In contrast, after PDBu exposure, MTs often extended deep into the P-domain and occasionally reached the leading edge of the growth cone.

This extension resulted from an increase in MT growth lifetimes as shown in the MT growth lifetime histo- grams in Fig. 7, H and I, under control conditions and after PDBu treatment.

Note the presence of MT growth life- times in excess of 80 s in PDBu (Fig 7 I) and the nearly twofold increase in mean MT growth lifetime (Fig. 8 A).

An example of a prolonged MT growth lifetime in PDBu is shown in Fig. 7 F.


The increase in growth lifetimes in PDBu was accompanied by a 1.7-fold increase in the MT rescue frequency and an approximately twofold decrease in catastrophe frequency (Table I).

Rates of MT growth and shortening increased slightly after PDBu treatment (Table I), but these changes were not statistically signifi- cant.

Anterograde and retrograde translocation rates re- mained unchanged (Table I).


Interestingly, after PDBu treatment, MTs penetrating the P-domain sometimes underwent prolonged periods of retrograde translocation, apparently in conjunction with retrograde F-actin flow.

An example of this phenomenon is illustrated in Fig. 7 G, where a MT continuously translo- cates in the retrograde direction (red marks) while still growing at a rate that matched or exceeded the rate of ret- rograde transport.

This resulted in the distal tip of the MT maintaining its position near the leading edge despite the opposing retrograde F-actin flow.

To analyze the contribu- tion of MT translocation in the observed effects, the per- centage of time MTs spent moving in the anterograde or retrograde direction or in a nonmoving state was assessed under control conditions and after PDBu treatment.

We found MTs spent significantly more time undergoing ret- rograde movement after PDBu treatment and a propor- tional decrease in the amount of time MTs were not mov- ing (Fig. 8 B).

It should be emphasized that net MT extension occurred despite the increase in retrograde MT


The effects of PDBu on MTs appear to be specifically due to PKC-regulated events since multiple activators such as PDBu, TPA, or OAG had similar MT assembly-pro- moting effects that could be completely blocked by spe- cific PKC inhibitors and not observed with the inactive PDBu analogue, 4-␣-phorbol.

Although phorbol esters are known to cause activation of proteins other than PKC, such as chimaerin, Munc 13-1, Unc13, or Ras-GRP (Ron and Kazanietz, 1999), none of these proteins are kinases and therefore their activity should not be sensitive to ATP site-active PKC inhibitors such as Bis 1 used in this study.


In contrast to activation, inhibition of PKC with Bis or re- lated compounds was surprisingly without observable ef- fects.

This could indicate that a minimal level of basal PKC activity is required for the maintenance of GC structure under the conditions employed here.

This finding appar- ently contradicts the "growth-cone collapse" due to PKC inhibition after introduction of protein kinase C pseu- dosubstrate peptides reported earlier (Theodore et al., 1995).

Indeed, we observed effects similar to "growth- cone collapse" when we tested inhibitors such as chel- erythrine or calphostin C that interact with regions of PKC other than the C3 (ATP binding) domain (our unpub- lished observations).

Caution should be exercised, how- ever, in interpreting effects of these agents on growth cone structure, given that chelerythrine is reported to have di- rect effects on MT polymerization, and calphostin C has unpredictable phototoxic effects due to light-stimulated oxidative damage to PKC (Gopalakrishna et al., 1992; Wolff and Knipling, 1993).

It has also been suggested that the regulatory domain of PKC, especially the pseudosub- strate, the C1 domain, and part of the V3 region, may be necessary and sufficient for sustaining neurite outgrowth (Zeidman et al., 1999).

These observations are consistent with the aforementioned disruption of growth-cone struc- ture after introduction of excessive pseudosubstrate pep- tide into growth cones (Theodore et al., 1995).

The differ- ence in the effects of ATP- and pseudosubstrate-based inhibitors may lie in the fact that addition of pseudosub- strate may increase availability of the regulatory domain for interactions while inhibiting the ATP transfer function of the kinase, whereas ATP-based inhibitors will only in- hibit ATP transfer without affecting availability of the reg- ulatory domain.


It should be noted that our culture condi- tions (i.e., serum-free defined medium and poly-L-lysine substrate) minimize cross-talk between cell adhesion mol- ecule and growth factor signaling pathways, which could have a significant effect upon the ultimate response of the neurons to PKC activation.

These factors should be kept in mind when comparing the responses reported here to those observed in more complex systems.


Previous studies on the effects of nocodazole in cells have demonstrated that concentrations in the 4-400-nM range inhibit MT dynamics without resulting in significant loss of MT polymer mass (Rochlin et al., 1996; Jordan and Wil- son, 1998b; Jordan and Wilson, 1999).

