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Conservation of the jaguar relies on holistic and transdisciplinary conservation strategies that integratively safeguard essential, connected habitats, sustain viable populations and their genetic exchange, and foster peaceful human-jaguar coexistence. These strategies define four research priorities to advance jaguar conservation throughout the species’ range. In this thesis I provide several relevant ecological and sociological insights into these research priorities, each addressed in a separate chapter. I focus on the effects of anthropogenic landscapes on jaguar habitat use and population gene flow, spatial patterns of jaguar habitat suitability and functional population connectivity, and on innovative governance approaches which can work synergistically to help achieve human-wildlife conviviality. Furthermore, I translate these insights into recommendations for conservation practice by providing tools and suggestions that conservation managers and stakeholders can use to implement local actions but also make broad scale conservation decisions in Central America. In Chapter 2, I model regional habitat use of jaguars, producing spatially-explicit maps for management of key areas of habitat suitability. Using an occupancy model of 13-year-camera-trap occurrence data, I show that human influence has the strongest impact on jaguar habitat use, and that Jaguar Conservation Units are the most important reservoirs of high quality habitat in this region. I build upon these results by zooming in to an area of high habitat suitability loss in Chapter 3, northern Central America. Here I study the drivers of jaguar gene flow and I produce spatially-explicit maps for management of key areas of functional population connectivity in this region. I use microsatellite data and pseudo-optimized multiscale, multivariate resistance surfaces of gene flow to show that jaguar gene flow is influenced by environmental, and even more strongly, by human influence variables; and that the areas of lowest gene flow resistance largely coincide with the location of the Jaguar Conservation Units. Given that human activities significantly impact jaguar habitat use and gene flow, securing viable jaguar populations in anthropogenic landscapes also requires fostering peaceful human-wildlife coexistence. This is a complex challenge that cannot be met without transdisciplinary academic research and cross-sectoral, collaborative governance structures that effectively respond to the multiple challenges of such coexistence. With this in mind, I focus in Chapter 4 on carnivore conservation initiatives that apply transformative governance approaches to enact transformative change towards human-carnivore coexistence. Using the frameworks of transformative biodiversity governance and convivial conservation, I highlight in this chapter concrete pathways, supported by more inclusive, democratic forms of conservation decision-making and participation that promote truly transformative changes towards human-jaguar conviviality.
Predation drives coexistence, evolution and population dynamics of species in food webs, and has strong impacts on related ecosystem functions (e.g. primary production). The effect of predation on these processes largely depends on the trade-offs between functional traits in the predator and prey community. Trade-offs between defence against predation and competitive ability, for example, allow for prey speciation and predator-mediated coexistence of prey species with different strategies (defended or competitive), which may stabilize the overall food web dynamics. While the importance of such trade-offs for coexistence is widely known, we lack an understanding and the empirical evidence of how the variety of differently shaped trade-offs at multiple trophic levels affect biodiversity, trait adaptation and biomass dynamics in food webs. Such mechanistic understanding is crucial for predictions and management decisions that aim to maintain biodiversity and the capability of communities to adapt to environmental change ensuring their persistence.
In this dissertation, after a general introduction to predator-prey interactions and tradeoffs, I first focus on trade-offs in the prey between qualitatively different types of defence (e.g. camouflage or escape behaviour) and their costs. I show that these different types lead to different patterns of predator-mediated coexistence and population dynamics, by using a simple predator-prey model. In a second step, I elaborate quantitative aspects of trade-offs and demonstrates that the shape of the trade-off curve in combination with trait-fitness relationships strongly affects competition among different prey types: Either specialized species with extreme trait combinations (undefended or completely defended) coexist, or a species with an intermediate defence level dominates. The developed theory on trade-off shapes and coexistence is kept general, allowing for applications apart from defence-competitiveness trade-offs. Thirdly, I tested the theory on trade-off shapes on a long-term field data set of phytoplankton from Lake Constance. The measured concave trade-off between defence and growth governs seasonal trait changes of phytoplankton in response to an altering grazing pressure by zooplankton, and affects the maintenance of trait variation in the community. In a fourth step, I analyse the interplay of different tradeoffs at multiple trophic levels with plankton data of Lake Constance and a corresponding tritrophic food web model. The results show that the trait and biomass dynamics of the different three trophic levels are interrelated in a trophic biomass-trait cascade, leading to unintuitive patterns of trait changes that are reversed in comparison to predictions from bitrophic systems. Finally, in the general discussion, I extract main ideas on trade-offs in multitrophic systems, develop a graphical theory on trade-off-based coexistence, discuss the interplay of intra- and interspecific trade-offs, and end with a management-oriented view on the results of the dissertation, describing how food webs may respond to future global changes, given their trade-offs.
