Relative importance of landscape and species characteristics on extinction debt,immigration credit and relaxation time after habitat turnover

Habitat turnover concomitantly causes destruction and creation of habitat patches. Following such a perturbation, metapopulations harbor either an extinction debt or an immigration credit, that is the future decrease or increase in population numbers due to this disturbance. Extinction debt and immigration credit are rarely considered simultaneously and disentangled from the relaxation time (time to new equilibrium). In this contribution, we test the relative importance of two potential drivers of time-delayed metapopulation dynamics: the spatial configuration of the habitat turnover and species dispersal ability. We provide a simulation-based investigation projecting metapopulation dynamics following habitat turnover in virtual landscapes. We consider two virtual species (a short-distance and a long-distance disperser) and five scenarios of habitat turnover depending on net habitat loss or gain and habitat aggregation. Our analyses reveal that (a) the main determinant of the magnitude of the extinction debt or immigration credit is the net change in total habitat area, followed by species dispersal distance and finally by the post-turnover habitat aggregation; (b) relaxation time weakly depends on the magnitude of the immigration credit or of the extinction debt; (c) the main determinant of relaxation time is dispersal distance followed by the net change in total habitat area and finally by the post-turnover habitat aggregation. These results shed light on the relative importance of dispersal ability and habitat turnover spatial structure on the components of time-delayed metapopulation dynamics.     

Comparing clustered sampling designs for spatially explicit estimation of population density

Spatially explicit capture–recapture methods do not assume that animals have equal access to sampling devices (e.g., detectors), which allows for gaps in the sampling extent and nonuniform (e.g., clustered) sampling designs. However, the performance (i.e., relative root mean squared error [RRMSE], confidence interval coverage, relative bias and relative standard error) of clustered detector arrays has not been thoroughly evaluated. I used simulations to evaluate the performance of various detector and cluster spacings, cluster configurations (i.e., number of detectors arranged in a square grid), sampling extents and number of sampling occasions for estimating population density, the relationship between detection rate and distance to a detector from the animal's center of activity (σ) and base detection rates, using American black bears (Ursus americanus) as a case study. My simulations indicated that a wide range of detector configurations can provide reliable estimates if spacing between detectors in clusters is ≥1σ and ≤3σ. A number of cluster configurations and occasion lengths produced estimates that were unbiased, resulted in good spatial coverage, and were relatively precise. Moreover, increasing the duration of sampling, establishing large study areas, increasing detection rates and spacing clusters so that cross-cluster sampling of individuals can occur could help ameliorate deficiencies in the detector layout. These results have application for a wide array of species and sampling methods (e.g., DNA sampling, camera trapping, mark-resight and search-encounter) and suggest that clustered sampling can significantly reduce the effort necessary to provide reliable estimates of population density across large spatial extents that previously would have been infeasible with nonclustered sampling designs.     

Physiological model using diffuse reflectance spectroscopy for nonmelanoma skin cancer diagnosis

Diffuse reflectance spectroscopy (DRS) is a noninvasive, fast, and low‐cost technology with potential to assist cancer diagnosis. The goal of this study was to test the capability of our physiological model, a computational Monte Carlo lookup table inverse model, for nonmelanoma skin cancer diagnosis. We applied this model on a clinical DRS dataset to extract scattering parameters, blood volume fraction, oxygen saturation and vessel radius. We found that the model was able to capture physiological information relevant to skin cancer. We used the extracted parameters to classify (basal cell carcinoma [BCC], squamous cell carcinoma [SCC]) vs actinic keratosis (AK) and (BCC, SCC, AK) vs normal. The area under the receiver operating characteristic curve achieved by the classifiers trained on the parameters extracted using the physiological model is comparable to that of classifiers trained on features extracted via Principal Component Analysis. Our findings suggest that DRS can reveal physiologic characteristics of skin and this physiologic model offers greater flexibility for diagnosing skin cancer than a pure statistical analysis. Physiological parameters extracted from diffuse reflectance spectra data for nonmelanoma skin cancer diagnosis.     

Label‐free grading and staging of urothelial carcinoma through multimodal fibre‐probe spectroscopy

Urothelial carcinoma (UC) is the most common bladder tumour. Proper treatment requires tumour resection for diagnosing its grade (aggressiveness) and stage (invasiveness). White‐light cystoscopy and histopathological examination are the gold standard procedures for clinical and histopathological diagnostics, respectively. However, cystoscopy is limited in terms of specificity, histology requires long tissue processing, both procedures rely on operator's experience. Multimodal optical spectroscopy can provide a powerful tool for detecting, staging and grading bladder tumours in a fast, reliable and label‐free modality. In this study, we collected fluorescence, Raman and reflectance spectra from 50 biopsies obtained from 32 patients undergoing transurethral resection of bladder tumour using a multimodal fibre‐probe. Principal component analysis allowed distinguishing normal from pathological tissues, as well as discriminating tumour stages and grades. Each individual spectroscopic technique provided high specificity and sensitivity in classifying all tissues; however, a multimodal approach resulted in a considerable increase in diagnostic accuracy (≥95%), which is of paramount importance for tumour grading and staging. The presented method offers the potential for being applied in cystoscopy and for providing an automated diagnosis of UC at the clinical level, with an improvement with respect to current state‐of‐the‐art procedures.      

