Terrestrial mammals are experiencing a massive collapse in their population sizes and geographical ranges around the world, but many of the drivers, patterns and consequences of this decline remain poorly understood. Here we provide an analysis showing that bushmeat hunting for mostly food and medicinal products is driving a global crisis whereby 301 terrestrial mammal species are threatened with extinction. Nearly all of these threatened species occur in developing countries where major coexisting threats include deforestation, agricultural expansion, human encroachment and competition with livestock. The unrelenting decline of mammals suggests many vital ecological and socio-economic services that these species provide will be lost, potentially changing ecosystems irrevocably. We discuss options and current obstacles to achieving effective conservation, alongside consequences of failure to stem such anthropogenic mammalian extirpation. We propose a multi-pronged conservation strategy to help save threatened mammals from immediate extinction and avoid a collapse of food security for hundreds of millions of people.

Global biodiversity loss is a critical environmental crisis, yet the lack of spatial data on biodiversity threats has hindered conservation strategies. Theory predicts that abrupt biodiversity declines are most likely to occur when habitat availability is reduced to very low levels in the landscape (10-30%). Alternatively, recent evidence indicates that biodiversity is best conserved by minimizing human intrusion into intact and relatively unfragmented landscapes. Here we use recently available forest loss data to test deforestation effects on International Union for Conservation of Nature Red List categories of extinction risk for 19,432 vertebrate species worldwide. As expected, deforestation substantially increased the odds of a species being listed as threatened, undergoing recent upgrading to a higher threat category and exhibiting declining populations. More importantly, we show that these risks were disproportionately high in relatively intact landscapes; even minimal deforestation has had severe consequences for vertebrate biodiversity. We found little support for the alternative hypothesis that forest loss is most detrimental in already fragmented landscapes. Spatial analysis revealed high-risk hot spots in Borneo, the central Amazon and the Congo Basin. In these regions, our model predicts that 121-219 species will become threatened under current rates of forest loss over the next 30 years. Given that only 17.9% of these high-risk areas are formally protected and only 8.9% have strict protection, new large-scale conservation efforts to protect intact forests are necessary to slow deforestation rates and to avert a new wave of global extinctions.

Summary1. Recent advances in occupancy estimation that adjust for imperfect detection have provided substantial improvements over traditional approaches and are receiving considerable use in applied ecology. To estimate and adjust for detectability, occupancy modelling requires multiple surveys at a site and requires the assumption of 'closure' between surveys, i.e. no changes in occupancy between surveys. Violations of this assumption could bias parameter estimates; however, little work has assessed model sensitivity to violations of this assumption or how commonly such violations occur in nature. 2. We apply a modelling procedure that can test for closure to two avian point-count data sets in Montana and New Hampshire, USA, that exemplify time-scales at which closure is often assumed. These data sets illustrate different sampling designs that allow testing for closure but are currently rarely employed in field investigations. Using a simulation study, we then evaluate the sensitivity of parameter estimates to changes in site occupancy and evaluate a power analysis developed for sampling designs that is aimed at limiting the likelihood of closure. 3. Application of our approach to point-count data indicates that habitats may frequently be open to changes in site occupancy at time-scales typical of many occupancy investigations, with 71% and 100% of species investigated in Montana and New Hampshire respectively, showing violation of closure across time periods of 3 weeks and 8 days respectively. 4. Simulations suggest that models assuming closure are sensitive to changes in occupancy. Power analyses further suggest that the modelling procedure we apply can effectively test for closure. 5. Synthesis and applications. Our demonstration that sites may be open to changes in site occupancy over time-scales typical of many occupancy investigations, combined with the sensitivity of models to violations of the closure assumption, highlights the importance of properly addressing the closure assumption in both sampling designs and analysis. Furthermore, inappropriately applying closed models could have negative consequences when monitoring rare or declining species for conservation and management decisions, because violations of closure typically lead to overestimates of the probability of occurrence.

Spatial models of under-canopy temperature show that old-growth forests are cooler in spring months than mature forest plantations.

To maximize fitness, organisms must assess and select suitable habitat. Early research studying birds suggested that organisms consider primarily vegetation structural cues in their habitat choices. We show that experimental exposure to singing in the post-breeding period provides a social cue that is used for habitat selection the following year by a migrant songbird, the black-throated blue warbler (Dendroica caerulescens). Our experimental social cues coerced individuals to adopt territories in areas of very poor habitat quality where individuals typically do not occur. This indicates that social information can override typical associations with vegetation structure. We demonstrate that a strong settlement response was elicited because post-breeding song at a site is highly correlated with reproductive success. These results constitute a previously undocumented, but highly parsimonious mechanism for the inadvertent transfer of reproductive (public) information from successful breeders to dispersers. We hypothesize that post-breeding song is a pervasive and reliable cue for species that communicate vocally, inhabit temporally autocorrelated environments, produce young asynchronously and/or abandon territories after reproductive failure.

Animal-mediated pollination is essential for both ecosystem services and conservation of global biodiversity, but a growing body of work reveals that it is negatively affected by anthropogenic disturbance. Landscape-scale disturbance results in two often inter-related processes: (1) habitat loss, (2) disruptions of habitat configuration (i.e. fragmentation). Understanding the relative effects of such processes is critical in designing effective management strategies to limit pollination and pollinator decline. We reviewed existing published work from 1989 to 2009 and found that only six of 303 studies considering the influence of landscape context on pollination separated the effects of habitat loss from fragmentation. We provide a synthesis of the current landscape, behavioural, and pollination ecology literature in order to present preliminary multiple working hypotheses explaining how these two landscape processes might independently influence pollination dynamics. Landscape disturbance primarily influences three components of pollination interactions: pollinator density, movement, and plant demography. We argue that effects of habitat loss on each of these components are likely to differ substantially from the effects of fragmentation, which is likely to be more complex and may influence each pollination component in contrasting ways. The interdependency between plants and animals inherent to pollination systems also has the possibility to drive cumulative effects of fragmentation, initiating negative feedback loops between animals and the plants they pollinate. Alternatively, due to their asymmetrical structure, pollination networks may be relatively robust to fragmentation. Despite the potential importance of independent effects of habitat fragmentation, its effects on pollination remain largely untested. We postulate that variation across studies in the effects of 'fragmentation' owes much to artifacts of the sampling regimes adopted, particularly (1) incorrectly separating fragmentation from habitat loss, and (2) mis-matches in spatial scale between landscapes studied and the ecological processes of interest. The field of landscape pollination ecology could be greatly advanced through the consideration and quantification of the matrix, landscape functional connectivity, and pollinator movement behaviour in response to these elements. Studies designed to disentangle the independent effects of habitat loss and fragmentation are essential for gaining insight into landscape-mediated pollination declines, implementing effective conservation measures, and optimizing ecosystem services in complex landscapes.

Although field-collected recordings typically contain multiple simultaneously vocalizing birds of different species, acoustic species classification in this setting has received little study so far. This work formulates the problem of classifying the set of species present in an audio recording using the multi-instance multi-label (MIML) framework for machine learning, and proposes a MIML bag generator for audio, i.e., an algorithm which transforms an input audio signal into a bag-of-instances representation suitable for use with MIML classifiers. The proposed representation uses a 2D time-frequency segmentation of the audio signal, which can separate bird sounds that overlap in time. Experiments using audio data containing 13 species collected with unattended omnidirectional microphones in the H. J. Andrews Experimental Forest demonstrate that the proposed methods achieve high accuracy (96.1% true positives/negatives). Automated detection of bird species occurrence using MIML has many potential applications, particularly in long-term monitoring of remote sites, species distribution modeling, and conservation planning.

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