Trade-offs between functional traits are ubiquitous in nature and can promote species coexistence depending on their shape. Classic theory predicts that convex trade-offs facilitate coexistence of specialized species with extreme trait values (extreme species) while concave trade-offs promote species with intermediate trait values (intermediate species). We show here that this prediction becomes insufficient when the traits translate non-linearly into fitness which frequently occurs in nature, e.g., an increasing length of spines reduces grazing losses only up to a certain threshold resulting in a saturating or sigmoid trait-fitness function. We present a novel, general approach to evaluate the effect of different trade-off shapes on species coexistence. We compare the trade-off curve to the invasion boundary of an intermediate species invading the two extreme species. At this boundary, the invasion fitness is zero. Thus, it separates trait combinations where invasion is or is not possible. The invasion boundary is calculated based on measurable trait-fitness relationships. If at least one of these relationships is not linear, the invasion boundary becomes non-linear, implying that convex and concave trade-offs not necessarily lead to different coexistence patterns. Therefore, we suggest a new ecological classification of trade-offs into extreme-favoring and intermediate-favoring which differs from a purely mathematical description of their shape. We apply our approach to a well-established model of an empirical predator-prey system with competing prey types facing a trade-off between edibility and half-saturation constant for nutrient uptake. We show that the survival of the intermediate prey depends on the convexity of the trade-off. Overall, our approach provides a general tool to make a priori predictions on the outcome of competition among species facing a common trade-off in dependence of the shape of the trade-off and the shape of the trait-fitness relationships.
It is well-known that prey species often face trade-offs between defense against predation and competitiveness, enabling predator-mediated coexistence. However, we lack an understanding of how the large variety of different defense traits with different competition costs affects coexistence and population dynamics. Our study focusses on two general defense mechanisms, that is, pre-attack (e.g., camouflage)and post-attack defenses (e.g., weaponry) that act at different phases of the predator—prey interaction. We consider a food web model with one predator, two prey types and one resource. One prey type is undefended, while the other one is pre-or post-attack defended paying costs either by a higher half-saturation constant for resource uptake or a lower maximum growth rate. We show that post-attack defenses promote prey coexistence and stabilize the population dynamics more strongly than pre-attack defenses by interfering with the predator’s functional response: Because the predator spends time handling “noncrackable” prey, the undefended prey is indirectly
facilitated. A high half-saturation constant as defense costs promotes coexistence more and stabilizes the dynamics less than a low maximum growth rate. The former imposes high costs at low resource concentrations but allows for temporally high growth rates at predator-induced resource peaks preventing the extinction of
the defended prey. We evaluate the effects of the different defense mechanisms and costs on coexistence under different enrichment levels in order to vary the importance of bottom-up and top-down control of the prey community.
It is well-known that prey species often face trade-offs between defense against predation and competitiveness, enabling predator-mediated coexistence. However, we lack an understanding of how the large variety of different defense traits with different competition costs affects coexistence and population dynamics. Our study focusses on two general defense mechanisms, that is, pre-attack (e.g., camouflage) and post-attack defenses (e.g., weaponry) that act at different phases of the predator—prey interaction. We consider a food web model with one predator, two prey types and one resource. One prey type is undefended, while the other one is pre-or post-attack defended paying costs either by a higher half-saturation constant for resource uptake or a lower maximum growth rate. We show that post-attack defenses promote prey coexistence and stabilize the population dynamics more strongly than pre-attack defenses by interfering with the predator’s functional response: Because the predator spends time handling “noncrackable” prey, the undefended prey is indirectly
facilitated. A high half-saturation constant as defense costs promotes coexistence more and stabilizes the dynamics less than a low maximum growth rate. The former imposes high costs at low resource concentrations but allows for temporally high growth rates at predator-induced resource peaks preventing the extinction of the defended prey. We evaluate the effects of the different defense mechanisms and costs on coexistence under different enrichment levels in order to vary the importance of bottom-up and top-down control of the prey community.
The shape of a defense-growth trade-off governs seasonal trait dynamics in natural phytoplankton
(2020)
Theory predicts that trade-offs, quantifying costs of functional trait adjustments, crucially affect community trait adaptation to altered environmental conditions, but empirical verification is scarce. We evaluated trait dynamics (antipredator defense, maximum growth rate, and phosphate affinity) of a lake phytoplankton community in a seasonally changing environment, using literature trait data and 21 years of species-resolved high-frequency biomass measurements. The trait data indicated a concave defense-growth trade-off, promoting fast-growing species with intermediate defense. With seasonally increasing grazing pressure, the community shifted toward higher defense levels at the cost of lower growth rates along the trade-off curve, while phosphate affinity explained some deviations from it. We discuss how low fitness differences of species, inferred from model simulations, in concert with stabilizing mechanisms, e.g., arising from further trait dimensions, may lead to the observed phytoplankton diversity. In conclusion, quantifying trade-offs is key for predictions of community trait adaptation and biodiversity under environmental change.
