Ecological similarity explains species abundance distribution of small mammal communities

Assessing abundance-suitability models to prioritize conservation areas for the dwarf caimans in South America

Species-environment relationships have been extensively explored through species distribution models (SDM) and species abundance models (SAM), which have become key components to understand the spatial ecology and population dynamics directed at biodiversity conservation. Nonetheless, within the internal structure of species' ranges, habitat suitability and species abundance do not always show similar patterns, and using information derived from either SDM or SAM could be incomplete and mislead conservation efforts. We gauged support for the abundance-suitability relationship and used the combined information to prioritize the conservation of South American dwarf caimans (Paleosuchus palpebrosus and P. trigonatus). We used 7 environmental predictor sets (surface water, human impact, topography, precipitation, temperature, dynamic habitat indices, soil temperature), 2 regressions methods (Generalized Linear Models-GLM, Generalized Additive Models-GAM), and 4 parametric distributions (Binomial, Poisson, Negative binomial, Gamma) to develop distribution and abundance models. We used the best predictive models to define four categories (low, medium, high, very high) to plan species conservation. The best distribution and abundance models for both Paleosuchus species included a combination of all predictor sets, except for the best abundance model for P. trigonatus which incorporated only temperature, precipitation, surface water, human impact, and topography. We found non-consistent and low explanatory power of environmental suitability to predict abundance which aligns with previous studies relating SDM-SAM. We extracted the most relevant information from each optimal SDM and SAM and created a consensus model (2,790,583 km2) that we categorized as low (39.6%), medium (42.7%), high (14.9%), and very high (2.8%) conservation priorities. We identified 279,338 km2 where conservation must be critically prioritized and only 29% of these areas are under protection. We concluded that optimal models from correlative methods can be used to provide a systematic prioritization scheme to promote conservation and as surrogates to generate insights for quantifying ecological patterns.

Aggregated Distribution as an Explanation for the Paradox of Plankton and Collective Animal Behavior

This work analyzes the evolutionary consequences of different aggregation levels of species distribution with an Evolutionary Cellular Automaton (ECA). We have found that in habitats with the same carrying capacity, aggregated distributions preserve smaller populations than do uniform distributions, i.e., they are less efficient. Nonetheless, we have also found that aggregated distributions, among other factors, can help the evolutionary stability of some biological interactions, such as predator-prey interactions, despite their granting less individual fitness. Besides, the competitive exclusion principle does not usually stand in populations with aggregated distribution. We have applied ECA to study the effects of aggregated distribution in two notorious cases: in the so-called paradox of the plankton and in gregarious animals. In doing so, we intend to ratify long-established ecological knowledge explaining these phenomena from a new perspective. In the first case, due to aggregate distribution, large aggregations of digital organisms mimicking very abundant planktonic species, leave large patches or oceanic areas free for other less competitive organisms, which mimic rare species, to prosper. In this case, we can see how effects, such as ecological drift and the small portion, act simultaneously. In the second case of aggregation, the aggregate distribution of gregarious animals could be explained under specialized predator-prey interactions and interdemic competition. Thus, digital organisms that imitate predators reduce the competitive capacity of their prey, destabilizing their competitiveness against other species. The specialized predator also goes extinct if the prey goes extinct by natural selection. Predators that have an aggregate distribution compensate the prey and thus avoid exclusion. This way there are more predator-free patches in which the prey can prosper. However, by granting greater colonization capacity to its prey, the predator loses competitiveness. Therefore, it is a multilevel selection event in which group adaptation grows to the detriment of the predator as an individual.

