# Insights on the effect of mega-carcass abundance on the population dynamics of a facultative scavenger predator and its prey

Mellina Sidous, Sarah Cubaynes, Olivier Gimenez, Nolwenn Drouet, Nolwenn Drouet-Hoguet, Stéphane Dray, Loïc Bollache, Daphine Madhlamoto, Adelaide Nobesuthu, Hervé Fritz, et al.

<figure><img src="image_1.png" /></figure>

## To cite this version:

Mellina Sidous, Sarah Cubaynes, Olivier Gimenez, Nolwenn Drouet, Nolwenn Drouet-Hoguet, et al. "Insights on the effect of mega-carcass abundance on the population dynamics of a facultative scavenger predator and its prey." Peer Community Journal, 2024, 4, pp.e53. 10.24072/pci.ecology.100594. hal-04620548

HAL Id: hal-04620548

https://hal.science/hal-04620548

Submitted on 21 Jun 2024

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.
# Insights on the effect of mega-carcass abundance on the population dynamics of a facultative scavenger predator and its prey

Mellina Sidous, Sarah Cubaynes, Olivier Gimenez, Nolwenn Drouet-Hoguet, Stéphane Dray, Loïc Bollache, Daphine Madhlamoto, Adelaide Nobesuthu, Hervé Fritz, Marion Valeix

<figure><img src="image_1.png" /></figure>

## To cite this version:

Mellina Sidous, Sarah Cubaynes, Olivier Gimenez, Nolwenn Drouet-Hoguet, Stéphane Dray, et al. "Insights on the effect of mega-carcass abundance on the population dynamics of a facultative scavenger predator and its prey." Peer Community Journal, 2024, 4, pp.e53. 10.24072/pci.ecology.100594. hal-04620548

HAL Id: hal-04620548

https://hal.science/hal-04620548v1

Submitted on 21 Jun 2024

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>
Research article

Published
2024-05-30

**Cite as**
Mellina Sidous, Sarah Cubaynes, Olivier Gimenez, Nolwenn Drouet-Hoguet, Stéphane Dray, Loïc Bollache, Daphine Madhlamoto, Nobesuthu Adelaide Ngwenya, Hervé Fritz and Marion Valeix (2024) *Insights on the effect of mega-carcass abundance on the population dynamics of a facultative scavenger predator and its prey*, Peer Community Journal, 4: e53.

**Correspondence**
mellina sidous@gmail.com

**Peer-review**
Peer reviewed and
recommended by
PCI Ecology,
https://doi.org/10.24072/pci.ecology.100594

<figure><img src="image_2.png" /></figure>

This article is licensed
under the Creative Commons
Attribution 4.0 License.

# Insights on the effect of mega-carcass abundance on the population dynamics of a facultative scavenger predator and its prey

Mellina Sidous<sup>1,2</sup>, Sarah Cubaynes<sup>1,2</sup>, Olivier Gimenez<sup>1,2</sup>, Nolwenn Drouet-Hoguet<sup>1,3,4,5</sup>, Stéphane Dray<sup>1,3</sup>, Loïc Bollache<sup>1,1</sup>, Daphine Madhlamoto<sup>1,6</sup>, Nobesuthu Adelaide Ngwenya<sup>1,6</sup>, Hervé Fritz<sup>1,#,3,4,7,8</sup>, and Marion Valeix<sup>1,#,2,3,4</sup>

Volume 4 (2024), article e53

https://doi.org/10.24072/pcjournal.420

## Abstract

The interplay between facultative scavenging and predation has gained interest in the last decade. The prevalence of scavenging induced by the availability of large carcasses may modify predator density or behaviour, potentially affecting prey. In contrast to behavioural mechanisms through which scavenging affects predation, the demographic effects of facultative scavenging on predator and prey populations remain poorly studied. We used the semi-natural experimental opportunity in Hwange National Park, Zimbabwe, where contrasted management measures (culling and artificial supply of water) have led to fluctuations in elephant carrion abundance, to identify the consequences of facultative scavenging on the population dynamics of a large mammalian carnivore, the spotted hyaena (* Crocuta crocuta*), and its prey. Using a 50-year dataset and Multivariate Autoregressive State Space models, we estimated hyaena and prey densities over four periods contrasted in elephant carrion availability due to management practices. Models that allow hyaena and their prey populations' growth rate to vary depending on these four periods contributed significantly to explain variations in their density, which is consistent with an effect of management measures on the population dynamics of hyaena and its prey. Although our results support a predominant role of bottom-up mechanisms, whereby hyaena density is driven by herbivore density, itself driven by resources availability, some subtle patterns of densities could be interpreted as consequences of changes in predation pressure following changes in scavenging opportunities. We discuss why signals of prey and predator population dynamics decoupling are less likely to be observed in systems with a high diversity of prey, such as African savannas, and why inputs of mega-carcasses as pulsed resources hardly impacted top-down relationships in the long run. This study represents a first investigation of the long-term effects of carrion pulses, whose frequency may increase with climate changes, on the classical predator-prey coupling for large mammals.

<figure><img src="image_3.png" /></figure>

<footnote n="1">¹Chrono-environnement UMR6249, CNRS, Université Bourgogne Franche-Comté, Besançon, France, ²CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France, ³CNRS, Université de Lyon, Université de Lyon 1, VetAgroSup, LBBE, UMR 5558, Villeurbanne, France, ⁴Long-Term Socio-Ecological Research Site (LTSER) France, Zone Atelier "Hwange", Hwange National Park, Bag 62, Dete, Zimbabwe, ⁵OFB, Gières, France, ⁶Zimbabwe Parks and Wildlife Management Authority, Main Camp Research, Hwange National Park, Zimbabwe, ⁷REHABS International Research Laboratory, CNRS – Université Lyon – Nelson Mandela University, George Campus, South Africa, ⁸Sustainability Research Unit, Nelson Mandela University, George Campus, South Africa, #Equal contribution</footnote>
# Peer Community Journal

Section: Ecology

Research article

Published
2024-05-30

**Cite as**
Mellina Sidous, Sarah Cubaynes, Olivier Gimenez, Nolwenn Drouet-Hoguet, Stéphane Dray, Loïc Bollache, Daphine Madhlamoto, Nobesuthu Adelaide Ngwenya, Hervé Fritz and Marion Valeix (2024) *Insights on the effect of mega-carcass abundance on the population dynamics of a facultative scavenger predator and its prey*, Peer Community Journal, 4: e53.

**Correspondence**
mellina sidous@gmail.com

**Peer-review**
Peer reviewed and
recommended by
PCI Ecology,
https://doi.org/10.24072/pci.ecology.100594

<figure><img src="image_3.png" /></figure>

This article is licensed
under the Creative Commons
Attribution 4.0 License.

## Insights on the effect of mega-carcass abundance on the population dynamics of a facultative scavenger predator and its prey

Mellina Sidous<sup>1,2</sup>, Sarah Cubaynes<sup>1,2</sup>, Olivier Gimenez<sup>1,2</sup>, Nolwenn Drouet-Hoguet<sup>1,3,4,5</sup>, Stéphane Dray<sup>1,3</sup>, Loïc Bollache<sup>1,1</sup>, Daphine Madhlamoto<sup>1,6</sup>, Nobesuthu Adelaide Ngwenya<sup>1,6</sup>, Hervé Fritz<sup>1,#,3,4,7,8</sup>, and Marion Valeix<sup>1,#,2,3,4</sup>

Volume 4 (2024), article e53

https://doi.org/10.24072/pcjournal.420

## Abstract

The interplay between facultative scavenging and predation has gained interest in the last decade. The prevalence of scavenging induced by the availability of large carcasses may modify predator density or behaviour, potentially affecting prey. In contrast to behavioural mechanisms through which scavenging affects predation, the demographic effects of facultative scavenging on predator and prey populations remain poorly studied. We used the semi-natural experimental opportunity in Hwange National Park, Zimbabwe, where contrasted management measures (culling and artificial supply of water) have led to fluctuations in elephant carrion abundance, to identify the consequences of facultative scavenging on the population dynamics of a large mammalian carnivore, the spotted hyaena (*Crocuta crocuta*), and its prey. Using a 50-year dataset and Multivariate Autoregressive State Space models, we estimated hyaena and prey densities over four periods contrasted in elephant carrion availability due to management practices. Models that allow hyaena and their prey populations' growth rate to vary depending on these four periods contributed significantly to explain variations in their density, which is consistent with an effect of management measures on the population dynamics of hyaena and its prey. Although our results support a predominant role of bottom-up mechanisms, whereby hyaena density is driven by herbivore density, itself driven by resources availability, some subtle patterns of densities could be interpreted as consequences of changes in predation pressure following changes in scavenging opportunities. We discuss why signals of prey and predator population dynamics decoupling are less likely to be observed in systems with a high diversity of prey, such as African savannas, and why inputs of mega-carcasses as pulsed resources hardly impacted top-down relationships in the long run. This study represents a first investigation of the long-term effects of carrion pulses, whose frequency may increase with climate changes, on the classical predator-prey coupling for large mammals.

