Tropical forest hotspots have a high diversity of species but have lost > 70% of their original vegetation cover and are characterized by a multitude of small and isolated fragments. Paradoxically, conservation actions in these areas are still mainly focused on protection of large tracts of forests, a strategy now infeasible because of the small area of forest remnants. Here we use the Vulnerable black-handed titi monkey Callicebus melanochir as a model to study the effects of habitat loss, fragmentation and degradation on arboreal mammals and to provide insights for science-driven conservation in fragmented landscapes in tropical forest hotspots. We surveyed 38 Atlantic Forest fragments in Bahia State, Brazil and assessed the effects of patch area, quality and visibility, and landscape connectivity on the occurrence of our model species. Patch area was the single best model explaining species occurrence. Nonetheless, patch quality and visibility, and landscape connectivity, positively affect occurrence. In addition to patch area, patch quality, patch visibility and landscape connectivity are useful for predicting the occurrence of arboreal mammals in the fragments of tropical forest hotspots. We encourage the assessment of habitat quality (based on remotely sensed vegetation indices) and habitat visibility (based on digital elevation models) to improve discoverability of arboreal mammal populations and selection of fragments for conservation purposes across fragmented landscapes of tropical forest hotspots. Large remnants of tropical forest hotspots are scarce and therefore we require baseline data to support conservation actions and management in small forest fragments.

The eastern North Atlantic (ENA) has many highly productive areas where several species of cetaceans have been recorded, with the common dolphin (Delphinus delphis) being one of the most frequently sighted species. However, its spatial and temporal distribution in high seas is poorly known. The study presents the results from 5 years of cetacean monitoring in the ENA (2012–2016) aboard cargo ships that follow the routes from Continental Portugal to the Macaronesian archipelagos and north-west Africa. Common dolphin was the most frequently sighted cetacean with 192 occurrences registered on effort and an overall encounter rate of 0.36 sightings/100 nmi. The species was distributed in coastal and offshore waters, but absent from the Canaries and Cape Verde islands. Statistical ‘habitat’ models were developed to describe and explain the occurrence of sightings of the species: variables affecting detection of dolphins had a small impact and there were clear spatiotemporal distribution patterns, influenced to some degree by environmental variables. Predicted probability of occurrence was highest in coastal waters of continental Portugal and around the Azores. The models, combined with maps of distribution, were useful to identify important areas for the species, which could be the focus of future conservation efforts. Common dolphin presence was related to depth, distance to coast and seamounts, seabed slope, chlorophyll concentration, sea-surface temperature and sea level anomalies; the possible ecological significance of these relationships is explored.

Artificial Neural Network models (ANNs) were used to predict habitat suitability for 12 macroinvertebrate taxa, usingenvironmental input variables. This modelling technique was applied to a dataset of 102 measurement series collected in 31sampling sites in the Greek river Axios. The database consisted of seven physical-chemical and seven structural variables, as wellas abundances of 90 macroinvertebrate taxa. A seasonal variable was included to allow the description of potential temporalchanges in the macroinvertebrate communities. The induced models performed well for predicting habitat suitability of themacroinvertebrate taxa. Senso-nets and sensitivity analyses revealed that dissolved oxygen concentration and the substratecomposition always played a crucial role in predicting habitat suitability of the macroinvertebrates. Although ANNs are oftenreferred to as black box prediction techniques, it was demonstrated that ANNs combined with sensitivity analyses can provideinsight in the relationship between river conditions and the occurrence of macroinvertebrates, and thus deliver new ecologicalknowledge. Consequently, these models can be useful in decision-making for river restoration and conservation management.

Integrated ecological models are of great potential as predictive tools in decision support of river management. Such models need to be transparent and consistent with the existing expert knowledge, and give the river manager adequate information regarding their inherent uncertainty. One way to fulfil these needs is through the use of Bayesian Belief Networks (BBNs). Such networks represent cause-and-effect assumptions between system variables in a graphical structure. To establish the potential of Bayesian Belief Networks in river management, a small-scale study was performed with the aim of assessing the success of prediction of macroinvertebrate taxa in rivers by means of a selected number of environmental variables. Gammaridae and Asellidae were chosen because of their high relative abundances in small and large brooks in contrast to other macroinvertebrate taxa. Based on one-layered BBN networks, the predictive capacity of the models was assessed by means of the number of Correctly Classified Instances (CCI) and Cohen’s Kappa (K). The performance of these models was moderate to good for presence/absence classifications but showed a low to moderate performance when predicting abundance classes. When extending the former BBN network to a two-layered one, enhancing the number of links and variables, no obvious improvement in model performance was detected. The results indicate that thoughtful input variable selection as well as sensitivity analysis will improve the models for practical use in river restoration management.

Macaroni penguins (Eudyptes chrysolophus) and rockhopper penguins (E. chrysocome) account for a substantial proportion of the avian biomass at the subantarctic Prince Edward Islands (47°S, 38°E) in summer, when both species are breeding at the islands. Information on breeding population sizes, breeding and moulting activities, and diets were combined with measurements of the penguins' energy expenditures to construct a bioenergetic model of their energy requirements and food consumption at the islands. Total energy requirements of adults and chicks amounted to 460 × 109 kJ and 162 × 109 kJ for macaroni and rockhopper penguins, respectively. Food consumption was estimated to amount to a total of 166 000 tonnes during the seven-month breeding and moulting cycle, of which macaroni penguins consumed 75%. Most, if not all, of this food is taken within a 200-km radius of the islands. Available information suggests that potential primary production in the immediate vicinity of the islands is sufficient to support the macaroni and rockhopper penguin populations. However, the importance in the diets of the penguins of several prey species more typical of Antarctic and subtropical regions suggests that the penguins rely to a large extent on the importation of prey populations from other areas.