Using multiple metrics to estimate seasonal landscape connectivity for Blanding’s turtles (<Emphasis Type="Italic">Emydoidea blandingii</Emphasis>) in a fragmented landscape

Context

Landscapes and animal behavior can exhibit temporal variability and connectivity estimates should consider this phenomenon. In many species, timing of activities such as nesting, mate searching, and hibernation occurs during distinct periods in which movement events may differ, along with physical characteristics of the surrounding landscape.

Objectives

We estimate movement, landscape conductance, and patch importance for a turtle species across two seasonal activity periods (spring, late summer) in a fragmented agricultural region. Three connectivity approaches are compared to identify their advantages and disadvantages.

Methods

A least-cost distance model, circuit-based approach, and patch-based index were used to collectively describe the potential functional connectivity of Blanding’s turtle (Emydoidea blandingii) across a multi-temporal scale in an agricultural region of south western Ontario.

Results

Connectivity decreased further into the active season exhibited through lower conductance of the landscape and fewer pathways, while the importance of habitat nodes shifted due to temporal variability in the number and distribution of nodes. Models provided different yet complimentary information, with least-cost models overestimating discrete pathways yet providing a secondary measure of landscape barriers. The circuit-based model estimated corridors of least resistance providing an overall characterization of the landscape, while patch-based indices provided key information on the importance of individual habitat patches.

Conclusion

Findings highlight the importance of including a temporal aspect in connectivity modelling as results demonstrate a change in functional connectivity over time. We also recommend employing multiple connectivity metrics to capture variation in movement behavior.

     

Host functional connectivity and the spread potential of Lyme disease

Context

In the ecology of Lyme disease emergence, it remains unclear to what extent spread of the tick vector (Ixodes scapularis) and the pathogen (Borrelia burgdorferi) are dependent upon the dispersal of vertebrate hosts in spatially heterogeneous landscapes. Yet, empirical measure of these complex ecologically driven spread processes present conceptual and methodological challenges despite important public health implications.

Objectives

To examine the relationship between landscape characteristics and tick-borne disease spread, we modeled the influence of landscape connectivity for a simplified vertebrate host community (white-footed mouse—Peromyscus leucopus, American robin—Turdus migratorius, white-tailed deer—Odocoileus virginianus) on the potential spread of the tick population compared to the pathogen in a spatially-structured landscape.

Methods

We parameterized a hybrid demographic-dispersal connectivity model by combining a series of reported host dispersal and tick burden estimates with empirically-measured tick abundance and pathogen prevalence sampled from a Lyme-endemic island landscape in Thousand Islands National Park (Ontario, Canada) and simulated several tick- and pathogen-spread scenarios.

Results

The extent of tick spread by mice [amount of reachable habitat (ARH)?=?18.0%] is considerably similar to that of robins (ARH?=?18.7%), while deer support the greatest tick spread extent (ARH?=?82.0%). Infected mice carrying ticks support the highest pathogen spread (ARH?=?19.8%). Short-distance pathogen spread and long-distance tick spread were facilitated by intermediate stepping stone habitat fragments.

Conclusions

We provide evidence that host functional connectivity mediates tick spread differently than pathogen spread, and depends strongly on landscape configuration. Our study therefore emphasizes the importance of landscape spatial heterogeneity on the ecological processes that influence regional tick-borne disease spread.

     

Identifying significant scale-specific spatial boundaries using wavelets and null models: spruce budworm defoliation in Ontario,Canada as a case study

We combine wavelet analysis and multiple null models to identify significant spatial scales of pattern and spatial boundaries in historical spruce budworm defoliation in Ontario, Canada. Previous analyses of budworm defoliation in Ontario over the last two outbreaks have suggested three distinct zones of defoliation. We asked the following three questions: (1) is there statistical support for the existence of these three zones? (2) Are the locations of these boundaries consistent between outbreak periods? And (3) how does boundary identification depend on the spatial null model used? Defoliation data for the two outbreak periods (1941–1965 and 1966–2001), and the combined period (1941–2001) were analyzed using a 1D continuous wavelet transform. Boundaries were identified through comparison of wavelet power spectra of each outbreak period to reference distributions based on three different spatial null models: (1) a complete spatial randomness model, (2) an autoregressive model, and (3) a Gaussian random field model. The Gaussian random field model identified coarser scales of pattern than the autoregressive model. Locally, the Gaussian random field model found significant boundaries similar to those previously described, whereas the autoregressive model only did so for the first outbreak. These results indicate that the coarse scale spatial factors that influenced defoliation were more consistent between outbreaks relative to fine scale factors, and that previously described boundaries were strongly driven by the first outbreak. Wavelet analysis combined with spatial null models provides a powerful tool for identifying non-arbitrary scales of structure and significant spatial boundaries in non-stationary ecological data.     