<figure><img src="image_4.png" /></figure>

<footnote n="1">¹Chrono-environnement UMR6249, CNRS, Université Bourgogne Franche-Comté, Besançon, France, ²CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France, ³CNRS, Université de Lyon, Université de Lyon 1, VetAgroSup, LBBE, UMR 5558, Villeurbanne, France, ⁴Long-Term Socio-Ecological Research Site (LTSER) France, Zone Atelier "Hwange", Hwange National Park, Bag 62, Dete, Zimbabwe, ⁵OFB, Gières, France, ⁶Zimbabwe Parks and Wildlife Management Authority, Main Camp Research, Hwange National Park, Zimbabwe, ⁷REHABS International Research Laboratory, CNRS – Université Lyon – Nelson Mandela University, George Campus, South Africa, ⁸Sustainability Research Unit, Nelson Mandela University, George Campus, South Africa, #Equal contribution</footnote>
Introduction

Predation is the most studied ecological process amongst those linking predator and prey populations (Abrams, 2000; Guiden et al., 2019). Although overlooked, scavenging (i.e., when animals feed on dead animals and carrion they did not kill) recently gained increased recognition (Wilson & Wolkovich, 2011; Moleón & Sánchez-Zapata, 2015; Luna et al., 2021). So far, these two processes have been little investigated together. However, because carnivores are often facultative scavengers (Fallows et al., 2013; Pereira et al., 2014; Gomo et al., 2017), and scavenging is energetically less costly than hunting, carnivores are distracted from their live prey in the presence of carrion (DeVault et al., 2003). Consequently, carrion and live prey are linked by indirect interactions. On the one hand, live prey can be subject to a higher predation pressure, or hyper-predation, if carrion pulses lead to a higher abundance of carnivores (through demography or local immigration) but carrions are not available (because of interference or decay of the carrion; Courchamp et al., 2000; Cortés-Avizanda et al., 2009a; Cortés-Avizanda et al., 2009b; Moleón et al., 2014). On the other hand, live prey can be subject to a reduced predation pressure, or hypo-predation, if carnivore abundance remains stable but the rate of consumption of live prey is reduced and compensated by scavenging (Bate & Hilker, 2012; Fallows et al., 2013; Moleón et al., 2014; Mellard et al., 2021). These indirect interactions linking carrion and live prey, through predators, have been studied theoretically (Andrén et al., 2011; Mellard et al., 2021), experimentally (Cortés-Avizanda et al., 2009a), and empirically (Cortés-Avizanda et al., 2009b; Fallows et al., 2013). These previous works globally conclude about an impact of scavenging on predation processes, either through an alteration of prey and predator space use (Cortés-Avizanda et al., 2009b), or through a direct modification of predation rate (Cortés-Avizanda et al., 2009a; Andrén et al., 2011, Fallows et al., 2013, Mellard et al., 2021). However, there is a dearth of long-term, demographic studies to assess whether regular pulses of carrion interfere with the population dynamics of predators and their prey (Moleón et al., 2014). Still, in some systems, population dynamics of a meso-predator was affected by facultative scavenging with associated consequences on prey populations (Roth, 2003), and regular anthropogenic food subsidies seem able to alter the classical predator-prey coupling of population dynamics (Rodewald et al., 2011). The megafauna is unparalleled in the extent of carrion they provide after death and mega-carcasses (from whales, elephants, rhinoceros) can be considered as important pulsed resources for scavengers.

In this study, we explored the effect of elephant (*Loxodonta Africana*) carrion availability on the numerical response of a large opportunist scavenging predator, the spotted hyaena (*Crocuta crocuta*; hyaena hereafter; Figure 1; Kruuk, 1972) and its prey. Their relative abundances were monitored over almost 50 years in Hwange National Park, Zimbabwe. As a consequence of management practices, this ecosystem witnessed large variations in the abundance of the elephant population over the years, and four periods characterized by different elephant carcass availability emerged.

From 1960 to 1986, massive culls were undertaken in the area in response to an elephant abundance perceived as “too high” regarding its effects on biodiversity (Child 2004; Slotow et al., 2008). During this time, at least 18,000 elephants were killed (Cumming, 1981; ZPWMA, 1998; Child, 2004; Slotow et al., 2008). This period corresponds to our first study period (1972-1986) when hundreds to a few thousand carcasses were available annually and clearly provided an important source of food for scavengers. In contrast, in the second period (1987-1992), elephant carcasses were rare due to a steady and continuous increase of the population triggered by surface water management (pumping of underground water) and the cessation of culling (Chamaillé-Jammes et al., 2008). This lasted until the third period (1993-2008) when elephant carcasses were present in large numbers in dry years (Dudley et al., 2001), after the elephant population reached density-dependence, fluctuating around 30,000 individuals (Chamaillé-Jammes et al., 2008). Finally, during the fourth period (2009-2020), natural mortality (density-dependence driven) still existed but was likely to be much lower than during period 3 due to the creation
Introduction

Predation is the most studied ecological process amongst those linking predator and prey populations (Abrams, 2000; Guiden et al., 2019). Although overlooked, scavenging (i.e., when animals feed on dead animals and carrion they did not kill) recently gained increased recognition (Wilson & Wolkovich, 2011; Moleón & Sánchez-Zapata, 2015; Luna et al., 2021). So far, these two processes have been little investigated together. However, because carnivores are often facultative scavengers (Fallows et al., 2013; Pereira et al., 2014; Gomo et al., 2017), and scavenging is energetically less costly than hunting, carnivores are distracted from their live prey in the presence of carrion (DeVault et al., 2003). Consequently, carrion and live prey are linked by indirect interactions. On the one hand, live prey can be subject to a higher predation pressure, or hyper-predation, if carrion pulses lead to a higher abundance of carnivores (through demography or local immigration) but carrions are not available (because of interference or decay of the carrion; Courchamp et al., 2000; Cortés-Avizanda et al., 2009a; Cortés-Avizanda et al., 2009b; Moleón et al., 2014). On the other hand, live prey can be subject to a reduced predation pressure, or hypo-predation, if carnivore abundance remains stable but the rate of consumption of live prey is reduced and compensated by scavenging (Bate & Hilker, 2012; Fallows et al., 2013; Moleón et al., 2014; Mellard et al., 2021). These indirect interactions linking carrion and live prey, through predators, have been studied theoretically (Andrén et al., 2011; Mellard et al., 2021), experimentally (Cortés-Avizanda et al., 2009a), and empirically (Cortés-Avizanda et al., 2009b; Fallows et al., 2013). These previous works globally conclude about an impact of scavenging on predation processes, either through an alteration of prey and predator space use (Cortés-Avizanda et al., 2009b), or through a direct modification of predation rate (Cortés-Avizanda et al., 2009a; Andrén et al., 2011, Fallows et al., 2013, Mellard et al., 2021). However, there is a dearth of long-term, demographic studies to assess whether regular pulses of carrion interfere with the population dynamics of predators and their prey (Moleón et al., 2014). Still, in some systems, population dynamics of a meso-predator was affected by facultative scavenging with associated consequences on prey populations (Roth, 2003), and regular anthropogenic food subsidies seem able to alter the classical predator-prey coupling of population dynamics (Rodewald et al., 2011). The megafauna is unparalleled in the extent of carrion they provide after death and mega-carcasses (from whales, elephants, rhinoceros) can be considered as important pulsed resources for scavengers.

In this study, we explored the effect of elephant (*Loxodonta Africana*) carrion availability on the numerical response of a large opportunist scavenging predator, the spotted hyaena (*Crocuta crocuta*; hyaena hereafter; Figure 1; Kruuk, 1972) and its prey. Their relative abundances were monitored over almost 50 years in Hwange National Park, Zimbabwe. As a consequence of management practices, this ecosystem witnessed large variations in the abundance of the elephant population over the years, and four periods characterized by different elephant carcass availability emerged.