In the current study, similar treatments with extremely low doses of nocodazole and other inhibitors that interfere with MT polymerization potently inhibited PDBu-stimulated MT extension (Fig. 5), indicating that MT assembly is necessary for the effects reported here.

Furthermore, our biochemical experiments showed an increase in MT polymer mass after PDBu treat- ment, and this MT polymer was also sensitive to depoly- merization by low doses of nocodazole (Fig. 6).

In support of the current findings in neurons, PDBu has also been re- ported to increase MT polymerization in macrophages (Robinson and Vandre, 1995).


To directly address the role of MT assembly, we mea- sured MT dynamics using the FSM MT marking tech- nique.

This is a powerful new technique pioneered by Waterman-Storer and Salmon (1998) for investigating cy- toskeletal protein dynamics in living cells, which enables independent assessment of the contribution of polymer as- 1042 sembly dynamics and polymer translocation events.


FSM analysis revealed that MT polymerization is indeed af- fected by PDBu treatment; specifically, the duration of typical MT assembly events were increased approximately twofold (Fig. 7, H and I).

This increase in MT growth life- times was accompanied by a 1.7-fold decrease in catastro- phe and approximately twofold increase in rescue fre- quencies (Table I).

It should be noted that control rescue frequencies may be underestimated due to the difficulty in detecting rescue events occurring in relatively short MTs near the C-T-domain interface, where MT density is high (see Tanaka and Kirschner, 1991).


To investigate this is- sue, a subpopulation of long MTs with ends already in the P-domain under control conditions were analyzed (Table I).

In this population, the catastrophe frequency still de- ceased 1.7-fold in PDBu relative to controls; however, the rescue frequencies increased only -3.1فfold.

This smaller PDBu effect on peripheral (i.e., long) versus total MTs might result from inefficient detection of rescue events when assessed in the total MT population; however, we cannot rule out differences in MT behavior in the transi- tion zone versus P-domain.

These fundamental domain- specific issues are currently under investigation.


Finally, actual rates of MT growth and shortening were not af- fected by PDBu (Table I), making changes in tubulin dimer concentration an unlikely mechanism for the ob- served MT advance.


Changes in rates of MT translocation are clearly not in- volved since retrograde and anterograde sliding rates were unaffected (Table I).

In fact, the proportion of time MTs spent undergoing translocation in the retrograde direction was actually increased in PDBu (36% vs. 49% for control vs. PDBu, respectively) despite net MT advance.

How can this paradox be resolved?


Retrograde F-actin flow appears to act as a barrier to MT advance (Forscher and Smith, 1988), and retrograde translocation of MTs in peripheral F-actin domains has been described in nonneuronal cells (Waterman-Storer and Salmon, 1997).


The current study supports these findings and suggests that when MTs extend into the P-domain, they do so predominantly by exhibiting prolonged bursts of assembly that exceed the rate of F-actin flow (e.g., Fig. 7 F).

After extension, a MT can maintain its position in the P-domain by assembling at a rate that matches retrograde flow (e.g., Fig. 7 G); however, if MT assembly stops or slows below the rate of retrograde flow, the MT will be translocated rearward out of the P-domain.


After PDBu treatment, the increased length of MTs within the P-domain promotes chances for MT-actin filament in- teractions, and this in turn may result in the increase in ret- rograde translocation events observed (Fig. 8 B).

This scenario is consistent with the effects observed after stabi- lizing MTs with taxol; specifically, MTs were cleared from both the peripheral domain and transition zone, presum- ably via interactions with retrograde flow (Fig. 5 H).


This explanation suggests a role for the actomyosin-based pro- cess (Lin et al., 1996) of retrograde F-actin flow in clearing MTs from the P-domain; however, it does not rule out par- ticipation of other molecular motors in the variety of MT translocation events observed (a detailed analysis of MT behavior in this system will be published separately).


Whether PDBu-stimulated MT advance results from di- rect effects on the MTs themselves, indirectly from alter- ation of F-actin structure, or from changes in MT-F-actin interactions remains to be determined.

PDBu treatment does in fact affect F-actin structure; for example, in the in- hibition of actin-based ruffling seen in Fig. 2 B. Several lines of evidence, however, suggest that the structure of the P-domain cytomatrix remains largely intact after PDBu treatment.

First, the large (100-150-nm diameter) organelles typical of the C-domain do not significantly ad- vance into the P-domain in PDBu.

This is in contrast to treatment with cytochalasin, which disrupts peripheral F-actin and results in organelle advance into the P-domain (Forscher and Smith, 1988).