The sensitivity of least-cost habitat graphs to relative cost surface values

Maintaining and restoring connectivity among high-quality habitat patches is recognized as an important goal for the conservation of animal populations. To provide an efficient measure of potential connectivity pathways in heterogeneous landscapes, least-cost route analysis has been combined with graph-theoretical techniques. In this study we use spatially explicit least-cost habitat graphs to examine how matrix quality and spatial configuration influence assessments of habitat connectivity. We generated artificial landscapes comprised of three landcover types ranked consistently from low to high quality: inhospitable matrix, hospitable matrix, and habitat. We controlled the area and degree of fragmentation of each landcover in a factorial experiment for a total of 20 combinations replicated 100 times. In each landscape we compared eight sets of relative landcover qualities (cost values of 1 for habitat, between 1.5 and 150 for hospitable matrix, and 3–10,000 for inhospitable matrix). We found that the spatial location of least-cost routes was sensitive to differences in relative cost values assigned to landcover types and that the degree of sensitivity depended on the spatial structure of the landscape. Highest sensitivity was found in landscapes with fragmented habitat and between 20 and 50% hospitable matrix; sensitivity decreased as habitat fragmentation decreased and the amount of hospitable matrix increased. As a means of coping with this sensitivity, we propose identifying multiple low-cost routes between pairs of habitat patches that collectively delineate probable movement zones. These probable movement zones account for uncertainty in least-cost routes and may be more robust to variation in landcover cost values.     

Differentiation of Phoma foveata from P. exigua using a RAPD Generated PCR-RFLP Marker

Phoma foveata and P. exigua variety exigua both infect potatoes and are morphologically very similar. P. foveata produces a pigment which allows differentiation from P. exigua in culture. Discrimination of the two species based on the production of a secondary metabolite, which is dependent on the growth conditions, is not reliable. Therefore, there is a need to develop nucleic acid based identification markers. A 482bp random amplified polymorphic DNA (RAPD) fragment from P. foveata was isolated and sequenced. Polymerase chain reaction (PCR) primers, developed from the sequence of the RAPD product, amplified a 474bp fragment for P. foveata and P. exigua varieties exigua, diversispora, inoxydibilis and sambuci-nigrae. The similarity of the PCR fragments was demonstrated by sequence analysis and by using the restriction enzymes DdeI and DpnII. P. foveata was distinguished from the four varieties of P. exigua on the basis of the RFLP patterns of the PCR fragment. Ten isolates of P. foveata and nine of P. exigua var. exigua from different geographic locations were tested and all isolates but one showed the restriction digest pattern of the PCR fragment (PCR-RFLP) specific to each species. One isolate of P. foveata demonstrated a PCR-RFLP pattern similar to P. exigua var. exigua leading to the conclusion that the isolate had been previously misidentified as a strain of P. foveata lacking the ability to produce pigment.     

Integrating landscape resistance and multi-scale predictor of habitat selection for amphibian distribution modelling at large scale

Context

Species distribution modelling is a common tool in conservation biology but two main criticisms remain: (1) the use of simplistic variables that do not account for species movements and/or connectivity and (2) poor consideration of multi-scale processes driving species distributions.

Objectives

We aimed to determine if including multi-scale and fine-scale movement processes in SDM predictors would improve accuracy of SDM for low-mobility amphibian species compared with species-level analysis.

Methods

We tested and compared different SDMs for nine amphibian species with four different sets of predictors: (1) simple distance-based predictors; (2) single-scale compositional predictors; (3) multi-scale compositional predictors with a priori selection of scale based on knowledge of species mobility and scale-of-effect; and (4) multi-scale compositional predictors calculated using a friction-based functional grain to account for resource accessibility with landscape resistance to movement.

Results

Using friction-based functional grain predictors produced slight to moderate improvements of SDM performance at large scale. The multi-scale approach, with a priori scale selection, led to ambiguous results depending on the species studied, in particular for generalist species.

Conclusion

We underline the potential of using a friction-based functional grain to improve SDM predictions for species-level analysis.