From 1960 to 1986, massive culls were undertaken in the area in response to an elephant abundance perceived as “too high” regarding its effects on biodiversity (Child 2004; Slotow et al., 2008). During this time, at least 18,000 elephants were killed (Cumming, 1981; ZPWMA, 1998; Child, 2004; Slotow et al., 2008). This period corresponds to our first study period (1972-1986) when hundreds to a few thousand carcasses were available annually and clearly provided an important source of food for scavengers. In contrast, in the second period (1987-1992), elephant carcasses were rare due to a steady and continuous increase of the population triggered by surface water management (pumping of underground water) and the cessation of culling (Chamaillé-Jammes et al., 2008). This lasted until the third period (1993-2008) when elephant carcasses were present in large numbers in dry years (Dudley et al., 2001), after the elephant population reached density-dependence, fluctuating around 30,000 individuals (Chamaillé-Jammes et al., 2008). Finally, during the fourth period (2009-2020), natural mortality (density-dependence driven) still existed but was likely to be much lower than during period 3 due to the creation
of additional waterholes (Appendix 1), allowing the elephant population to pass the equilibrium reached previously and start growing again.

The existence of these four periods contrasted in terms of elephant carcass availability, together with the long-term monitoring of hyaena and prey populations covering these different periods, provide a unique semi-natural experimental opportunity to assess the impacts of scavenging on predation at the level of population dynamics. Here, we hypothesised that if scavenging modified predation with further demographic consequences, we would detect that the Hwange hyaena population is driven by its live prey in periods of low elephant carcass abundance (periods 2 and 4), but less dependent of its live prey in periods of high elephant carcass abundance (periods 1 and 3).

<figure><img src="image_4.png" /><figcaption>**Figure 1** – Hyaena scavenging from an elephant carcass (credit: S. Périquet).</figcaption></figure>

# Material and methods

## Study area

Hwange National Park covers ~15,000 km² of dystrophic semi-arid (~600 mm annual rainfall) savanna in western Zimbabwe (19°00' S, 26°30' E). This protected area is not fenced, which allows wildlife to move freely. Surface water is primarily found in natural depressions that hold rainwater. However, most dry up as the dry season progresses, from May to September. To guarantee sufficient drinking water for animals in the dry season, water has been managed since 1935 through the pumping of underground water into waterholes distributed mostly in the northern sector of the park. The number of pumped waterholes has varied over the past 50 years, from about 20 to more than 80 (Appendix 1). In this study, we focus on a 3,000 km² study area in the north-eastern sector of the park (Figure 2), the most touristic area of the park, with most pumped waterholes.

## Data

We used a long-term data set resulting from the regular monitoring of waterholes from 1972 to 2020 by the Wildlife Environment Zimbabwe (WEZ). This monitoring consists of annual 24h counts during full moon at the end of the dry season. At this time of the year, it is mostly the pumped waterholes that provide drinking water to animals, but in years with a good rainy season a few natural pans can hold water too. Because this monitoring is largely influenced by annual rainfall, counts are characterized by
of additional waterholes (Appendix 1), allowing the elephant population to pass the equilibrium reached previously and start growing again.

The existence of these four periods contrasted in terms of elephant carcass availability, together with the long-term monitoring of hyaena and prey populations covering these different periods, provide a unique semi-natural experimental opportunity to assess the impacts of scavenging on predation at the level of population dynamics. Here, we hypothesised that if scavenging modified predation with further demographic consequences, we would detect that the Hwange hyaena population is driven by its live prey in periods of low elephant carcass abundance (periods 2 and 4), but less dependent of its live prey in periods of high elephant carcass abundance (periods 1 and 3).

<figure><img src="image_5.png" /><figcaption>**Figure 1** – Hyaena scavenging from an elephant carcass (credit: S. Périquet).</figcaption></figure>

# Material and methods

## Study area

Hwange National Park covers ~15,000 km² of dystrophic semi-arid (~600 mm annual rainfall) savanna in western Zimbabwe (19°00' S, 26°30' E). This protected area is not fenced, which allows wildlife to move freely. Surface water is primarily found in natural depressions that hold rainwater. However, most dry up as the dry season progresses, from May to September. To guarantee sufficient drinking water for animals in the dry season, water has been managed since 1935 through the pumping of underground water into waterholes distributed mostly in the northern sector of the park. The number of pumped waterholes has varied over the past 50 years, from about 20 to more than 80 (Appendix 1). In this study, we focus on a 3,000 km² study area in the north-eastern sector of the park (Figure 2), the most touristic area of the park, with most pumped waterholes.

## Data

We used a long-term data set resulting from the regular monitoring of waterholes from 1972 to 2020 by the Wildlife Environment Zimbabwe (WEZ). This monitoring consists of annual 24h counts during full moon at the end of the dry season. At this time of the year, it is mostly the pumped waterholes that provide drinking water to animals, but in years with a good rainy season a few natural pans can hold water too. Because this monitoring is largely influenced by annual rainfall, counts are characterized by
high inter-annual variance and cannot be considered as total counts. However, abundance trends detected with these counts are the same as those obtained with other more traditional methods (aerial census; Valeix et al., 2008), and thus we consider that long-term trends inferred from counts at waterholes reliably reflect actual patterns of abundance changes in populations (as in Valeix et al., 2008). Additionally, to control for the proximity of some waterholes and the differential surfaces considered as their attraction zones, monitored waterholes were grouped in new spatial units created using a centroidal Voronoi tessellation based on pumped waterholes (Okabe et al., 2009) (Figure 2).

<figure><img src="image_5.png" /><figcaption>**Figure 2** – Hwange National Park and location of the study area. Black dots are the waterholes of the park. Voronoi polygons used for the analysis are coloured in blue.</figcaption></figure>

Indeed, if we consider a pumped waterhole, its attraction is reduced when new pumped waterholes are added in its vicinity or in years when neighbouring natural pans still hold water. In other words, we expect less animals to be observed at a focused pumped waterhole since animals should split between this waterhole and the neighbouring pumped or natural waterholes. This could lead to conclude that species abundance has decreased when in reality the patterns observed come from this change in attraction rather than an actual decrease in regional abundance of species. By grouping neighbouring waterholes into new spatial units, we expect to reduce this bias arising from the fluctuation in the number of waterholes over years. For each year, we summed the animals counted at the different waterholes of each spatial unit and divided it by the number of waterholes surveyed, and then by the surface of the spatial unit to assess densities. For political (civil war) and economic reasons, counts were not performed in 1974, 1983, and from 1976 to 1981. For the analyses, we selected the spatial units that were monitored for at least 50% of the years composing each period, except for period 1 (the period impacted by the civil war) for which we kept units with at least 25% of the years monitored.

One spatial unit was excluded for the analyses because it is known for the presence of a hyaena den close to a waterhole and thus presented singularly high hyaena counts. Counts were log-transformed and we dealt with 0 values by adding 0.0001 to the whole dataset, which corresponds to one order of magnitude lower than the smallest value of the data.
high inter-annual variance and cannot be considered as total counts. However, abundance trends detected with these counts are the same as those obtained with other more traditional methods (aerial census; Valeix et al., 2008), and thus we consider that long-term trends inferred from counts at waterholes reliably reflect actual patterns of abundance changes in populations (as in Valeix et al., 2008). Additionally, to control for the proximity of some waterholes and the differential surfaces considered as their attraction zones, monitored waterholes were grouped in new spatial units created using a centroidal Voronoi tessellation based on pumped waterholes (Okabe et al., 2009) (Figure 2).

<figure><img src="image_6.png" /><figcaption>**Figure 2** – Hwange National Park and location of the study area. Black dots are the waterholes of the park. Voronoi polygons used for the analysis are coloured in blue.</figcaption></figure>

Indeed, if we consider a pumped waterhole, its attraction is reduced when new pumped waterholes are added in its vicinity or in years when neighbouring natural pans still hold water. In other words, we expect less animals to be observed at a focused pumped waterhole since animals should split between this waterhole and the neighbouring pumped or natural waterholes. This could lead to conclude that species abundance has decreased when in reality the patterns observed come from this change in attraction rather than an actual decrease in regional abundance of species. By grouping neighbouring waterholes into new spatial units, we expect to reduce this bias arising from the fluctuation in the number of waterholes over years. For each year, we summed the animals counted at the different waterholes of each spatial unit and divided it by the number of waterholes surveyed, and then by the surface of the spatial unit to assess densities. For political (civil war) and economic reasons, counts were not performed in 1974, 1983, and from 1976 to 1981. For the analyses, we selected the spatial units that were monitored for at least 50% of the years composing each period, except for period 1 (the period impacted by the civil war) for which we kept units with at least 25% of the years monitored.