Second, previous studies sug- gest that the P-domain pore size or gel exclusion limit is 70-100 nM (Popov and Poo, 1992), which is considerably larger than the -52فnm diameter of a single MT, indicat- ing that individual MTs would not be excluded from the P-domain simply on the basis of cross-sectional area alone.

In agreement with this, we found that 2,000,000 kD FITC- dextran (-06فnm stokes radius) gained diffusional access into the P-domain and, furthermore, the distribution of this probe did not appear to change with PDBu treatment (our unpublished observations).

Taken together, these ob- servations suggest that organelles are simply sterically ex- cluded from the peripheral domain by an F-actin-based barrier and the mechanism for MT exclusion from the P-domain is more complex.


What potential effector proteins might be involved?

Re- cent evidence suggests the four parameters of MT dynam- ics (growth, shortening, catastrophe frequency, and rescue frequency) can be altered independently of one another in vivo (Desai and Mitchison, 1997; Andersen, 2000), so the situation is potentially complex.

With that said, the current data does point to a mechanism involving modulation of the frequency of MT catastrophe and/or rescue events by PKC.


Potential candidates are proteins that regulate MT catastrophe frequency such as Op18/stathmin (Belmont and Mitchison, 1996; Di Paolo et al., 1997), kinesin family members KIN 1 (Desai et al., 1999) and XKCM1 (Wal- czak et al., 1996; Tournebize et al., 2000), or XMAP310, a MT rescue-promoting factor (Andersen and Karsenti, 1997).


On the other hand, phosphorylation of MAPs by PKC is well known to promote their dissociation from MTs, MT unbundling, and decreased MT stability (Mori et al., 1991; Gordon-Weeks, 1993; Goold et al., 1999), all of which are consistent with the results presented here.

These lines of inquiry deserve further attention.


Previous analysis of PKC's regulation of neurite out- growth have focused primarily on changes in F-actin struc- ture and actin polymerization dynamics.

Our findings sug- gest an additional mechanism for regulating the overall rate of axon growth through modulation of MT dynamics.

PKC activation results in a net increase in MT polymeriza- tion via a pronounced increase in MT growth lifetimes, thus promoting MT extension into the P-domain.

MT in- vasion of the P-domain could contribute to increased rates of neurite outgrowth we and many others have observed with chronic PKC activation (Hsu et al., 1989; Hundle et al., 1995; Fagerstrom et al., 1996).

Interestingly, recent evi- dence suggests that dynamic MTs are necessary for deliv- ery and incorporation of new membranes into the distal 1043 axon (Zakharenko and Popov, 1998).

Thus, PKC activa- tion, by promoting MT growth, could also facilitate tar- geted delivery of membrane components.


This work was supported by a National Institutes of Health (NIH) grant RO1-NS28695 to P. Forscher and a Postdoctoral NIH National Research Service Award (1F32NS11122) to A.W. Schaefer.


Interactions between microtubules (MTs) and filamentous actin (f-actin) are involved in directed cell locomotion, but are poorly understood.

To test the hypothesis that MTs and f-actin associate with one another and affect each other’s organization and dynamics, we performed time-lapse dual-wavelength spinning-disk confocal fluorescent speckle microscopy (FSM) of MTs and f-actin in migrating newt lung epithelial cells.

F-actin exhibited four zones of dynamic behavior: rapid retrograde flow in the lamellipodium, slow retrograde flow in the lamellum, anterograde flow in the cell body, and no movement in the convergence zone between the lamellum and cell body.

Speckle analysis showed that MTs moved at the same trajectory and velocity as f-actin in the cell body and lamellum, but not in the lamellipodium or convergence zone.

MTs grew along f-actin bundles, and quiescent MT ends moved in association with f-actin bundles.

These results show that the movement and organization of f-actin has a profound effect on the dynamic organization of MTs in migrating cells, and suggest that MTs and f-actin bind to one another in vivo.


The directed movement of cells is thought to result from complex and dynamic interactions between the microtubule (MT) and filamentous actin (f-actin) cytoskeletal systems [1].

Migrating cells have a polarized morphology, with a ruffling lamellipodium at the leading edge facing the direction of migration, followed by a flat lamellum, a tapering cell body, and a trailing tail.

This polarity results from highly polarized organization and assembly/disassembly dynamics of f-actin.

The lamellipodium contains a dense meshwork of actin filaments whose barbed ends continuously grow near the leading edge.

F-actin in both the lamellipodium and lamellum undergoes retrograde movement towards the cell center, which may mediate cellular traction [2][3][4].


In the cell body and tail, f-actin at the ventral cell surface forms bundles that may be contractile and mediate retraction of the cell rear [5].