Methodological developments in the use of visible reflectance spectroscopy for discriminating pasture-fed from concentrate-fed lamb carcasses

The ability to authenticate the feed given to animals from the animal products has become a major challenge for scientists, monitoring bodies and commercial entities alike. This study compared two methods based on the use of the visible reflectance spectrum of the fat to discriminate pasture-fed (P) from stall concentrate-fed (S) lamb carcasses. A total of 307 (143 P and 164 S) Limousine lambs were used over 2 years. Pasture-fed lambs grazed a permanent pasture that was maintained at a leafy, green vegetative stage, and offered ad libitum; they received no supplementation at pasture. Body weight of P lambs when turning out to pasture and at slaughter averaged 9.2 (standard deviation (s.d.) 2.21) kg and 33.2 (s.d. 2.89) kg, respectively. S lambs were fed indoors on an ad libitum diet of commercial concentrate and hay until slaughter at a mean body weight of 33.7 (s.d. 3.62) kg. The reflectance spectrum of perirenal and subcutaneous caudal fat was measured at slaughter and at 24 h post mortem. Plasma carotenoid concentration was measured at slaughter. In method 1, the fat reflectance spectrum data were used at wavelengths between 450 and 510 nm to calculate an index quantifying light absorption by carotenoid pigments. In method 2, a multivariate analysis was performed over the full set of fat reflectance data at wavelengths between 400 and 700 nm. Method 2 yielded a higher proportion of correctly classified lambs compared with method 1 (P < 0.05 to 0.001), except for measurements made at 24 h post mortem on perirenal fat for S lambs. The proportion of lambs correctly classified using method 2 was 87.4% and 92.9% for measurements made on perirenal and caudal fat at slaughter, and 93.9% and 91.0% for measurements made on perirenal and caudal fat 24 h post mortem. Plasma carotenoid concentrations were higher in P lambs than in S lambs (P < 0.001), which led to correct classification of 90.7% of the lambs.     

Utilizing spectral vegetation indices for yield assessment of tomato genotypes grown in arid conditions

Tomato is among important vegetable crops cultivated in different climates; however, heat stress can greatly affect fruit quality and overall yield. Crop reflectance measurements based on ground reflectance sensor data are reliable indicators of crop tolerance to abiotic stresses. Here, we report on using non-destructive spectral vegetation indices to monitor yield traits of 10 tomato genotypes transplanted on three different dates (Aug. 2, Sept. 3 and Oct. 1) during 2019 growing season in the Riyadh region. The ten genotypes comprised six commercial cultivars–(Pearson Improved, Strain B, Valentine, Marmande VF, Super Strain B, and Pearson early) ––and four local Saudi cultivars (Al-Ahsa, Al-Qatif, Hail and Najran). Spectral reflectance data were utilized using a FieldSpec 3 spectroradiometer in the range of 350–2500 nm to calculate nine vegetation indices (VIs): Normalized Water Band Index (NWBI), Difference Water Index (NDWI), Photochemical Reflectance Index (PRI), Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Red Edge Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index (SAVI), Red Edge Normalized Difference Vegetation Index (RENDVI), Renormalized Difference Vegetation Index (RDVI), and Normalized Difference Nitrogen Index (NDNI). VIs and yield parameters (total fruit yield, harvest index) revealed that second transplanting date was optimal for all the genotypes. Valentine showed the best growth performance followed by Najran, Hail, Super Strain B and finally Pearson early. For all the three transplanting dates, Valentine recorded the highest total fruit yield. Additionally, some genotypes had no significant differences in the VIs values or the total fruit yield between the second and third transplanting dates. This study indicated that yield parameters could be linked to rapid, non-destructive hyperspectral reflectance data to predict tomato production under heat stress.     