One spatial unit was excluded for the analyses because it is known for the presence of a hyaena den close to a waterhole and thus presented singularly high hyaena counts. Counts were log-transformed and we dealt with 0 values by adding 0.0001 to the whole dataset, which corresponds to one order of magnitude lower than the smallest value of the data.
We used data on hyaena and its prey. For prey, we pooled all (males, females, adults, sub-adults and juveniles) preferred and common prey species of hyaena in Hwange National Park (Périquet et al., 2015) ('prey' hereafter). This includes plains zebra (Equus quagga), greater kudu (Tragelaphus strepsiceros), sable (Hippotragus niger), roan (Hippotragus equinus), blue wildebeest (Connochaetes taurinus), waterbuck (Kobus ellipsiprymnus), impala (Aepyceros melampus), and warthog (Phacochoerus africanus).

## Statistical analysis

We fitted a Multivariate Autoregressive State-Space Model (Holmes et al., 2012) using R package MARSS (Holmes et al., 2021; R Core Team, 2021) to estimate prey and hyaena densities from 1972 to 2020 from counts data at the waterholes, while accounting for observation and process errors. We considered each spatial unit to be a replicate of a common dynamic, and therefore estimated one density for prey and one density for hyaena for the entire study area. MARSS separates the state process, capturing variations in species density, from the observation process explaining variations in the number of individuals observed at the waterholes. More precisely, the model uses an autoregressive process, which means that the state x of the system at time t ($x_t$) depends on its value at time t-1 ($x_{t-1}$). In our case, x is the densities of a guild (either prey or hyaena) and we have:

$$ (1) \qquad x_t = x_{t-1} + u_t + w_t; w_t \sim \text{Normal}(0, Q) $$

Where $x_t$, $x_{t-1}$, and Q are 1x1 matrices. $x_t$ and $x_{t-1}$ represent the abundance estimated at time t and t-1 respectively for the focus guild. The parameter $w_t$ is the error term of the state process and represents environmental stochasticity. This environmental stochasticity is modelled as a normal distribution with mean 0 and a variance Q that takes the same values for the whole time series of each guild. The parameter $u_t$ is the growth rate of the population at time t. This is the parameter that was constrained differently in the three models we tested. For each time step, the growth rate has a unique value and thus $u_t$ is a 1x1 matrix.

Equation (1) models the state process. Here, it represents the changes in densities of each guild over time. We have only access to a noisy signal of these dynamics through their observation at the different waterholes of our study area. The densities of each guild can thus be considered as “hidden states” and state-space models aim at retrieving access to these states through their signals. This is done by incorporating a second equation that models the observation process:

$$ (2) \qquad y_t = Z_t x_t + D d_t + a + v_t; v_t \sim \text{MVN}(0, R) $$

For n waterholes, $y_t$ has a dimension nx1, where n is the number of waterholes. Z is a nx1 matrix that links the observations $y_t$ to the state to be estimated ($x_t$). Since we only estimated the density of each species separately and not in the same model, Z matrix is full of 1s. The bias parameter, a, is set to be equal between sites. The parameter $v_t$ is the error term of the observation process equation, and comes from a multivariate normal distribution that share a common variance over time and site. The variance is the parameter R a variance covariance matrix of dimension nxn. Finally, $d_t$ is a vector with annual rainfall value at year t, and D further links the effect of $d_t$ to the $y_t$ that are inputs of the model. In this observation model, we included annual rainfall as a covariate to account for bias on counts at waterholes (Appendix 2). During dry years, animals need to visit waterholes more often than during wet years when more ephemeral natural ponds can be found in the park. Thus, when annual rainfall is not incorporated into the model, estimated densities are overestimated in dry years, and underestimated in wet year (Appendix 2).

We fitted and compared (using AICc; Burnham et al., 2011) three different models: (i) a constant model with a constant growth rate, (ii) a time-varying model estimating a growth rate per year, and (iii) a period model estimating one growth rate per period. The period model aims at testing a signal in
We used data on hyaena and its prey. For prey, we pooled all (males, females, adults, sub-adults and juveniles) preferred and common prey species of hyaena in Hwange National Park (Périquet et al., 2015) ('prey' hereafter). This includes plains zebra (Equus quagga), greater kudu (Tragelaphus strepsiceros), sable (Hippotragus niger), roan (Hippotragus equinus), blue wildebeest (Connochaetes taurinus), waterbuck (Kobus ellipsiprymnus), impala (Aepyceros melampus), and warthog (Phacochoerus africanus).

## Statistical analysis

We fitted a Multivariate Autoregressive State-Space Model (Holmes et al., 2012) using R package MARSS (Holmes et al., 2021; R Core Team, 2021) to estimate prey and hyaena densities from 1972 to 2020 from counts data at the waterholes, while accounting for observation and process errors. We considered each spatial unit to be a replicate of a common dynamic, and therefore estimated one density for prey and one density for hyaena for the entire study area. MARSS separates the state process, capturing variations in species density, from the observation process explaining variations in the number of individuals observed at the waterholes. More precisely, the model uses an autoregressive process, which means that the state x of the system at time t ($x_t$) depends on its value at time t-1 ($x_{t-1}$). In our case, x is the densities of a guild (either prey or hyaena) and we have:

$$ (1) \qquad x_t = x_{t-1} + u_t + w_t; w_t \sim \text{Normal}(0, Q) $$

Where $x_t$, $x_{t-1}$, and Q are 1x1 matrices. $x_t$ and $x_{t-1}$ represent the abundance estimated at time t and t-1 respectively for the focus guild. The parameter $w_t$ is the error term of the state process and represents environmental stochasticity. This environmental stochasticity is modelled as a normal distribution with mean 0 and a variance Q that takes the same values for the whole time series of each guild. The parameter $u_t$ is the growth rate of the population at time t. This is the parameter that was constrained differently in the three models we tested. For each time step, the growth rate has a unique value and thus $u_t$ is a 1x1 matrix.

Equation (1) models the state process. Here, it represents the changes in densities of each guild over time. We have only access to a noisy signal of these dynamics through their observation at the different waterholes of our study area. The densities of each guild can thus be considered as “hidden states” and state-space models aim at retrieving access to these states through their signals. This is done by incorporating a second equation that models the observation process:

$$ (2) \qquad y_t = Z_t x_t + D d_t + a + v_t; v_t \sim \text{MVN}(0, R) $$

For n waterholes, $y_t$ has a dimension nx1, where n is the number of waterholes. Z is a nx1 matrix that links the observations $y_t$ to the state to be estimated ($x_t$). Since we only estimated the density of each species separately and not in the same model, Z matrix is full of 1s. The bias parameter, a, is set to be equal between sites. The parameter $v_t$ is the error term of the observation process equation, and comes from a multivariate normal distribution that share a common variance over time and site. The variance is the parameter R a variance covariance matrix of dimension nxn. Finally, $d_t$ is a vector with annual rainfall value at year t, and D further links the effect of $d_t$ to the $y_t$ that are inputs of the model. In this observation model, we included annual rainfall as a covariate to account for bias on counts at waterholes (Appendix 2). During dry years, animals need to visit waterholes more often than during wet years when more ephemeral natural ponds can be found in the park. Thus, when annual rainfall is not incorporated into the model, estimated densities are overestimated in dry years, and underestimated in wet year (Appendix 2).

We fitted and compared (using AICc; Burnham et al., 2011) three different models: (i) a constant model with a constant growth rate, (ii) a time-varying model estimating a growth rate per year, and (iii) a period model estimating one growth rate per period. The period model aims at testing a signal in
densities caused by the fluctuations of elephant carrion availability. We used an analysis of deviance (ANODEV) to test the significance and assess the proportion of the deviance explained by a period-dependent growth rate for hyaena and its prey (Grosbois et al., 2008).

## Results

Observed counts data and estimated population densities for prey and hyaena are provided in Figure 3.