MTs, like f-actin, exhibit polarized organization and dynamics in migrating cells [1].

MT plus ends grow out from the cell center primarily towards the leading edge where they undergo dynamic instability, i.e., stochastic switching between growth and shortening.

There is evidence that MT movements may mimic those of f-actin, undergoing retrograde flow in the lamellum and forward motion in the cell body and tail [6][7][8].


How MTs and actin interact to produce directed cell motility is unclear.

MTs may provide spatial cues to localize f-actin–based protrusion and contraction [1].

One way this could be achieved is by physical interaction between the two filament systems.

In this study, we tested the hypothesis that MTs and f-actin interact with one another in living, migrating cells to affect each other’s dynamic organization by using time-lapse dual-wavelength spinning-disk confocal fluorescent speckle microscopy (FSM) [9][10] to near simultaneously visualize f-actin and MT dynamic behaviors in migrating newt lung epithelial cells.

This analysis reveals for the first time that MTs exhibit interactions with f-actin bundles and meshworks that guide the movement and organization of MTs during cell motility.


Results and discussion


F-actin exhibits four distinct zones of dynamic behavior


We concentrated our analysis of migrating newt lung epithelial cells on the cytoskeleton at the ventral cell surface because the f-actin in this region likely mediates the traction forces of motility.

To support this notion, we generated deconvolved three-dimensional reconstructions of fixed cells labeled with fluorescent phalloidin.

In X–Z views, f-actin in the lamellipodium and distal lamella fills an area the same thickness or thinner than our confocal optical section thickness of 530 nm.

In the proximal lamella and cell body, f-actin is concentrated solely in cortical regions of similar thickness, with little detectable actin in the central cell body.

This indicates that our confocal images contain the full population of ventral f-actin, and that in the cell center there is likely little interaction between dorsal and ventral f-actin.


For confocal FSM imaging, we injected cells with 0.5 mg/ml of X-rhodamine actin.

Random incorporation of a few labeled and many endogenous unlabeled monomers during filament assembly gives f-actin structures a fluorescent speckled appearance in diffraction-limited images [9][11].

The speckles act as fiduciary marks on the filaments; their movement reports polymer movement, whereas their appearance/disappearance report polymer assembly/disassembly.


To relate speckle dynamics to the f-actin structures seen in phalloidin-stained cells, we collected confocal FSM images at 10-s intervals followed by perfusion of fixative and staining with Alexa-488 phalloidin.

This showed that areas with somewhat even distributions of fluorescent speckles in FSM images corresponded to f-actin meshworks or fine f-actin bundles.

Linear arrays of f-actin speckles that moved together coherently in time-lapse FSM corresponded to bundles of f-actin.

There was a minor subset of f-actin bundles in the phalloidin image that had no correspondence in the FSM image, indicating their lack of incorporation of labeled actin and thus high stability.


Analysis of time-lapse confocal actin FSM image series in 39 cells revealed a general pattern of four distinct zones of f-actin movements.

At the leading edge and throughout the lamellipodium, f-actin speckles continuously appeared and moved towards the cell center at 1.62 μm/min, as determined from kymograph analysis along the axis of motion [10].

As speckles approached the junction between the lamellipodium and lamellum they usually disappeared, suggesting a zone of f-actin disassembly [11].

To confirm this apparent gradient in actin density across the lamellipodium, cells were coinjected with X-rhodamine actin and FITC dextran (6 kD) as a volume marker.

Ratio imaging indicated a steep gradient in actin density from the front to the rear of the lamellipodium.


F-actin speckles underwent retrograde flow more slowly in the lamellum at 0.34 μm/min.

Speckles also appeared and disappeared throughout the lamellum, suggesting f-actin assembly/disassembly throughout this region.


F-actin in the cell body generally moved towards the leading edge at a rate of 0.44 μm/min.

Linear arrays of f-actin speckles in filament bundles moved coherently together generally towards the leading edge, though cell body bundles often approached the cell front at oblique angles.


Between the cell body, where the f-actin moved anterograde, and the lamellum, where f-actin moved retrograde, kymograph analysis along either the axis of migration or parallel to the leading edge failed to detect directed movement of f-actin speckles, but local movements and speckle disappearance was noted.

Although one might expect a buildup of filaments in this novel convergence zone where f-actin movements from the lamellum and cell body meet, ratio imaging with a volume marker revealed no increase in actin density.

Thus, like the base of the lamellipodium, the convergence zone must be a region of massive f-actin disassembly.

Interestingly, the direction of movement of f-actin on the ventral surface of newt lung cells correlates well with the direction of traction forces that other migrating cell types generate on the substrate [12][13].