The shape of a defense-growth trade-off governs seasonal trait dynamics in natural phytoplankton
(2020)
Theory predicts that trade-offs, quantifying costs of functional trait adjustments, crucially affect community trait adaptation to altered environmental conditions, but empirical verification is scarce. We evaluated trait dynamics (antipredator defense, maximum growth rate, and phosphate affinity) of a lake phytoplankton community in a seasonally changing environment, using literature trait data and 21 years of species-resolved high-frequency biomass measurements. The trait data indicated a concave defense-growth trade-off, promoting fast-growing species with intermediate defense. With seasonally increasing grazing pressure, the community shifted toward higher defense levels at the cost of lower growth rates along the trade-off curve, while phosphate affinity explained some deviations from it. We discuss how low fitness differences of species, inferred from model simulations, in concert with stabilizing mechanisms, e.g., arising from further trait dimensions, may lead to the observed phytoplankton diversity. In conclusion, quantifying trade-offs is key for predictions of community trait adaptation and biodiversity under environmental change.
Toxic phytoplankton blooms have increased in many waterbodies worldwide with well-known negative impacts on human health, fisheries and ecosystems. However, why and how phytoplankton evolved toxin production is still a puzzling question, given that the producer that pays the costs often shares the benefit with other competing algae and thus provides toxins as a 'public good' (e.g. damaging a common competitor or predator). Furthermore, blooming phytoplankton species often show a high intraspecific variation in toxicity and we lack an understanding of what drives the dynamics of coexisting toxic and non-toxic genotypes. Here, by using an individual-based two-dimensional model, we show that small-scale patchiness of phytoplankton strains caused by demography can explain toxin evolution in phytoplankton with low motility and the maintenance of genetic diversity within their blooms. This patchiness vanishes for phytoplankton with high diffusive motility, suggesting different evolutionary pathways for different phytoplankton groups. In conclusion, our study reveals that small-scale spatial heterogeneity, generated by cell division and counteracted by diffusive cell motility and turbulence, can crucially affect toxin evolution and eco-evolutionary dynamics in toxic phytoplankton species. This contributes to a better understanding of conditions favouring toxin production and the evolution of public goods in asexually reproducing organisms in general.
Give chance a chance
(2019)
A large part of biodiversity theory is driven by the basic question of what allows species to coexist in spite of a confined number of niches. A substantial theoretical background to this question is provided by modern coexistence theory (MCT), which rests on mathematical approaches of invasion analysis to categorize underlying mechanisms into factors that reduce either niche overlap (stabilizing mechanisms) or the average fitness differences of species (equalizing mechanisms). While MCT has inspired biodiversity theory in the search for these underlying mechanisms, we feel that the strong focus on coexistence causes a bias toward the most abundant species and neglects the plethora of species that are less abundant and often show high local turnover. Given the more stochastic nature of their occurrence, we advocate a complementary cross-level approach that links individuals, small populations, and communities and explicitly takes into account (1) a more complete inclusion of environmental and demographic stochasticity affecting small populations, (2) intraspecific trait variation and behavioral plasticity, and (3) local heterogeneities, interactions, and feedbacks. Focusing on mechanisms that drive the temporary coviability of species rather than infinite coexistence, we suggest a new approach that could be dubbed coviability analysis (CVA). From a modeling perspective, CVA builds on the merged approaches of individual-based modeling and population viability analysis but extends them to the community level. From an empirical viewpoint, CVA calls for a stronger integration of spatiotemporal data on variability and noise, changing drivers, and interactions at the level of individuals. The resulting large volumes of data from multiple sources could be strongly supported by novel techniques tailored to the discovery of complex patterns in high-dimensional data. By complementing MCT through a stronger focus on the coviability of less common species, this approach can help make modern biodiversity theory more comprehensive, predictive, and relevant for applications.
Give chance a chance
(2019)
A large part of biodiversity theory is driven by the basic question of what allows species to coexist in spite of a confined number of niches. A substantial theoretical background to this question is provided by modern coexistence theory (MCT), which rests on mathematical approaches of invasion analysis to categorize underlying mechanisms into factors that reduce either niche overlap (stabilizing mechanisms) or the average fitness differences of species (equalizing mechanisms). While MCT has inspired biodiversity theory in the search for these underlying mechanisms, we feel that the strong focus on coexistence causes a bias toward the most abundant species and neglects the plethora of species that are less abundant and often show high local turnover. Given the more stochastic nature of their occurrence, we advocate a complementary cross-level approach that links individuals, small populations, and communities and explicitly takes into account (1) a more complete inclusion of environmental and demographic stochasticity affecting small populations, (2) intraspecific trait variation and behavioral plasticity, and (3) local heterogeneities, interactions, and feedbacks. Focusing on mechanisms that drive the temporary coviability of species rather than infinite coexistence, we suggest a new approach that could be dubbed coviability analysis (CVA). From a modeling perspective, CVA builds on the merged approaches of individual-based modeling and population viability analysis but extends them to the community level. From an empirical viewpoint, CVA calls for a stronger integration of spatiotemporal data on variability and noise, changing drivers, and interactions at the level of individuals. The resulting large volumes of data from multiple sources could be strongly supported by novel techniques tailored to the discovery of complex patterns in high-dimensional data. By complementing MCT through a stronger focus on the coviability of less common species, this approach can help make modern biodiversity theory more comprehensive, predictive, and relevant for applications.