<figure><img src="image_6.png" /></figure>

**Figure 3** – Top: Observed population densities of prey (A) and hyaena (B). Dots correspond to the densities observed at the spatial units (Voronoi polygons), each colour representing a spatial unit (95% of the data kept for visual clarity). Fluctuating thin lines represent the median of the observed densities each year. Bold line and ribbon represent the smooth of the median and its 95% confidence interval. Bottom: Estimated population densities of prey (C) and hyaena (D). Model estimates are given for the constant, time-varying, and period models. The ribbon represents the 95% of these estimates, but is very close to the estimates for the time-varying and period models. In all panels, the dotted vertical lines delimitate the four study periods; elephant pictogram indicates periods of high elephant carcass availability.

The period model best described the variations in density of prey (AICc=1862.05, 1890.89 and 1850.95 for the constant, time-varying and period models respectively) and hyaena (AICc=3039.18, 3089.02 and 3029.70 for the constant, time-varying and period models respectively). ANODEV tests were significant for both prey (F = 0.009; r² = 0.22; df= 3) and hyaena (F= 0.002; r² = 0.28; df = 3). Hyaena and prey densities showed similar trends across the 4 study periods: the two populations increased during period 1, stabilized during period 2, decreased during period 3, and increased again during period 4 (Figure 3; period models' growth rate estimates in Table 1). Differences between growth rate of hyaena and prey are similar in both periods when elephant carcasses were less abundant (0.069 in the second and 0.063 in the fourth period; Table 1). During periods 1 and 3 characterized by increased abundance of elephant
densities caused by the fluctuations of elephant carrion availability. We used an analysis of deviance (ANODEV) to test the significance and assess the proportion of the deviance explained by a period-dependent growth rate for hyaena and its prey (Grosbois et al., 2008).

## Results

Observed counts data and estimated population densities for prey and hyaena are provided in Figure 3.

<figure><img src="image_7.png" /></figure>

**Figure 3** – Top: Observed population densities of prey (A) and hyaena (B). Dots correspond to the densities observed at the spatial units (Voronoi polygons), each colour representing a spatial unit (95% of the data kept for visual clarity). Fluctuating thin lines represent the median of the observed densities each year. Bold line and ribbon represent the smooth of the median and its 95% confidence interval. Bottom: Estimated population densities of prey (C) and hyaena (D). Model estimates are given for the constant, time-varying, and period models. The ribbon represents the 95% of these estimates, but is very close to the estimates for the time-varying and period models. In all panels, the dotted vertical lines delimitate the four study periods; elephant pictogram indicates periods of high elephant carcass availability.

The period model best described the variations in density of prey (AICc=1862.05, 1890.89 and 1850.95 for the constant, time-varying and period models respectively) and hyaena (AICc=3039.18, 3089.02 and 3029.70 for the constant, time-varying and period models respectively). ANODEV tests were significant for both prey (F = 0.009; r² = 0.22; df= 3) and hyaena (F= 0.002; r² = 0.28; df = 3). Hyaena and prey densities showed similar trends across the 4 study periods: the two populations increased during period 1, stabilized during period 2, decreased during period 3, and increased again during period 4 (Figure 3; period models' growth rate estimates in Table 1). Differences between growth rate of hyaena and prey are similar in both periods when elephant carcasses were less abundant (0.069 in the second and 0.063 in the fourth period; Table 1). During periods 1 and 3 characterized by increased abundance of elephant
carrion in the landscape, this difference was increased or decreased (0.123 during the first and 0.004 during the third period). However, confidence intervals of the two guilds partially overlap and are large.

<table><caption>**Table 1** - Estimation of growth rate (with 95% confidence interval) from the period model for prey and hyaena populations.</caption><thead><tr><th></th><th>Prey</th><th>Hyaena</th></tr></thead><tbody><tr><td><strong>Period 1</strong><br>(1972-1986)</td><td>0.052 (0.017; 0.092)</td><td>0.175 (0.079; 0.269)</td></tr><tr><td><strong>Period 2</strong><br>(1987-1992)</td><td>-0.010 (-0.073; 0.049)</td><td>0.059 (-0.094; 0.204)</td></tr><tr><td><strong>Period 3</strong><br>(1993-2008)</td><td>-0.042 (-0.063; -0.021)</td><td>-0.058 (-0.107; -0.008)</td></tr><tr><td><strong>Period 4</strong><br>(2009-2020)</td><td>0.025 (-0.009; 0.056)</td><td>0.090 (0.013; 0.168)</td></tr></tbody></table>

Discussion

A megafauna's carcass, which represents several tons of resource, undoubtedly influences the foraging and ranging behaviour of facultative scavengers, which can use such a carrion for long periods. In marine ecosystems, sharks can use whale carrion for several days (Fallows et al., 2013). In African savannas, hyaenas definitely modify their foraging and ranging habits in the presence of an elephant carcass, which they can use for up to two weeks (Cozzi et al., 2015; Périquet et al., 2021). During this time, hyaenas are diverted from their prey. In periods of high elephant carcass availability, these carrion-induced behavioural changes should affect interactions between carnivores and their prey. Surprisingly, little is known about the cumulative long-term effects of these changes and the ultimate implication for population dynamics of predators able of facultative scavenging and their prey. Using almost 50 years of data, and a semi-natural experiment involving carrion of the largest terrestrial mammal, we do not detect patterns supporting effects of high carcass availability on the population dynamics of hyaena and its prey (i.e. we do not detect that hyaena population is less driven by its live prey in periods of high elephant carcass abundance, as we initially hypothesized). The main result is that hyaena and its prey showed the same population trends over all periods, with hyaena population dynamics following that of its prey independently of elephant carcass availability, as in classical predator-prey fluctuations (Table 1 and Figure 3; Krebs et al., 2001; Hebblewhite et al., 2002; Wang et al., 2009).

The periods contributed significantly to explain variations in prey and hyaena density (~ 30% of the deviance). Thus, management measures (elephant culling and water supply) and the resulting elephant population dynamics used to define the periods partly explained population trends. Prey abundance increased in the first and last periods, when artificial waterholes (determining the amount of resource available for herbivores) were added in the landscape, and decreased in periods 2 and 3 coinciding with an increase in the elephant population, but also to a decade of low dry season rainfall (Valeix et al., 2008). These patterns are coherent with the hypothesis that bottom-up mechanisms drive the patterns of densities we observe. Note that we did not explore other factors underlying the population trends of hyaena and its prey, such as the role of other large carnivores or climatic conditions, which was beyond the scope of this study. For example, the African lion, *Panthera leo*, which population has undergone large fluctuations, has the potential to influence herbivore populations and hyaena foraging behaviour (Grange et al., 2015; Périquet et al., 2015; Loveridge et al., 2016). Moreover, even though the hyaena population seems to decline 2-3 years after its prey population (Figure 3), we could not test for lags between their densities because periods were too short.

The main result of this study does not allow to conclude that a mechanism of hyper- or hypo-predation interfered with the population dynamics of predators and their prey on the long-term. Nonetheless some depicted patterns, although subtle, could be interpreted in the light of a role of scavenging. In period 1, both prey and hyaena population abundances increased, but this increase was
carrion in the landscape, this difference was increased or decreased (0.123 during the first and 0.004 during the third period). However, confidence intervals of the two guilds partially overlap and are large.