This novel convergence zone is located where forward and rearward traction forces are likely to meet.

This strongly implicates local ventral f-actin movement in generation of traction force, and suggests that actin depolymerization may be a major regulatory step in cell body traction, as has been shown for lamellipodial protrusion [14].


MTs are coupled to f-actin movements


To test the hypothesis that the movements of f-actin and MTs are directly correlated during cell migration, we monitored the dynamics of f-actin and MTs in 39 cells using dual-wavelength confocal FSM to capture images of X-rhodamine f-actin and Cy-2 MTs in rapid succession every 10 s. In single confocal sections, we could easily differentiate between an MT end, whose fluorescence intensity terminated abruptly, and an MT extending out of the plane of focus, whose fluorescence gradually decreased towards a terminus.


We first analyzed in detail the behavior of individual MTs that were oriented primarily parallel to the leading edge.

Using dual-wavelength kymographs, we determined the percentage of total MT movement rates that matched within 2.5% the movement rates of the immediately adjacent f-actin speckles.

The precise coupling of f-actin and parallel MT movements occurred in all four zones of f-actin dynamic behavior: the lamellipodium, lamellum, convergence zone, and cell body.


We also analyzed individual MTs that were oriented perpendicular to the leading edge.

Kymographs showed that speckle marks on perpendicular MTs moved at the same velocity as the immediately adjacent f-actin in both the lamellum and in the cell body.

In contrast, in the lamellipodium and convergence zone, perpendicular MT movements were often uncoupled from actin movements.

In the lamellipodium, f-actin speckles moved rapidly past MT speckles, and the rearward velocity of the portion of the MT in the lamellipodium matched the slower rate of f-actin rearward flow from the leading edge in the lamellum.

MTs that were perpendicular to the leading edge and extended from the cell body across the convergence zone and into the lamellum were often uncoupled from the stationary actin in the convergence zone.

Sections of MT spanning this region buckled and bent extensively and moved at odd angles relative to the stationary f-actin speckles as portions of MT length moved into the convergence zone from adjacent cellular regions.


These results directly show that the acto-myosin–dependent movements of MTs reported previously [6][7][8] occur at the exact same rate as the f-actin in the immediate vicinity of the moving MT.

We suggest that the coordinated movement of f-actin and MTs is mediated by a specific crosslinking interaction between f-actin and MTs, as opposed to nonspecific trapping of MTs in the moving f-actin meshwork.

First, the coupling of MT and f-actin movements is regionally regulated, being least robust in the lamellipodium, where the f-actin density is the highest.

This strongly argues against the notion of nonspecific trapping of MTs by a dense f-actin meshwork.

Second, both MTs oriented parallel to the leading edge, which have a large cross-sectional area exposed to the force of f-actin flow, and MTs that are oriented perpendicular to the leading edge and which have a very small cross-sectional area exposed to the force of f-actin flow, move at the exact same rate as the immediately adjacent f-actin in the lamella and cell body.

However, our analysis suggests that nonspecific trapping could occur for parallel MTs, which continue to move at f-actin flow rates in areas of the cell where perpendicular MTs are uncoupled from flow.

Alternatively, MTs and actin could both be riding on the same as yet unidentified conveyor belt, although we consider this unlikely, as we have documented MT/f-actin crosslinking interactions in Xenopus egg extracts [15].


MTs interact with f-actin bundles


In the cell body and convergence zone, dual-wavelength movies revealed that dynamic MTs often aligned along f-actin bundles and moved with them along their anterograde/oblique paths.

In addition, dynamically unstable MTs associated with f-actin bundles that terminated in dense f-actin plaques.

Analysis of speckle fiduciary marks indicated that the f-actin bundle and associated MT were stationary with respect to one another, demonstrating that MTs grew and shortened, as opposed to translocated, along f-actin bundles towards the plaques.


We also observed a second type of interaction between MTs and f-actin bundles in the cell body.

Individual MT ends terminated on linear arrays of f-actin speckles, often joining the f-actin bundle at oblique angles.

Speckle tracking analysis showed that the MT end and speckles in the f-actin bundle moved in tandem, whereas the MT end moved past adjacent, nonbundle-associated f-actin speckles.

Thus, the MT end and the f-actin bundle were cotranslocating, as opposed to the MT moving along the f-actin bundle.

In addition, the distance between the MT end and a speckle along the MT shaft stayed constant over time, indicating that the MT end was neither growing nor shortening, but was capped.

However, we have no indication of whether these were MT plus or minus ends.


We suggest that the guidance of MTs along actin bundles and the cotransport of MTs and f-actin in the lamellipodium and cell body may be mediated by the same putative MTs/f-actin crosslinking factor.

