.61). This suggests that measures of habitat use are indicative of habitat quality in the vicinity of Fort Bliss. Local- and intermediate-scale variables best explained shrike occurrence. Habitat variables were not related to any measures of fitness. A better understanding of the factors that limit individual bird fitness is therefore necessary to identify areas of high conservation value for this species.     

A positive response of mountain pine beetle to pine forest-clearcut edges at the landscape scale in British Columbia,Canada

In British Columbia, large-scale salvage harvesting has been underway to recover timber value from forest stands infested by mountain pine beetle during the current outbreak. Understanding the response of beetles to clearcut edges particularly at the landscape scale is crucial to understanding the impacts of increased habitat fragmentation due to salvage harvesting on the spread of the beetle infestations. A novel proximity analysis approach based on null models of complete spatial randomness with three different spatial extents was developed to examine the spatial patterns of infestations in relation to cutblocks. Inhomogeneous Poisson point process models were fitted to predict how intensities of infestations varied with distances to the nearest cutblocks. Marked Poisson point process models were also fitted to evaluate the effects of the variables associated with the nearest cutblocks and adjacent infested pine stands on the edge response of beetles. The results clearly illustrated a significant positive edge response of beetles at the landscape scale. The intensities of infestations decreased non-linearly with distances to the nearest cutblocks. The results also suggested that the quality and distribution of key habitat resources could not fully explain the fundamental mechanisms underlying the edge response. The behavioural change of beetle dispersal at edges may also be an important factor contributing to a positive edge response. The results from this study may be useful in improving the efficacy of mountain pine beetle management efforts.     

Maintaining or restoring connectivity of modified landscapes: evaluating the least-cost path model with multiple sources of ecological information

Habitat connectivity is an important element of functioning landscapes for mobile organisms. Maintenance or creation of movement corridors is one conservation strategy for reducing the negative effects of habitat fragmentation. Numerous spatial models exist to predict the location of movement corridors. Few studies, however, have investigated the effectiveness of these methods for predicting actual movement paths. We used an expert-based model and a resource selection function (RSF) to predict least-cost paths of woodland caribou. Using independent data for model evaluation, we found that the expert-based model was a poor predictor of long-distance animal movements; in comparison, the RSF model was effective at predicting habitat selection by caribou. We used the Path Deviation Index (PDI), cumulative path cost, and sinuosity to quantitatively compare the spatial differences between inferred caribou movement paths and predicted least-cost paths, and quasi-random null models of directional movement. Predicted movement paths were on average straighter than inferred movement paths for collared caribou. The PDI indicated that the least-cost paths were no better at predicting the inferred paths than either of two null models—straight line paths and randomly generated paths. We found statistically significant differences in cumulative cost scores for the main effects of model and path type; however, post-hoc comparisons were non-significant suggesting no difference among inferred, random, and predicted least cost paths. Paths generated from an expert based cost surface were more sinuous than those premised on the RSF model, but neither differed from the inferred path. Although our results are specific to one species, they highlight the importance of model evaluation when planning for habitat connectivity. We recommend that conservation planners adopt similar techniques when validating the effectiveness of movement corridors for other populations and species.