<table><caption>**Table 1** - Estimation of growth rate (with 95% confidence interval) from the period model for prey and hyaena populations.</caption><thead><tr><th></th><th>Prey</th><th>Hyaena</th></tr></thead><tbody><tr><td><strong>Period 1</strong><br>(1972-1986)</td><td>0.052 (0.017; 0.092)</td><td>0.175 (0.079; 0.269)</td></tr><tr><td><strong>Period 2</strong><br>(1987-1992)</td><td>-0.010 (-0.073; 0.049)</td><td>0.059 (-0.094; 0.204)</td></tr><tr><td><strong>Period 3</strong><br>(1993-2008)</td><td>-0.042 (-0.063; -0.021)</td><td>-0.058 (-0.107; -0.008)</td></tr><tr><td><strong>Period 4</strong><br>(2009-2020)</td><td>0.025 (-0.009; 0.056)</td><td>0.090 (0.013; 0.168)</td></tr></tbody></table>

Discussion

A megafauna's carcass, which represents several tons of resource, undoubtedly influences the foraging and ranging behaviour of facultative scavengers, which can use such a carrion for long periods. In marine ecosystems, sharks can use whale carrion for several days (Fallows et al., 2013). In African savannas, hyaenas definitely modify their foraging and ranging habits in the presence of an elephant carcass, which they can use for up to two weeks (Cozzi et al., 2015; Périquet et al., 2021). During this time, hyaenas are diverted from their prey. In periods of high elephant carcass availability, these carrion-induced behavioural changes should affect interactions between carnivores and their prey. Surprisingly, little is known about the cumulative long-term effects of these changes and the ultimate implication for population dynamics of predators able of facultative scavenging and their prey. Using almost 50 years of data, and a semi-natural experiment involving carrion of the largest terrestrial mammal, we do not detect patterns supporting effects of high carcass availability on the population dynamics of hyaena and its prey (i.e. we do not detect that hyaena population is less driven by its live prey in periods of high elephant carcass abundance, as we initially hypothesized). The main result is that hyaena and its prey showed the same population trends over all periods, with hyaena population dynamics following that of its prey independently of elephant carcass availability, as in classical predator-prey fluctuations (Table 1 and Figure 3; Krebs et al., 2001; Hebblewhite et al., 2002; Wang et al., 2009).

The periods contributed significantly to explain variations in prey and hyaena density (~ 30% of the deviance). Thus, management measures (elephant culling and water supply) and the resulting elephant population dynamics used to define the periods partly explained population trends. Prey abundance increased in the first and last periods, when artificial waterholes (determining the amount of resource available for herbivores) were added in the landscape, and decreased in periods 2 and 3 coinciding with an increase in the elephant population, but also to a decade of low dry season rainfall (Valeix et al., 2008). These patterns are coherent with the hypothesis that bottom-up mechanisms drive the patterns of densities we observe. Note that we did not explore other factors underlying the population trends of hyaena and its prey, such as the role of other large carnivores or climatic conditions, which was beyond the scope of this study. For example, the African lion, *Panthera leo*, which population has undergone large fluctuations, has the potential to influence herbivore populations and hyaena foraging behaviour (Grange et al., 2015; Périquet et al., 2015; Loveridge et al., 2016). Moreover, even though the hyaena population seems to decline 2-3 years after its prey population (Figure 3), we could not test for lags between their densities because periods were too short.

The main result of this study does not allow to conclude that a mechanism of hyper- or hypo-predation interfered with the population dynamics of predators and their prey on the long-term. Nonetheless some depicted patterns, although subtle, could be interpreted in the light of a role of scavenging. In period 1, both prey and hyaena population abundances increased, but this increase was
stronger for hyaena (Table 1), which could be explained by hyaenas benefiting from both the increase in prey populations and the additional energy-rich resources provided by elephant culling. In period 2, growth rate confidence intervals are large and encompass 0 for both prey and hyaenas (Table 1), showing a general stabilization for the two populations. However, estimated growth rates suggest that prey abundance tended to decrease while hyaena abundance continued increasing in period 2, which is consistent with a scenario whereby the hyaena population, then abundant, compensated for the loss of the energy-rich resources provided by elephant carrion in period 1 by a renewed predation pressure in period 2. In period 3, hyaena population abundance did not decrease at a rate lower than its prey population, but maintains a higher density than at the start of period 1 despite a strong decrease of prey density, which could be explained by the presence of the additional food source provided by elephant carcasses. Finally, during period 4, both hyaena and prey densities increased, but the rate of increase is slightly lower for prey than hyaena, possibly indicating again a renewed predation of hyaena on prey populations. Differences between estimated growth rates of each guild are also potentially suggesting a change in the link between hyaena and prey densities caused by elephant carrion abundance : the differences are similar in periods of lower abundance of elephant carrion (about 0.06), but higher and lower in periods of high elephant carrion abundance. Testing these hypotheses would require further investigations in the future, by comparing hyaena diet and hunting behaviour in drought years (when elephant mortality will be high) and in years with good annual rainfall (when elephant mortality will be low).

Overall, these patterns are indicative of a numerical response of hyaena to the increased elephant carcass availability, through a rapid increase in density in period 1 and the maintenance of densities higher than at the start of our study period in period 3. If they are not hunted, mammalian predators' density are generally associated with prey biomass (Fuller & Sievert, 2001; Hayward et al., 2007) and this was verified for hyaena in Kruger National Park, South Africa (Ferreira & Funston, 2016). Thus, a large increase of exploitable resources though inputs of additional food sources such as mega-carcasses should allow the survival and reproduction of more individuals; in other words, results in an increased carrying capacity (Oro et al., 2013). Contrary to our study, in Manitoba (Canada), the increased arctic fox (*Vulpes lagopus*) abundance after increased scavenging on carcasses of polar bear (*Usrus maritimus*) kills was accompanied by a signal in the population dynamics of its usual main prey, the collared lemming (*Dicrostonyx richardsoni*) (Roth, 2003). The classical cyclic dynamics of lemming was delayed, probably due to an increased predation pressure arising from the higher predator abundance, which is akin to a phenomenon of hyper-predation (Courchamp et al., 2000; Moleón et al., 2014). However, arctic and African savannas ecosystems differ fundamentally. While arctic predator-prey communities are sometimes referred as the simplest in the world and present few predators that all rely largely upon one main prey, lemmings (*Dycrostony spp.*, *Lemmus spp.* ; Gilg et al., 2006; Reiter & Andersen, 2011), African savannas present a high diversity of prey and predators in which classical prey and predator couplings are not observed (Sinclair et al., 2003). In arctic ecosystems, arctic fox only shifts to other prey when lemmings become very scarce (Gilg et al., 2006), which makes direct and observable repercussion of changes in their populations dynamics on that of lemmings more likely. In contrast, hyaena is known to be a generalist predator with a very plastic feeding behaviour, selecting food sources depending on their abundance (Kruuk, 1972; Périquet et al., 2015). Consequently, if hyaena predation starts to reduce the abundance of one prey, it is likely that the predator shifts easily to another type of prey, allowing the other species to recover.

Furthermore, carrion can be considered as pulsed resources whose characteristics can affect the consumer dynamics (Yang et al., 2008). Interestingly, the spatio-temporal predictability of elephant carrion pulses differed between periods 1 and 3. During period 3, they were predictable and limited in time and space, as elephants die naturally near waterholes at the end of the dry season (Conybeare & Haynes, 1984). From a hyaena perspective, a cursorial hunter, the end of the dry season is the time of the year when prey are the weakest and hence easier to catch (Cozzi et al., 2015). Consequently, mortality is
stronger for hyaena (Table 1), which could be explained by hyaenas benefiting from both the increase in prey populations and the additional energy-rich resources provided by elephant culling. In period 2, growth rate confidence intervals are large and encompass 0 for both prey and hyaenas (Table 1), showing a general stabilization for the two populations. However, estimated growth rates suggest that prey abundance tended to decrease while hyaena abundance continued increasing in period 2, which is consistent with a scenario whereby the hyaena population, then abundant, compensated for the loss of the energy-rich resources provided by elephant carrion in period 1 by a renewed predation pressure in period 2. In period 3, hyaena population abundance did not decrease at a rate lower than its prey population, but maintains a higher density than at the start of period 1 despite a strong decrease of prey density, which could be explained by the presence of the additional food source provided by elephant carcasses. Finally, during period 4, both hyaena and prey densities increased, but the rate of increase is slightly lower for prey than hyaena, possibly indicating again a renewed predation of hyaena on prey populations. Differences between estimated growth rates of each guild are also potentially suggesting a change in the link between hyaena and prey densities caused by elephant carrion abundance : the differences are similar in periods of lower abundance of elephant carrion (about 0.06), but higher and lower in periods of high elephant carrion abundance. Testing these hypotheses would require further investigations in the future, by comparing hyaena diet and hunting behaviour in drought years (when elephant mortality will be high) and in years with good annual rainfall (when elephant mortality will be low).