This factor could be distributed along the MT or actin filament lattices and subject to regional regulation to allow local uncoupling of MTs from f-actin, i.e., in the lamellipodium or convergence zone.

Further, such a crosslinker may be fundamental to cell motility.

For example, MT crosslinking to focal adhesion-associated f-actin bundles is likely the mechanism of MT targeting to focal adhesions, which spatially regulates focal adhesion turnover [16].

Similar MT/f-actin crosslinking may also mediate guidance of MTs by f-actin bundles in neurons to determine MT distribution in growth cones that is critical to axonal guidance and branching [17][18][19][20].


In contrast, the interactions of capped MT ends with f-actin bundles likely is mediated by a separate molecular mechanism, in which a MT end–binding protein links to f-actin.

We suggest that MT end/f-actin interactions may be critical to functions that require establishment of cellular asymmetries.

For example, linking of MT ends to the f-actin–rich cortex is important for orienting mitotic spindles and the placement of the cytokinetic furrow during cell division [21][22][23].

Similarly, MT end/f-actin interactions could orient the centrosome forward of the nucleus and thus establish the polarity of migrating cells.

Indeed, recent evidence has shown that centrosome reorientation in migrating cells is dependent on the MT end–associated protein complex, cytoplasmic dynein [24][25].

There are several interesting candidates for linking MT to f-actin [26], and it will be interesting to test their involvement in MT-actin coupling with our dual-wavelength FSM assay.


We have used multimode fluorescent speckle microscopy (FSM) and correlative differential interference contrast imaging to investigate the actin–microtubule (MT) interactions and polymer dynamics known to play a fundamental role in growth cone guidance.

We report that MTs explore the peripheral domain (P-domain), exhibiting classical properties of dynamic instability.

MT extension occurs preferentially along filopodia, which function as MT polymerization guides.

Filopodial bundles undergo retrograde flow and also transport MTs.

Thus, distal MT position is determined by the rate of plus-end MT assembly minus the rate of retrograde F-actin flow.

Short MT displacements independent of flow are sometimes observed.

MTs loop, buckle, and break as they are transported into the T-zone by retrograde flow.

MT breakage results in exposure of new plus ends which can regrow, and minus ends which rapidly undergo catastrophes, resulting in efficient MT turnover.

We also report a previously undetected presence of F-actin arc structures, which exhibit persistent retrograde movement across the T-zone into the central domain (C-domain) at 1/4 the rate of P-domain flow.

Actin arcs interact with MTs and transport them into the C-domain.

Interestingly, although the MTs associated with arcs are less dynamic than P-domain MTs, they elongate efficiently as a result of markedly lower catastrophe frequencies.


In the neuronal growth cone, a sharp, yet highly dynamic interface exists between growing microtubules (MTs) in the axon shaft, and peripheral actin networks.

Mounting evidence suggests that directed axon guidance depends on coordinated interactions between MTs and actin filaments in growth cones [1].

During axon guidance, actin based motility is harnessed to promote MT growth and steering.

However, the fundamental details of how F-actin networks influence MT behavior, and vice versa, are not well understood.


MT behavior in growth cones was initially characterized by Tanaka and Kirschner [2] (also Sabry et al., 1991).

They found that individual unbundled MTs invaded the actin-rich peripheral domain (P-domain) and appeared to display properties of dynamic instability; however, because it was not possible to make fiducial marks on MTs, the respective contributions of polymerization and translocation to MT advance could not be assessed.

Damping MT dynamics with vinblastine (without net depolymerization) disrupted the normal cycle of MT bundling and splaying, but actin-based motility was essentially unimpaired.

Interestingly, vinblastine-treated growth cones tended to wander, suggesting that MT dynamics are necessary for converting actin-based motility into directed, persistent growth [3].

Subsequent reports confirmed that dampening MT dynamics also inhibited growth cone turning in response to guidance cues [2, 4, 5].


Hints that actin motility is an early step in axon guidance responses emerged from studies where F-actin accumulated in growth cone filopodia before turning [6].

In particular, there has been much speculation about the function of filopodia.

Evidence suggests that filopodia can play a structural role, exert mechanical tension, selectively adhere to target substrates, and are involved in signal transduction [7, 8].

EM and immunofluorescence studies have also noted striking coalignment of filopodial F-actin bundles with MTs in growth cones [9, 10, 11].

These observations led to the notion that capture or stabilization of dynamic MT ends via interactions with filopodial actin bundles may be an early step in growth cone turning [10, 11, 12, 13, 14].

It is also well known that growth cones use actomyosin-based motility to exert steering forces on substrates [15, 16].

