Experience and social factors influence movement and habitat selection in scimitar-horned oryx (Oryx dammah) reintroduced into Chad

Reintroduced animals-especially those raised in captivity-are faced with the unique challenge of navigating a wholly unfamiliar environment, and often make erratic or extensive movements after release. Naïveté to the reintroduction landscape can be costly, e.g., through increased energy expenditure, greater exposure to predation, and reduced opportunities to forage. Integration with an extant population may provide opportunities for social information transfer. However, in the absence of interactions with residents, it is unclear how individual and social learning may affect an animal's ability to track resources in an unfamiliar landscape. We use integrated step selection functions (iSSFs) to address these knowledge gaps, by evaluating the extent to which environmental factors, individual experience (time since release), and social information-sharing (group size) influence movement decisions by scimitar-horned oryx (Oryx dammah) reintroduced into their native range for the first time in ca. 30 years. We found that both experience and social factors influenced the habitat selection and movement behavior of reintroduced oryx. Of four candidate iSSFs, the model that included environmental, experience, and group size variables performed best in both dry and wet periods. Statistically significant interaction terms between environmental variables and experience were generally larger than similar terms for group size, indicating that experience may affect habitat selection by reintroduced oryx more strongly than social factors. These findings may inform the management of recovering wildlife populations, update widely-held expectations about how released ungulates acclimate to novel landscapes, and demonstrate the utility of long-term monitoring of reintroduced populations.

Hierarchical multi-grain models improve descriptions of species' environmental associations, distribution, and abundance

The characterization of species' environmental niches and spatial distribution predictions based on them are now central to much of ecology and conservation, but implicitly requires decisions about the appropriate spatial scale (i.e., grain) of analysis. Ecological theory and empirical evidence suggest that range-resident species respond to their environment at two characteristic, hierarchical spatial grains: (1) response grain, the (relatively fine) grain at which an individual uses environmental resources, and (2) occupancy grain, the (relatively coarse) grain equivalent to a typical home range. We use a multi-grain (MG) occupancy model, aided by fine-grain remotely sensed imagery, to simultaneously estimate species-environment associations at both grains, conduct grain optimization to measure response grain, and apply this analysis framework to an example species: a medium-sized bird (Tockus deckeni) in a heterogeneous East African landscape. Based on home range analysis of movement data, we calculate an occupancy grain of 1 km for T. deckeni. Using a grain optimization procedure across 32 grains from 10 to 500 m, we identify 60 m as the most strongly supported response grain for a suite of environmental variables, slightly coarser than opportunistic behavioral observations would have suggested. Validation confirms that the accuracy of the optimized MG occupancy model substantially exceeds that of equivalent single-grain (SG) occupancy models. We further use a simulation approach to assess the potential impacts of accounting for the multi-scale structure of species' environmental requirements on estimates of population size. We find that the more strongly supported MG approach consistently predicts a minimum population size in the study landscape that is much lower than that provided by the SG model. This suggests that SG approaches commonly used in conservation applications could lead to overly optimistic abundance and population estimates, and that the MG approach may be more appropriate for supporting species conservation goals. More generally, we conclude that multi-grain approaches of the sort presented, and increasingly enabled by growing high-resolution remotely sensed data, hold great promise for offering a more mechanistic framework for assessing the appropriate grain(s) for population monitoring and management and enable more reliable estimates of abundances and species' distributions.

Effects of body size on estimation of mammalian area requirements

Accurately quantifying species' area requirements is a prerequisite for effective area-based conservation. This typically involves collecting tracking data on species of interest and then conducting home-range analyses. Problematically, autocorrelation in tracking data can result in space needs being severely underestimated. Based on the previous work, we hypothesized the magnitude of underestimation varies with body mass, a relationship that could have serious conservation implications. To evaluate this hypothesis for terrestrial mammals, we estimated home-range areas with global positioning system (GPS) locations from 757 individuals across 61 globally distributed mammalian species with body masses ranging from 0.4 to 4000 kg. We then applied block cross-validation to quantify bias in empirical home-range estimates. Area requirements of mammals <10 kg were underestimated by a mean approximately15%, and species weighing approximately100 kg were underestimated by approximately50% on average. Thus, we found area estimation was subject to autocorrelation-induced bias that was worse for large species. Combined with the fact that extinction risk increases as body mass increases, the allometric scaling of bias we observed suggests the most threatened species are also likely to be those with the least accurate home-range estimates. As a correction, we tested whether data thinning or autocorrelation-informed home-range estimation minimized the scaling effect of autocorrelation on area estimates. Data thinning required an approximately93% data loss to achieve statistical independence with 95% confidence and was, therefore, not a viable solution. In contrast, autocorrelation-informed home-range estimation resulted in consistently accurate estimates irrespective of mass. When relating body mass to home range size, we detected that correcting for autocorrelation resulted in a scaling exponent significantly >1, meaning the scaling of the relationship changed substantially at the upper end of the mass spectrum.

Biosynthesis of algal pheromones. A model study with the composite Senecio isatideus

Several cyclic and alicyclic C11 hydrocarbons have been shown to act as gamete releasing and/or attracting pheromones during sexual reproduction of brown algae (Phaeophyceae). The same compounds are also found in the essential oils of various plants, of which the occurrence of the cycloheptadiene-pheromone ectocarpene in Senecio isatideus (Compositae) is noteworthy. Administration of [3H]dodeca-3,6,9-trienoic acid to cuttings of this plant leads to incorporation of radioactivity into ectocarpene. Double-bond-deuterated nona-3,6-dienoic acid is converted to fucoserratene, the pheromone of several Fucales, which is certainly not present among the hydrocarbons of Senecio. This proves that the pool of medium-chain, multiply unsaturated fatty acids includes precursors of all types of highly unsaturated hydrocarbons. Appropriately labelled (deuterium markers) fatty acid homologues were synthesized and applied to Senecio plantlets to unravel the mechanistic aspects. The results strongly suggest radical initiation of the pheromone biosynthesis by abstraction of a single hydrogen from a 1,4-pentadienyl segment of the fatty acid followed by oxidation to the corresponding cation. This causes fragmentation of the reactive intermediate into an olefine and carbon dioxide by neighbouring-group participation of the flanking double bonds. A tentative biosynthetic scheme is deduced from the experimental results which also sets the stereochemistry of the algal pheromones into a uniform mechanistic concept.