Factors associated with black bear density and implications for management

Wildlife density estimates are important to accurately formulate population management objectives and understand the relationship between habitat characteristics and a species’ abundance. Despite advances in density and abundance estimation methods, management of common game species continues to be challenged by a lack of reliable population estimates. In Washington, USA, statewide American black bear (Ursus americanus) abundance estimates are predicated on density estimates derived from research in the 1970s and are hypothesized to be a function of precipitation and vegetation, with higher densities in western Washington. To evaluate current black bear density and landscape relationships in Washington, we conducted a 4-year capture-recapture study in 2 areas of the North Cascade Mountains using 2 detection methods, non-invasive DNA collection and physical capture and deployment of global positioning system (GPS) collars. We integrated GPS telemetry from collared bears with spatial capture-recapture (SCR) data and created a SCR-resource selection model to estimate density as a function of spatial covariates and test the hypothesis that density is higher in areas with greater vegetative food resources. We captured and collared 118 bears 132 times and collected 7,863 hair samples at hair traps where we identified 537 bears from 1,237 detections via DNA. The most-supported model in the western North Cascades depicted a negative relationship between black bear density and an index of human development. We estimated bear density at 20.1 bears/100 km², but density varied from 13.5/100 km² to 27.8 bears/100 km² depending on degree of human development. The model best supported by the data in the eastern North Cascades estimated an average density of 19.2 bears/100 km², which was positively correlated with primary productivity, with resulting density estimates ranging from 7.1/100 km² to 33.6 bears/100 km². The hypothesis that greater precipitation and associated vegetative production in western Washington supports greater bear density compared to eastern Washington was not supported by our data. In western Washington, empirically derived average density estimates (including cubs) were nearly 50% lower than managers expected prior to our research. In eastern Washington average black bear density was predominantly as expected, but localized areas of high primary productivity supported greater than anticipated bear densities. Our findings underscore the importance that black bear density is not likely uniform and management risk may be increased if an average density is applied at too large a scale. Disparities between expected and empirically derived bear density illustrate the need for more rigorous monitoring to understand processes that affect population numbers throughout the jurisdiction, and suggest that management plans may need to be reevaluated to determine if current harvest strategies are achieving population objectives. © 2019 The Wildlife Society.     

Testing and interpreting the shared space‐environment fraction in variation partitioning analyses of ecological data

Variation partitioning analyses combined with spatial predictors (Moran's eigenvector maps, MEM) are commonly used in ecology to test the fractions of species abundance variation purely explained by environment and space. However, while these pure fractions can be tested using a classical residuals permutation procedure, no specific method has been developed to test the shared space‐environment fraction (SSEF). Yet, the SSEF is expected to encompass a major driver of community assembly, that is, an induced spatial dependence effect (ISD; i.e. the reflection of a spatially structured habitat filter on a species distribution). A reliable test of this fraction is therefore crucial to properly test the presence of an ISD on ecological data. To bridge the gap, we propose to test the SSEF through spatially‐constrained null models: torus‐translations, and Moran spectral randomisations. We investigated the type I error rate and statistical power of our method based on two real environmental datasets and simulations of tree distributions. Ten types of tree distribution displaying contrasted aggregation properties were simulated, and their abundances were sampled in 153 regularly‐distributed 20 × 20 m quadrats. The SSEF was tested for 1000 simulated tree distributions either unrelated to the environment, or filtered by environmental variables displaying contrasting spatial structures. The method proposed provided a correct type I error rate (< 0.05). The statistical power was high (> 0.9) when abundances were filtered by an environmental variable structured at broad scale. However, the spatial resolution allowed by the sampling design limited the power of the method when using a fine‐scale filtering variable. This highlighted that an ISD can be properly detected providing that the spatial pattern of the filtering process is correctly captured by the sampling design of the study. An R function to apply the SSEF testing method is provided and detailed in a tutorial.     

Spectral Sensitivity of the Hawaiian Saddle Wrasse, Thalassoma duperrey, and Implications for Visually Mediated Behaviour on Coral Reefs

Although tropical coral reefs are one of the most spectrally complex habitats, there is relatively little known about colour vision of reef fish. In this study, we measured the spectral sensitivity of an endemic Hawaiian coral reef fish, Thalassoma duperrey (family Labridae), and assessed the possible role of visual sensitivity in mediating intraspecific communication. Electrophysiological recordings of compound action potentials from retinal ganglion cells were used to generate spectral sensitivity curves for specific wavelengths (380–620nm). We found at least 2 sensitivity peaks for the on response (_max=460, 550nm). The off response lacked a short wavelength mechanism but a medium wavelength mechanism (_max=545nm) and a longwave mechanism (_max=570nm) were found. To quantify the visual stimulus provided by a conspecific individual, spectral reflectance from the colour pattern of T. duperrey was measured with a spectroradiometer. Luminance and spectral contrast were computed between colour patches of the pattern and between the patches and natural backgrounds (i.e., water and coral). Reflectance from the blue head and contrast from the blue, green and red patches matched the sensitivity maxima of T. duperrey, although this depended on the type of background. Our results indicate that T. duperrey should be able to visually detect the colour pattern of a conspecific fish and that T. duperrey's visual system is designed to enhance target detection in the coral reef habitat with matched and offset cone mechanisms.