Not surprisingly, cytochalasin treatments resulted in loss of axon and MT guidance capabilities [17], although axonal MTs can still extend randomly in the absence of actin assembly [18].

MTs were also observed to rapidly extend to the leading edge of growth cones treated with cytochalasin after clearance of P-domain F-actin [19], again suggesting intimate MT–actin interactions.


In complimentary experiments, damping MT dynamics (with taxol or low doses of MT assembly inhibitors) did not markedly affect retrograde F-actin flow; however, MTs were cleared from the P-domain [20, 21, 22, 23], suggesting retrograde actin flow may regulate MT positioning.

Indeed, recent studies in motile cells have demonstrated that retrograde F-actin flow affects MT translocation, assembly, and catastrophe rates [24].

All of these observations point to the importance of MT–actin filament interactions; however, a clear description of how these two cytoskeletal polymers influence each other’s behavior in growth cones has not emerged.


Recent advances in cooled CCD-based imaging techniques, including multimode fluorescent speckle microscopy (FSM), make it possible to directly measure actin and tubulin polymer dynamics in living cells, and correlate them with cell motility [25, 26].

In the current study we have used FSM to determine the relative contribution of MT assembly dynamics versus MT translocation in establishing the steady-state MT distribution in growth cones, and to address how F-actin dynamics influence MT behavior.

This data provides a quantitative framework for interpreting the complex effects of signaling pathways on the cytoskeletal motility machinery during axon growth and guidance [27].

We focus on three findings: (1) filopodia are assembly guides for a highly dynamic population of MTs that efficiently explore the P-domain; (2) actin filament arc structures in the T-zone are associated with a population of MTs with distinctly different kinetic parameters than those in the P-domain; and (3) filopodia and arcs both transport MTs.


Although elephants (Loxodonta africana Blumenbach, 1797) have less stringent habitat requirements than many other smaller mammals (Laws, 1970), they do not use habitats randomly (e.g. Leuthold, 1977; Western & Lindsay, 1984; Viljoen, 1989; Verlinden & Gavor, 1998).

This is especially true in the dry season when resources are less abundant and occur in hot spots (Ntumi et al., 2005).

Stokke & Du Toit (2000) argue that investi- gations into sex- and age-related differences in elephant feeding patterns and habitat use, coupled with surveys of elephant population structure, would be logical precursors to meaningful quantifications of the impacts of elephant populations on savanna vegetation.

In this paper, we investigate sexual segregation in space and resource use between mixed herds and bull groups in Kruger National Park (KNP), South Africa.


Various studies have shown that larger herbivores can tolerate a wider range of forage quality than smaller herbivores can (e.g. Bell, 1971; Geist, 1974; Jarman, 1974; Du Toit & Owen-Smith, 1989; Illius & Gordon, 1992; Wilmshurst et al., 2000).

This phenomena is mostly ascribed to the absolute metabolic needs and retention time increasing with body size, and mass-specific metabolic rates decreasing, with gut capacity as fraction of body mass remaining almost constant.

This means that larger herbivores can satisfy their nutritional requirements even when ingesting forage of lower quality, because the cell walls are better digested as a result of increased ingesta retention time (Demment & van der Soest, 1985).

Therefore, critical forage quality thresholds are higher for small Diversity and Distributions, 13, 225-236, Journal compilation © 2007 Blackwell Publishing Ltd than large animals and critical forage quantity thresholds are higher for large animals than small animals.


In the context of the present study, the theory of allometric scaling of dietary tolerance therefore implies that female and juvenile elephants will have higher nutritional requirements per unit body size than male elephants because of their smaller body size (Du Toit & Owen-Smith, 1989; Du Toit & Cumming, 1999).

Considering this, together with the increased reproductive demands on cows (pregnancy and lacta- tion), we can expect bulls to be less selective and more tolerant of lower-quality food than cows and juveniles (Stokke, 1999; Stokke & Du Toit, 2000).

This would result in a wider use of habitats and thus a wider spatial distribution of bull groups compared to mixed herds.

This forms the first hypothesis we addressed in this paper.


Although bulls have greater total water requirements (Young, 1970), cows and juveniles have higher relative rates of water turn- over because of their higher rates of evaporative and respiratory water loss (Gordon, 1977; Beuchat, 1990).

Furthermore, mixed herds with juveniles and neonates are less mobile than bull groups.

As a consequence, mixed herds will have to drink more often and minimize their distance to the closest water source.

The second hypothesis we tested is that mixed herds occur closer to surface water sources than bull groups.

This hypothesis has also been proposed by Stokke & Du Toit (2002).


In this paper, we examined dry season sexual segregation in the KNP elephant population at the landscape scale.

