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Savanna ecosystems are characterized by the co‐occurrence of trees and grasses. In this paper, we argue that the balance between trees and grasses is, to a large extent, determined by the indirect interactive effects of herbivory and fire. These effects are based on the positive feedback between fuel load (grass biomass) and fire intensity. An increase in the level of grazing leads to reduced fuel load, which makes fire less intense and, thus, less damaging to trees and, consequently, results in an increase in woody vegetation. The system then switches from a state with trees and grasses to a state with solely trees. Similarly, browsers may enhance the effect of fire on trees because they reduce woody biomass, thus indirectly stimulating grass growth. This consequent increase in fuel load results in more intense fire and increased decline of biomass. The system then switches from a state with solely trees to a state with trees and grasses. We maintain that the interaction between fire and herbivory provides a mechanistic explanation for observed discontinuous changes in woody and grass biomass. This is an alternative for the soil degradation mechanism, in which there is a positive feedback between the amount of grass biomass and the amount of water that infiltrates into the soil. The soil degradation mechanism predicts no discontinuous changes, such as bush encroachment, on sandy soils. Such changes, however, are frequently observed. Therefore, the interactive effects of fire and herbivory provide a more plausible explanation for the occurrence of discontinuous changes in savanna ecosystems.

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Hosts species for multi-host pathogens show considerable variation in the species' reservoir competence, which is usually used to measure species' potential to maintain and transmit these pathogens. Although accumulating research has proposed a trade-off between life-history strategies and immune defences, only a few studies extended this to host species' reservoir competence. Using a phylogenetic comparative approach, we studied the relationships between some species' life-history traits and reservoir competence in three emerging infectious vector-borne disease systems, namely Lyme disease, West Nile Encephalitis (WNE) and Eastern Equine Encephalitis (EEE). The results showed that interspecific variation in reservoir competence could be partly explained by the species' life histories. Species with larger body mass (for hosts of Lyme disease and WNE) or smaller clutch size (for hosts of EEE) had a higher reservoir competence. Given that both larger body mass and smaller clutch size were linked to higher extinction risk of local populations, our study suggests that with decreasing biodiversity, species with a higher reservoir competence are more likely to remain in the community, and thereby increase the risk of transmitting these pathogens, which might be a possible mechanism underlying the dilution effect.

Issues of residual spatial autocorrelation (RSA) and spatial scale are critical to the study of species-environment relationships, because RSA invalidates many statistical procedures, while the scale of analysis affects the quantification of these relationships. Although these issues independently are widely covered in the literature, only sparse attention is given to their integration. This paper focuses on the interplay between RSA and the spatial scaling of species-environment relationships. Using a hypothetical species in an artificial landscape, we show that a mismatch between the scale of analysis and the scale of a species' response to its environment leads to a decrease in the portion of variation explained by environmental predictors. Moreover, it results in RSA and biased regression coefficients. This bias stems from error-predictor dependencies due to the scale mismatch, the magnitude of which depends on the interaction between the scale of landscape heterogeneity and the scale of a species' response to this heterogeneity. We show that explicitly considering scale effects on RSA can reveal the characteristic scale of a species' response to its environment. This is important, because the estimation of species-environment relationships using spatial regression methods proves to be erroneous in case of a scale mismatch, leading to spurious conclusions when scaling issues are not explicitly considered. The findings presented here highlight the importance of examining the appropriateness of the spatial scales used in analyses, since scale mismatches affect the rigor of statistical analyses and thereby the ability to understand the processes underlying spatial patterning in ecological phenomena.

Summary 1. Plant organ biomass partitioning has been hypothesized to be driven by resources, such that species from drier environments allocate more biomass to roots than species from wetter environments to access water at greater soil depths. In savanna systems, fire may select for greater allocation to root biomass, especially in humid environments where fire is more frequent. Therefore, species from drier environments may have been under selection pressure to reach deeper soil water more effectively than species from humid environments, through faster root extension, more efficient depth penetration, and faster plant growth rates to respond rapidly to variable rainfall events. 2. We compared biomass partitioning, root morphology traits [root extension rate, RER; specific taproot length (STRL)] and relative growth rate (RGR) of seedlings of 51 savanna tree species, sampled from three continents (Africa, Australia and South America) in a greenhouse experiment. We used phylogenetically corrected and uncorrected analyses to compare the traits of the groups. We conducted a permanova on the combined traits to establish whether species could be distinguished on the basis of their combined traits. 3. On average, species from humid environments allocated more biomass to roots and less to stems than species from semi‐arid environments, consistent with the expectation that fire pressure selects for greater allocation to roots in humid environments. However, some species from humid environments had fast growth rates instead of high allocation to roots. Both RER and STRL were greater among species of semi‐arid environments than among species of humid environments, and also differed between continents. Differences between strategies under each climate type appear to be associated with leaf habit. 4. Synthesis. Plant biomass partitioning has been selected by defoliation pressure and the effects of this selection pressure can supersede any selection in response to local water constraints. Root morphological adaptations, but not plant growth rate, of tree seedlings, have been selected in response to water deficits.

Inexpensive and accessible sensors are accelerating data acquisition in animal ecology. These technologies hold great potential for large-scale ecological understanding, but are limited by current processing approaches which inefficiently distill data into relevant information. We argue that animal ecologists can capitalize on large datasets generated by modern sensors by combining machine learning approaches with domain knowledge. Incorporating machine learning into ecological workflows could improve inputs for ecological models and lead to integrated hybrid modeling tools. This approach will require close interdisciplinary collaboration to ensure the quality of novel approaches and train a new generation of data scientists in ecology and conservation.