Species Abundance Distributions Patterns between Tiankeng Forests and Nearby Non-Tiankeng Forests in Southwest China

Identifying the species abundance distributions (SADs) in Tiankeng forests is crucial for restoring and managing degraded karst ecosystem, whereas previous studies rarely explored the differences and response of vegetation dynamics to environmental variations. The species composition and SADs of the inner and outer fringe areas of Tiankeng forest and nearby non-Tiankeng forest were compared in Southwest China. Six models were adopted to compare SADs of three habitats. Kolmogrov–Smirnov (K–S) test was selected to compare the discrepancy between the simulated and observed SAD patterns. The Akaike Information Criterion (AIC) test was adopted to compare the models, and the best model was indicated by the lowest AIC value. The results showed that (1) the species dispersal from the inside of Tiankeng forests to the nearby non-Tiankeng forests is limited, while species have unlimited dispersal from nearby non-Tiankeng forests to the inside of Tiankeng forests via the fringe of Tiankeng forests. (2) Species abundance, species rarity, richness, and species accumulation rate in the Tiankeng forests were significant in non-Tiankeng forests (p < 0.05), and most species in inner Tiankeng forests originated from nearby non-Tiankeng forests. (3) Based on the criterion of K-S values, all models have passed the K–S test (p > 0.05), which indicated that niche processes and neutral process worked together in the maintenance of community species diversity, the community in study area is a niche-neutral continuum. (4) Considered the lowest AIC value, the neutral (△mean AIC = 1.3) models performed better than the niche (△mean AIC = 22.7) models and statistical (△mean AIC = 2.7) in the Tiankeng forest, while the statistical models performed better than the niche and neutral models in the non-Tiankeng forests. The results suggested that the main driving force of Tiankeng forests is the neutral process. The negative terrain in Tiankeng restricted the species dispersal due to topographic constraints. However, the species dispersal from the nearby non-Tiankeng forests could promote the species succession in the inner Tiankeng. Therefore, we propose that nearby non-Tiankeng forests should be emphasized for protecting the biodiversity of Tiankeng forests.

Long-term changes in a small mammal community in a temperate zone meadow subject to seasonal floods and habitat transformation

We tested small mammal (SM) community response to the influence of seasonal floods and habitat transformation in a temporal zone meadow (west Lithuania). The habitat transitioned from a natural flooded meadow in 2008-2012 to a mowed and pastured area in 2020. SM were trapped in late summer to autumn using lines of 25 traps (145 in total), the number of trapping sessions being one or several per year. We tested the hypotheses that: (1) any reaction to the multiple disturbances is species-specific, that is, the relative abundance of SM species and their proportion in the community differs with respect to the flood magnitude and habitat transformation, and (2) at the guild level, disturbances favor habitat generalists, thereby changing the characteristics of SM community. The average relative density of SM was 20.25 individuals per 100 trap nights (range 7.56-40.67), with 4-year-long cyclical changes of density observed. In separate years, we recorded from 4 to 9 species; Shannon's diversity index varied from 0.46 to 2.19, with expressed change of the dominant species. Habitat generalist species were favored, while habitat specialists were disadvantaged. As the meadow transformation progressed, unfavored states in the SM community prevailed, with excessive numbers of granivore and herbivore species present. Our study suggests that multiple disturbances may lead to an increase in relative abundance, species richness, and diversity within the SM assemblages.

Environmental Factors Are Stronger Predictors of Primate Species’ Distributions Than Basic Biological Traits

Understanding the neutral, biological, and environmental processes driving species distributions is valuable in informing conservation efforts because it will help us predict how species will respond to changes in environmental conditions. Environmental processes affect species differently according to their biological traits, which determine how they interact with their environment. Therefore, functional, trait-based modeling approaches are considered important for predicting distributions and species responses to change but even for data-rich primate communities our understanding of the relationships between traits and environmental conditions is limited. Here we use a large-scale, high-resolution data set of African diurnal primate distributions, biological traits, and environmental conditions to investigate the role of biological traits and environmental trait filtering in primate distributions. We collected data from published sources for 354 sites and 14 genera with 57 species across sub-Saharan Africa. We then combined a three-table ordination method, RLQ, with the fourth-corner approach to test relationships between environmental variables and biological traits and used a mapping approach to visually assess patterning in primate genus and species’ distributions. We found no significant relationships between any groups of environmental variables and biological traits, despite a clear role of environmental filtering in driving genus and species’ distributions. The most important environmental driver of species distributions was temperature seasonality, followed by rainfall. We conclude that the relative flexibility of many primate genera means that not any one particular set of traits drives their species–environment associations, despite the clear role of such associations in their distribution patterns.