     

Assessing Spatial Uncertainty Associated with Forest Fire Boundary Delineation

Uncertainty in managing forested landscapes arises from many sources, including complexities inherent in forest ecosystems and their disturbance processes. However, gaining knowledge about forested ecosystems at the landscape level is often impeded by limitations in collecting comprehensive, representative, as well as accurate data sets. Historical reference data sets about past disturbances are also mostly lacking. In the case of ground fires, however, records of past fires can be obtained by analyzing fire scars using dendrochronology. While the temporal series of disturbance can be determined, there is still uncertainty about the spatial limits of individual forest surface fires. Here, we investigate how a patch-based method (fuzzy set membership) and a boundary-based uncertainty method (boundary membership) can help determine the spatial uncertainty related to forest fire events and their boundary locations. We compare these methods using fire scar data from ponderosa pine (Pinus ponderosa) and Douglas-fir (Pseudotsuga menziesii) sampled at 33 1-ha plots in a 1500-ha study area within the Stein River watershed (British Columbia). Patch-based fire maps, using multiple constraints, were derived for years 1785–1937. We compared the resulting total fire event maps with the boundary-based method, finding that depending on values chosen for the patch-based method, negative correlation was present (though very modest: r = − 0.1, p ≤ 0.05) between some maps. However, significant positive correlation between maps (though again modest: r = 0.22, p ≤ 0.05) was found under the least constrained patch-based methods, suggesting that fire patches are counted more than once in riparian zones. Our results suggest that these two methods provide complementary information about historical fire size and spatial limits. Quantifying spatial uncertainty about fire size and fire boundary location using a boundary membership method can contribute to not only understanding past fire regimes but also to providing better estimates of area burned.     

The influence of landscape characteristics and home-range size on the quantification of landscape-genetics relationships

A common approach used to estimate landscape resistance involves comparing correlations of ecological and genetic distances calculated among individuals of a species. However, the location of sampled individuals may contain some degree of spatial uncertainty due to the natural variation of animals moving through their home range or measurement error in plant or animal locations. In this study, we evaluate the ways that spatial uncertainty, landscape characteristics, and genetic stochasticity interact to influence the strength and variability of conclusions about landscape-genetics relationships. We used a neutral landscape model to generate 45 landscapes composed of habitat and non-habitat, varying in percent habitat, aggregation, and structural connectivity (patch cohesion). We created true and alternate locations for 500 individuals, calculated ecological distances (least-cost paths), and simulated genetic distances among individuals. We compared correlations between ecological distances for true and alternate locations. We then simulated genotypes at 15 neutral loci and investigated whether the same influences could be detected in simple Mantel tests and while controlling for the effects of isolation-by-distance using the partial Mantel test. Spatial uncertainty interacted with the percentage of habitat in the landscape, but led to only small reductions in correlations. Furthermore, the strongest correlations occurred with low percent habitat, high aggregation, and low to intermediate levels of cohesion. Overall genetic stochasticity was relatively low and was influenced by landscape characteristics.     

Spatial autocorrelation and statistical tests: Some solutions

Spatial dependence or spatial autocorrelation often occurs in ecological data and can be a serious problem in analysis, affecting the significance rates of statistical tests, making them too liberal when the dependence is positive. Ecological phenomena often are patchy and give data with a wave structure, producing autocorrelation that cycles between positive and negative with increasing distance, further complicating the situation. This article describes the essentials of dealing with this problem as commonly encountered in analyzing ecological data for two variables. We investigated two related approaches to correcting statistical tests for data with spatial autocorrelation from one-dimensional sampling schemes like the transects used in plant ecology, the example of interest here. Both approaches estimate the “effective sample size” based on the observed autocorrelation structures of the variables. We examined tests of correlation and bivariate goodness-of-fit tests, as well as extensions beyond both of these test classes. The correction methods prove to be robust for a wide range of spatial autocorrelation structures in one-dimensional data and provide reliable corrections in most cases. They fail only when the data have strong and consistent waves that cause persistent cycles in the autocorrelation as a function of distance. By examining the spatial autocorrelation structure of the ecological data, we can predict the likelihood of successful correction for these bivariate tests.     

Identification of genetically modified potato (Solanum tuberosum) cultivars using event specific polymerase chain reaction

Several genetically modified (GM) cultivars are registered in Canada although they are not currently in commercial production. The GM cultivars can be distinguished from the non-GM and other GM cultivars by analyzing the DNA nucleotide sequence at the insertion site of the transgene corresponding to a single transformation event in the plant genome. Techniques based on modified polymerase chain reaction (PCR) strategies were used to generate sequence information from the plant genome flanking the insertion site of transgenic DNA for specific GM potato events. The plant genome sequence adjacent to the transgenic insertion was used to design PCR primers, which could be used in combination with a primer annealing to one of the nearby inserted genetic elements to amplify an event specific DNA fragment. The event specific PCR fragments generated were sequenced to confirm the specificity of the method.