Overall, these patterns are indicative of a numerical response of hyaena to the increased elephant carcass availability, through a rapid increase in density in period 1 and the maintenance of densities higher than at the start of our study period in period 3. If they are not hunted, mammalian predators' density are generally associated with prey biomass (Fuller & Sievert, 2001; Hayward et al., 2007) and this was verified for hyaena in Kruger National Park, South Africa (Ferreira & Funston, 2016). Thus, a large increase of exploitable resources though inputs of additional food sources such as mega-carcasses should allow the survival and reproduction of more individuals; in other words, results in an increased carrying capacity (Oro et al., 2013). Contrary to our study, in Manitoba (Canada), the increased arctic fox (*Vulpes lagopus*) abundance after increased scavenging on carcasses of polar bear (*Usrus maritimus*) kills was accompanied by a signal in the population dynamics of its usual main prey, the collared lemming (*Dicrostonyx richardsoni*) (Roth, 2003). The classical cyclic dynamics of lemming was delayed, probably due to an increased predation pressure arising from the higher predator abundance, which is akin to a phenomenon of hyper-predation (Courchamp et al., 2000; Moleón et al., 2014). However, arctic and African savannas ecosystems differ fundamentally. While arctic predator-prey communities are sometimes referred as the simplest in the world and present few predators that all rely largely upon one main prey, lemmings (*Dycrostony spp.*, *Lemmus spp.* ; Gilg et al., 2006; Reiter & Andersen, 2011), African savannas present a high diversity of prey and predators in which classical prey and predator couplings are not observed (Sinclair et al., 2003). In arctic ecosystems, arctic fox only shifts to other prey when lemmings become very scarce (Gilg et al., 2006), which makes direct and observable repercussion of changes in their populations dynamics on that of lemmings more likely. In contrast, hyaena is known to be a generalist predator with a very plastic feeding behaviour, selecting food sources depending on their abundance (Kruuk, 1972; Périquet et al., 2015). Consequently, if hyaena predation starts to reduce the abundance of one prey, it is likely that the predator shifts easily to another type of prey, allowing the other species to recover.

Furthermore, carrion can be considered as pulsed resources whose characteristics can affect the consumer dynamics (Yang et al., 2008). Interestingly, the spatio-temporal predictability of elephant carrion pulses differed between periods 1 and 3. During period 3, they were predictable and limited in time and space, as elephants die naturally near waterholes at the end of the dry season (Conybeare & Haynes, 1984). From a hyaena perspective, a cursorial hunter, the end of the dry season is the time of the year when prey are the weakest and hence easier to catch (Cozzi et al., 2015). Consequently, mortality is
likely to be compensatory (meaning that individuals killed by hyaena were weak and likely to have died from other causes, such that population's total mortality remained unchanged), and resource pulses at the end of the dry season may not matter that much. In contrast, during period 1, carcasses were highly aggregated (because entire elephant families were removed through culling) and unpredictable in space and time (Cumming, 1981). They were more likely to influence hyaena fitness and hence demography with later consequences on prey demography. However, the unpredictability and short duration of carrion availability through these measures may explain the lack of strong and observable impacts on population dynamics on the long term as we observed here.

The frequency and intensity of pulses of carrion are likely to be altered under global changes and human activities. In large terrestrial mammals, peaks of herbivore carcasses are mostly associated with extreme weather periods, such as droughts or severe winters, which should become more frequent in the future (Intergovernmental Panel on Climate Change, 2023). Moreover, anthropogenic carcasses become increasingly present (Moreno-Opo & Margalida, 2019) through hunting activities (Mateo-Tomás et al., 2015), poaching (Underwood et al., 2013) or increasing number of domestic herbivores (Arrondo et al., 2019). Understanding how the characteristics of pulses of large herbivore carcasses may affect the coupling between large carnivores and their live prey populations is key if we want to predict the future of these populations.

## Acknowledgements

We are indebted to the Wildlife Environment Zimbabwe and thank warmly the Zimbabwe Parks and Wildlife Management Authority for their dedicated effort in monitoring wildlife in Zimbabwe in general and in Hwange National Park in particular and for providing this invaluable long-term dataset that spans over 50 years. We also thank Arnold Tshipa, Makalolo Camp and Wilderness for providing their rainfall data for this study. This manuscript benefited from comments from Eli Strauss and one anonymous reviewer. Preprint version 2 of this article has been peer-reviewed and recommended by Peer Community In Ecology (https://doi.org/10.24072/pci.ecology.100594; Sebastián González, 2024).

## Funding

The authors declare that they have received no specific funding for this study.

## Conflict of interest disclosure

The authors declare that they comply with the PCI rule of having no financial conflicts of interest in relation to the content of the article.

## Data, scripts, code, and supplementary information availability

Data, scripts and code are available online: https://doi.org/10.5281/zenodo.10086271 (Sidous, 2023).
likely to be compensatory (meaning that individuals killed by hyaena were weak and likely to have died from other causes, such that population's total mortality remained unchanged), and resource pulses at the end of the dry season may not matter that much. In contrast, during period 1, carcasses were highly aggregated (because entire elephant families were removed through culling) and unpredictable in space and time (Cumming, 1981). They were more likely to influence hyaena fitness and hence demography with later consequences on prey demography. However, the unpredictability and short duration of carrion availability through these measures may explain the lack of strong and observable impacts on population dynamics on the long term as we observed here.

The frequency and intensity of pulses of carrion are likely to be altered under global changes and human activities. In large terrestrial mammals, peaks of herbivore carcasses are mostly associated with extreme weather periods, such as droughts or severe winters, which should become more frequent in the future (Intergovernmental Panel on Climate Change, 2023). Moreover, anthropogenic carcasses become increasingly present (Moreno-Opo & Margalida, 2019) through hunting activities (Mateo-Tomás et al., 2015), poaching (Underwood et al., 2013) or increasing number of domestic herbivores (Arrondo et al., 2019). Understanding how the characteristics of pulses of large herbivore carcasses may affect the coupling between large carnivores and their live prey populations is key if we want to predict the future of these populations.

## Acknowledgements

We are indebted to the Wildlife Environment Zimbabwe and thank warmly the Zimbabwe Parks and Wildlife Management Authority for their dedicated effort in monitoring wildlife in Zimbabwe in general and in Hwange National Park in particular and for providing this invaluable long-term dataset that spans over 50 years. We also thank Arnold Tshipa, Makalolo Camp and Wilderness for providing their rainfall data for this study. This manuscript benefited from comments from Eli Strauss and one anonymous reviewer. Preprint version 2 of this article has been peer-reviewed and recommended by Peer Community In Ecology (https://doi.org/10.24072/pci.ecology.100594; Sebastián González, 2024).

## Funding

The authors declare that they have received no specific funding for this study.

## Conflict of interest disclosure

The authors declare that they comply with the PCI rule of having no financial conflicts of interest in relation to the content of the article.

## Data, scripts, code, and supplementary information availability

Data, scripts and code are available online: https://doi.org/10.5281/zenodo.10086271 (Sidous, 2023).
Appendices

<figure><img src="image_7.png" /></figure>

**Appendix 1** - Number of waterholes holding water at the end of the dry season, and hence monitored, in Hwange National Park (top) and in the study area (bottom) from 1972 to 2020. Dotted lines delimitate the different study periods: those with an elephant pictogram are the periods with a high abundance of elephant carcasses in the landscape (periods 1 and 3).
Appendices

<figure><img src="image_8.png" /></figure>

**Appendix 1** - Number of waterholes holding water at the end of the dry season, and hence monitored, in Hwange National Park (top) and in the study area (bottom) from 1972 to 2020. Dotted lines delimitate the different study periods: those with an elephant pictogram are the periods with a high abundance of elephant carcasses in the landscape (periods 1 and 3).
<figure><img src="image_8.png" /></figure>

**Appendix 2** - Rainfall information. A) Annual rainfall in the study area. Data were collected from two meteorological stations: Main Camp until 2006 and Makalolo from 2007 to 2020. Annual rainfall for year y is calculated as the sum of the rainfall from October of year y-1 to September of year y. B) Density estimates of a constant model without annual rainfall as a covariate of the observation process. When annual rainfall is not considered, density estimates are overestimated in dry years, such as 2020, because animals need to visit waterholes more often to drink compared to wet years, such as 1974, for which density estimates are underestimated because animal need to visit waterholes less since natural ponds that allow them to drink are numerous and found throughout the park. C) AICc values of models with and without annual rainfall as a covariate of the observation process. AICc are lower for models with annual rainfall than models without, except for the time-varying model. This could be due to an over parametrization because time varying models present 54 parameters when annual rainfall is added and 53 without annual rainfall as a covariate. D) Density estimates of a constant model with annual rainfall as a covariate of the observation process. Density estimates are corrected: they are lower in dry years and higher in wet years, compared to the model without covariate in panel B. For all panels, the dotted vertical lines delimitate the different study periods: periods with an elephant pictogram are the ones with a high abundance of elephant carrion in the landscape (periods 1 and 3).

