In a review of landscape-scale empirical studies, Fahrig (2017a) found that ecological responses to habitat fragmentation per se (fragmentation independent of habitat amount) were usually non-significant (> 70% of responses) and that 76% of significant relationships were positive, with species abundance, occurrence, richness, and other response variables increasing with habitat fragmentation per se. Fahrig concluded that to date there is no empirical evidence supporting the widespread assumption that a group of small habitat patches generally has lower ecological value than large patches of the same total area. Fletcher et al.(2018) dispute this conclusion, arguing that the literature to date indicates generally negative ecological effects of habitat fragmentation per se. They base their argument largely on extrapolation from patchscale patterns and mechanisms (effects of patch size and isolation, and edge effects) to landscape-scale effects of habitat fragmentation. We argue that such extrapolation is unreliable because: (1) it ignores other mechanisms, especially those acting at landscape scales (e.g., increased habitat diversity, spreading of risk, landscape complementation) that can counteract effects of the documented patch-scale mechanisms; and (2) extrapolation of a small-scale mechanism to a large-scale pattern is not evidence of that pattern but, rather a prediction that must be tested at the larger scale. Such tests were the subject of Fahrig's review. We find no support for Fletcher et al.'s claim that biases in Fahrig's review would alter its conclusions. We encourage further landscape-scale empirical studies of effects of habitat fragmentation per se, and research aimed at uncovering the mechanisms that underlie positive fragmentation effects.

No abstract

Summary1. Landscape patterns influence a range of ecological processes at multiple spatial scales. Landscape pattern metrics are often used to study the patterns that result from the linear and nonlinear interactions between spatial aggregation and abundance of habitat. 2. However, many class-level pattern metrics are highly correlated with habitat abundance, making their use as a measure of habitat fragmentation problematic. 3. We argue that a class-level pattern metric should be (1) able to differentiate landscapes across a range of spatial aggregations, and (2) independent of habitat abundance, if it is to be used to distinguish between effects of habitat amount and fragmentation. 4. Based on these criteria and using both simulated and actual landscapes, we evaluated 64 class-level pattern metrics. These metrics were reclassified into four groups based on their correlation with aggregation and abundance. 5. Among all these metrics, nine were considered robust for fragmentation measurements, which cover most of the characteristics that define pattern, including core area, shape, proximity / isolation, contrast, and contagion / interspersion. 6. Optimal metrics for individual studies will depend on both biological rationales and statistically robust metrics that are appropriate for achieving each study objectives.

Summary 1.Accurate resource selection functions (RSFs) are important for managing animal populations. Developing RSFs using data from GPS telemetry can be problematic due to serial autocorrelation, but modern analytical techniques can help to compensate for this correlation. 2. We used telemetry locations from 18 woodland caribou Rangifer tarandus caribou in Saskatchewan, Canada, to compare marginal (population-specific) generalized estimating equations (GEEs), and conditional (subject-specific) generalized linear mixed-effects models (GLMMs), for developing resource selection functions at two spatial scales. We evaluated the use of empirical standard errors, which are robust to misspecification of the correlation structure. We compared these approaches with destructive sampling. 3. Statistical significance was strongly influenced by the use of empirical vs. model-based standard errors, and marginal (GEE) and conditional (GLMM) results differed. Destructive sampling reduced apparent habitat selection. k -fold cross-validation results differed for GEE and GLMM, as it must be applied differently for each model. 4. Synthesis and applications . Due to their different interpretations, marginal models (e.g. generalized estimating equations, GEEs) may be better for landscape and population management, while conditional models (e.g. generalized linear mixed-effects models, GLMMs) may be better for management of endangered species and individuals. Destructive sampling may lead to inaccurate resource selection functions (RSFs), but GEEs and GLMMs can be used for developing RSFs when used with empirical standard errors.

The global lockdown to mitigate COVID-19 pandemic health risks has altered human interactions with nature. Here, we report immediate impacts of changes in human activities on wildlife and environmental threats during the early lockdown months of 2020, based on 877 qualitative reports and 332 quantitative assessments from different studies. Hundreds of reports of unusual species observations from around the world suggest that animals quickly responded to the reductions in human presence. However, negative effects of lockdown on conservation also emerged, as confinement resulted in some park officials being unable to perform conservation, restoration and enforcement tasks, resulting in local increases in illegal activities such as hunting. Overall, there is a complex mixture of positive and negative effects of the pandemic lockdown on nature, all of which have the potential to lead to cascading responses which in turn impact wildlife and nature conservation. While the net effect of the lockdown will need to be assessed over years as data becomes available and persistent effects emerge, immediate responses were detected across the world. Thus, initial qualitative and quantitative data arising from this serendipitous global quasi-experimental perturbation highlights the dual role that humans play in threatening and protecting species and ecosystems. Pathways to favorably tilt this delicate balance include reducing impacts and increasing conservation effectiveness.

Summary 1.Ecological data sets often use clustered measurements or use repeated sampling in a longitudinal design. Choosing the correct covariance structure is an important step in the analysis of such data, as the covariance describes the degree of similarity among the repeated observations. 2. Three methods for choosing the covariance are: the Akaike information criterion (AIC), the quasi-information criterion (QIC) and the deviance information criterion (DIC). We compared the methods using a simulation study and using a data set that explored effects of forest fragmentation on avian species richness over 15 years. 3. The overall success was 80AE6% for the AIC, 29AE4% for the QIC and 81AE6% for the DIC. For the forest fragmentation study the AIC and DIC selected the unstructured covariance, whereas the QIC selected the simpler autoregressive covariance. Graphical diagnostics suggested that the unstructured covariance was probably correct. 4. We recommend using DIC for selecting the correct covariance structure.

To evaluate water-level manipulations as a management tool in boreal wetlands, marsh bird and waterfowl habitat use were studied in the Saskatchewan River Delta, Manitoba, Canada, during 2008 and 2009. Call-response and aerial surveys were used to estimate densities of marsh birds and waterfowl, respectively, within six wetland basins undergoing two different water-level treatments. Generalized linear models were used to determine relationships between presence and densities of birds to water depth, vegetation characteristics, and relative forage fish and invertebrate abundances at two spatial scales. American Bittern (Botaurus lentiginosus) and Piedbilled Grebe (Podilymbus podiceps) densities were positively influenced by water depth and relative fish abundance. American Coots (Fulica americana) and diver waterfowl (Aythya, Bucephala) also responded positively to increased water depth, whereas dabbler waterfowl (Anas, Aix) were negatively influenced by increasing water depth. Densities of Sora (Porzana carolina) and Virginia Rail (Rallus limicola) were positively correlated with the relative abundances of invertebrates, but negatively correlated with relative fish abundance. Due to the high avian biodiversity in the region, managers should focus on providing a variety of wetland habitats. Using a combination of partial waterlevel drawdowns and high water, habitat for numerous avian species can be created simultaneously within wetland complexes.

The COVID-19 pandemic resulted in extraordinary declines in human mobility, which, in turn, may affect wildlife. Using records of more than 4.3 million birds observed by volunteers from March to May 2017-2020 across Canada and the United States, we found that counts of 66 (80%) of 82 focal bird species changed in pandemic-altered areas, usually increasing in comparison to prepandemic abundances in urban habitat, near major roads and airports, and in counties where lockdowns were more pronounced or occurred at the same time as peak bird migration. Our results indicate that human activity affects many of North America's birds and suggest that we could make urban spaces more attractive to birds by reducing traffic and mitigating the disturbance from human transportation after we emerge from the pandemic.