1. Modelling spatio-temporal changes in species abundance and attributing those changes to potential drivers such as climate, is an important but difficult problem. The standard approach for incorporating climatic variables into such models is to include each weather variable as a single covariate whose effect is expressed through a low-order polynomial or smoother in an additive model. This, however, confounds the spatial and temporal effects of the covariates.
2. We developed a novel approach to distinguish between three types of change in any particular weather covariate. We decomposed the weather covariate into three new covariates by separating out temporal variation in weather (averaging over space), spatial variation in weather (averaging over years) and a space-time anomaly term (residual variation). These three covariates were each fitted separately in the models. We illustrate the approach using generalized additive models applied to count data for a selection of species from the UK’s Breeding Bird Survey, 1994-2013. The weather covariates considered were the mean temperatures during the preceding winter and temperatures and rainfall during the preceding breeding season. We compare models that include these covariates directly with models including decomposed components of the same covariates, considering both linear and smooth relationships.
3. The lowest QAIC values were always associated with a decomposed weather covariate model. Different relationships between counts and the three new covariates provided strong evidence that the effects of changes in covariate values depended on whether changes took place in space, in time, or in the space-time anomaly. These results promote caution in predicting species distribution and abundance in future climate, based on relationships that are largely determined by environmental variation over space.
4. Our methods estimate the effect of temporal changes in weather, whilst accounting for spatial effects of long-term climate, improving inference on overall and/or localised effects of climate change. With increasing availability of large-scale data sets, need is growing for appropriate analytical tools. The proposed decomposition of the weather variables represents an important advance by eliminating the confounding issue often inherent in large-scale data sets.
Aim
Climate changes are anticipated to have pervasive negative effects on biodiversity and are expected to necessitate widespread range shifts or contractions. Such projections are based upon the assumptions that (1) species respond primarily to broad-scale climatic regimes, or (2) that variation in climate at fine spatial scales is less relevant at coarse spatial scales. However, in montane forest landscapes, high degrees of microclimate variability could influence occupancy dynamics and distributions of forest species. Using high-resolution bird survey and under-canopy air temperature data, we tested the hypothesis that the high vagility of most forest bird species combined with the heterogeneous thermal regime of mountain landscapes would enable them to adjust initial settlement decisions to track their thermal niches.
Location
Western Cascade Mountains, Oregon, USA.
Methods
We used dynamic occupancy models to test the degree to which microclimate affects the distribution patterns of forest birds in a heterogeneous mountain environment. In all models we statistically accounted for vegetation structure, vegetation composition and potential biases due to imperfect detection of birds. We generated spatial predictions of forest bird distributions in relation to microclimate and vegetation structure.
Results
Fine-scale temperature metrics were strong predictors of bird distributions; effects of temperature on within-season occupancy dynamics were as large or larger (1–1.7 times) than vegetation effects. Most species (86.7%) exhibited apparent within-season occupancy dynamics. However, species were almost as likely to be warm associated (i.e., apparent settlement at warmer sites and/or vacancy at cooler sites; 53.3% of species) as cool associated (i.e., apparent settlement at cooler sites and/or vacancy at warmer sites; 46.7% of species), suggesting that microclimate preferences are species specific.
Main conclusions
High-resolution temperature data increase the quality of predictions about avian distribution dynamics and should be included in efforts to project future distributions. We hypothesize that microclimate-associated distribution patterns may reflect species' potential for behavioural buffering from climate change in montane forest environments.
Over half of the world's forests are secondary regrowth and support considerable biodiversity. Thinning of these forests is a widespread management practice that can affect forest species, including echolocating bats and their prey.
We compared total activity of 11 bat taxa, foraging activity of six bat guilds and biomass of 11 insect orders across four forest thinning categories in managed remnant eucalypt forests in south-eastern Australia: unthinned regrowth, forest thinned recently (0–4 years) and in the medium term (5–10 years) and reference (mature open forest). Thinning had been carried out at large (?350 ha) spatial scales.
Total bat activity was 60% less and foraging activity was 80% less in unthinned regrowth, compared to reference sites, but activity levels were similar among thinned and reference sites. Insect biomass was greatest in unthinned sites, and while bat activity was related to prey biomass, this relationship was weak in unthinned sites. Together, this suggests that forest structure was more important than prey availability or time since thinning in influencing bat activity patterns.
Synthesizing our findings with the broader literature on bats and thinning, we found support for a clutter threshold of 1100 stems ha?1, above which bat activity was markedly lower, across two continents (the USA and Australia) and four broad vegetation types (eucalypt, conifer, deciduous and mixed forests).
While elsewhere bats with adaptive traits for open habitats generally respond positively to thinning, in our study, species with traits consistent with clutter tolerance (high call frequency and low wing aspect ratio) had lowest activity levels (up to 22 times) in unthinned regrowth compared to all other forest types.
Synthesis and applications. Widespread dense regrowth forest can restrict movement and foraging of bats, even those adapted to clutter. We recommend thinning dense regrowth or plantations to below 1100 stems ha?1 when targeting bat foraging habitat, but effects of thinning on roost habitat and other forest biota require further investigation.
It is well known that bird richness in the Amazon is greater in upland forests and that seasonally flooded forest is particularly species poor. However, the misleading pattern of greater bird richness in seasonally flooded forest has emerged seemingly unnoticed numerous times in richness maps in the literature. We hypothesize that commission errors in digital distribution maps (DDMs) are the cause behind the misleading richness pattern. In the Amazon, commission errors are a consequence of the different methodological treatment given to large-ranged versus small-ranged habitat specialists when mapping distributions. DDMs of 1007 Amazonian birds were examined, and maps that had commission errors were corrected. We generated two richness maps, one from the overlay of original DDMs and another from the overlay of the corrected ones. We identified 291 species whose distribution maps had errors. In the original data, seasonally flooded forests showed higher species richness than upland forest, but this pattern was reverted in the corrected richness map. Commission errors were 35 times more likely in the seasonally flooded forest. We conclude that DDMs accurately portray the distribution of single species in the Amazon. Commission errors in individual maps, however, accumulate when they are overlaid, explaining the misleading pattern for birds in the Amazon. DDMs can continue to be used mapping richness, as long as, at a regional scale: (1) basic map refinements are carried, or (2) only small-range species are used for mapping species richness.