References

Abrams PA (2000) The evolution of predator-prey interactions: Theory and evidence. *Annual Review of Ecology and Systematics*, **31**, 79–105. https://doi.org/10.1146/annurev.ecolsys.31.1.79

Andrén H, Persson J, Mattisson J, Danell AC (2011) Modelling the combined effect of an obligate predator and a facultative predator on a common prey: lynx *Lynx lynx* and wolverine *Gulo gulo* predation on reindeer *Rangifer tarandus*. *Wildlife Biology*, **17**, 33–43. https://doi.org/10.2981/10-065

Arrondo E, Morales-Reyes Z, Moleón M, Cortés-Avizanda A, Donázar JA, Sánchez-Zapata JA (2019) Rewilding traditional grazing areas affects scavenger assemblages and carcass consumption patterns. *Basic and Applied Ecology*, **41**, 56–66. https://doi.org/10.1016/j.baae.2019.10.006
<figure><img src="image_9.png" /><figcaption>C</figcaption></figure>

<table><thead><tr><th>Species</th><th>Model</th><th>AICc without rainfall</th><th>AICc with rainfall</th></tr></thead><tbody><tr><td rowspan="3">Prey</td><td>Constant</td><td>1892.00</td><td>1862.05</td></tr><tr><td>Time Varying</td><td>1888.49</td><td>1890.89</td></tr><tr><td>Period</td><td>1897.15</td><td>1850.05</td></tr><tr><td rowspan="3">Hyaena</td><td>Constant</td><td>3046.43</td><td>3039.18</td></tr><tr><td>Time Varying</td><td>3086.64</td><td>3089.02</td></tr><tr><td>Period</td><td>3037.43</td><td>3029.70</td></tr></tbody></table>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

**Appendix 2** - Rainfall information. A) Annual rainfall in the study area. Data were collected from two meteorological stations: Main Camp until 2006 and Makalolo from 2007 to 2020. Annual rainfall for year y is calculated as the sum of the rainfall from October of year y-1 to September of year y. B) Density estimates of a constant model without annual rainfall as a covariate of the observation process. When annual rainfall is not considered, density estimates are overestimated in dry years, such as 2020, because animals need to visit waterholes more often to drink compared to wet years, such as 1974, for which density estimates are underestimated because animal need to visit waterholes less since natural ponds that allow them to drink are numerous and found throughout the park. C) AICc values of models with and without annual rainfall as a covariate of the observation process. AICc are lower for models with annual rainfall than models without, except for the time-varying model. This could be due to an over parametrization because time varying models present 54 parameters when annual rainfall is added and 53 without annual rainfall as a covariate. D) Density estimates of a constant model with annual rainfall as a covariate of the observation process. Density estimates are corrected: they are lower in dry years and higher in wet years, compared to the model without covariate in panel B. For all panels, the dotted vertical lines delimitate the different study periods: periods with an elephant pictogram are the ones with a high abundance of elephant carrion in the landscape (periods 1 and 3).

References

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Andrén H, Persson J, Mattisson J, Danell AC (2011) Modelling the combined effect of an obligate predator and a facultative predator on a common prey: lynx *Lynx lynx* and wolverine *Gulo gulo* predation on reindeer *Rangifer tarandus*. *Wildlife Biology*, **17**, 33–43. https://doi.org/10.2981/10-065

Arrondo E, Morales-Reyes Z, Moleón M, Cortés-Avizanda A, Donázar JA, Sánchez-Zapata JA (2019) Rewilding traditional grazing areas affects scavenger assemblages and carcass consumption patterns. *Basic and Applied Ecology*, **41**, 56–66. https://doi.org/10.1016/j.baae.2019.10.006
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Gilg O, Sittler B, Sabard B, Hurstel A, Sané R, Delattre P, Hanski I (2006) Functional and numerical responses of four lemming predators in high arctic Greenland. *Oikos*, **113**, 193–216. https://doi.org/10.1111/j.2006.0030-1299.14125.x

Gomo G, Mattisson J, Hagen BR, Moa PF, Willebrand T (2017) Scavenging on a pulsed resource: quality matters for corvids but density for mammals. *BMC Ecology*, **17**, 22. https://doi.org/10.1186/s12898-017-0132-1

Grange S, Barnier F, Duncan P, Gaillard J-M, Valeix M, Ncube H, Périquet S, Fritz H (2015) Demography of plains zebras (*Equus quagga*) under heavy predation. *Population Ecology*, **57**, 201–214. https://doi.org/10.1007/s10144-014-0469-7
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Burnham KP, Anderson DR, Huyvaert KP (2011) AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons. *Behavioral Ecology and Sociobiology*, **65**, 23–35. https://doi.org/10.1007/s00265-010-1029-6

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Child G (2004) Elephant culling in Zimbabwe. *ZimConservation Opinion*, **1**.

Conybeare A, Haynes G (1984) Observations on elephant mortality and bones in water holes. *Quaternary Research*, **22**, 189–200. https://doi.org/10.1016/0033-5894(84)90039-5

Cortés-Avizanda A, Carrete M, Serrano D, Donázar J (2009a) Carcasses increase the probability of predation of ground-nesting birds: A caveat regarding the conservation value of vulture restaurants. *Animal Conservation*, **12**, 85–88. https://doi.org/10.1111/j.1469-1795.2008.00231.x

Cortés-Avizanda A, Selva N, Carrete M, Donázar J (2009b) Effects of carrion resources on herbivore spatial distribution are mediated by facultative scavengers. *Basic and Applied Ecology*, **10**, 265–272. https://doi.org/10.1016/j.baae.2008.03.009

Courchamp F, Langlais M, Sugihara G (2000) Rabbits killing birds: modelling the hyperpredation process. *Journal of Animal Ecology*, **69**, 154–164. https://doi.org/10.1046/j.1365-2656.2000.00383.x

Cozzi G, Börger L, Hutter P, Abegg D, Beran C, McNutt JW, Ozgul A (2015) Effects of Trophy Hunting Leftovers on the Ranging Behaviour of Large Carnivores: A Case Study on Spotted Hyenas. *PLOS ONE*, **10**, e0121471. https://doi.org/10.1371/journal.pone.0121471

Cumming D (1981) The management of elephant and other large mammals in Zimbabwe. In: *Problems in Management of Locally Abundant Wild Mammals*, pp. 91–118. Academic Press. https://doi.org/10.1016/B978-0-12-385280-9.50012-3

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Dudley JP, Criag GC, Gibson DStC, Haynes G, Klimowicz J (2001) Drought mortality of bush elephants in Hwange National Park, Zimbabwe. *African Journal of Ecology*, **39**, 187–194. https://doi.org/10.1046/j.0141-6707.2000.00297.x

Fallows C, Gallagher AJ, Hammerschlag N (2013) White Sharks (Carcharodon carcharias) Scavenging on Whales and Its Potential Role in Further Shaping the Ecology of an Apex Predator. *PLoS ONE*, **8**, e60797. https://doi.org/10.1371/journal.pone.0060797

Ferreira SM, Funston PJ (2016) Population estimates of spotted hyaenas in the Kruger National Park, South Africa. *African Journal of Wildlife Research*, **46**, 61–70. https://doi.org/10.3957/056.046.0061

Fuller T, Sievert P (2001) Carnivore demography and the consequences of changes in prey availability. In: *Carnivore Conservation*, pp. 163–178. Cambridge University Press.

Gilg O, Sittler B, Sabard B, Hurstel A, Sané R, Delattre P, Hanski I (2006) Functional and numerical responses of four lemming predators in high arctic Greenland. *Oikos*, **113**, 193–216. https://doi.org/10.1111/j.2006.0030-1299.14125.x

Gomo G, Mattisson J, Hagen BR, Moa PF, Willebrand T (2017) Scavenging on a pulsed resource: quality matters for corvids but density for mammals. *BMC Ecology*, **17**, 22. https://doi.org/10.1186/s12898-017-0132-1

Grange S, Barnier F, Duncan P, Gaillard J-M, Valeix M, Ncube H, Périquet S, Fritz H (2015) Demography of plains zebras (*Equus quagga*) under heavy predation. *Population Ecology*, **57**, 201–214. https://doi.org/10.1007/s10144-014-0469-7
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