In particular, we considered differences between mixed herds and bull groups with regard to (1) spatial distribution, (2) forage and habitat selection (tree cover and herbaceous biomass), and (3) ranging distance from rivers and artificial waterholes.

We expect bull groups to (1) have a wider spatial distribution, (2) be less specific in their forage and habitat requirements, and (3) occur farther from surface water than mixed herds.


This study was conducted on a landscape scale and included the whole of KNP.

The park covers a total area of nearly 20,000 km 2 in the eastern Lowveld region of South Africa, adjoining the Mozambique border in the east and Zimbabwe in the north.

Elevations range from 260 to 839 m a.s.l. and mean annual rainfall varies from around 750 mm in the south to around 440 mm in the north, but with large variations between years (Gertenbach, 1980; Venter et al., 2003).

The west of the park is dominated by granites and their erosion products, whereas the east is domi- nated by basalts.

The Lebombo Mountains, primarily rhyolite, run from north to south on the eastern boundary.


Four major vegetation types dominate the KNP landscape (Eckhardt et al., 2000).

The south-west are well-wooded, with accumulation of low-quality grazing because of the nutrient- poor granitic soils underlying this area.

Important trees include the red bushwillow (Combretum apiculatum Sond.), knobthorn (Acacia nigrescens Oliv.), tamboti (Spirostachys africana Sonder) and marula (Sclerocarya birrea [A. Rich.] Hochst.).


In the south-east, on the basalts, the grasses are more palatable and tend to be more heavily grazed.

Important trees include knobthorn, leadwood (Combretum imberbe Wawra) and marula.


The north-west granites are dominated by mopane (Colophospermum mopane [Kirk ex benth.] Kirk ex J. Leonard) and red bushwillow and are poorly grassed.


The north-east basalts are dominated by homogeneous 1-2 m high, multi-stemmed mopane shrubs, with more grass cover than the north-west granites.


Six perennial rivers cross the park from west to east.

Further- more, more than 300 boreholes were drilled between the 1930s and the 1980s as part of the water provision programme to ensure a reliable year-round network of surface water throughout the park (Gaylard et al., 2003).


In more recent years, the desirability of such a high density of surface water, minimizing the influence of temporal variability in rainfall, has been questioned (e.g. Walker et al., 1987; Owen-Smith, 1996; Harrington et al., 1999; Owen-Smith & Mills, 2006).


Consequently, the water provision policy was revised, resulting in the closure of more than half the artificial waterholes (Pienaar et al., 1997).

The history of the water provision programme and the revision thereof is described in detail, respectively, by Pienaar (1985) and Pienaar et al. ( 1997).


A common spatial framework to which all the Geographical Information System (GIS) and satellite data layers could be summarized had to be enforced.

This is a common approach in ecological studies faced with data from different sources and with different underlying spatial resolutions (e.g. Tognelli & Kelt, 2004).

Considering the spatial resolution of the different data sets, it was decided to summarize all variables to a common 1-km grid.

This resulted in 19,269 grid cells covering the whole of the park.

As could be expected, some cells on the boundaries were smaller than 1 km 2 , and as a result all the different data layers were summarized to the reference grid by averaging within the grid, rather than by adding within the grid (i.e. standardizing by area).


Two KNP elephant distribution data sets were used.

Data from the annual mega-herbivore census (a helicopter count of ele- phants, buffaloes and rhinos) were used for testing whether the elephant sexes exhibit spatial and resource segregation.

These data were also used for calibrating the multivariate autologistic regression models that identify significant predictors of elephant distribution.

The second data set came from the annual ecologi- cal aerial survey (EAS), which used a fixed-wing aeroplane to count all the large herbivores in the park.

The EAS data set was used only for verifying the predictions of the autologistic models.


A helicopter was flown at an average height of 150 m and at a speed of 100 knots, with four observers on board, counting and noting all elephants, buffaloes and rhinos.

These annual censuses were conducted over the whole park in the late dry season (August), using a total count on a pattern that follows all of the Sexual segregation of elephants © 2007 The Authors Diversity and Distributions, 13, 225-236, Journal compilation © 2007 Blackwell Publishing Ltd 227 drainage lines (Whyte, 2001a).

When a big elephant herd was sighted, the helicopter would circle lower until the observers reached consensus on the herd size.

The number and location was plotted on a 1 : 100,000 map for later digitization.

For each sighting, it was noted whether it was a mixed herd or a bull group.

Mixed herds were defined to include breeding herds with a matriarch and other adult females with their offspring, as well as family units attended by adult males.

Bull groups comprised of adult males only, occurring independently from family units.

Individual elephant bulls were included in the bull group category.


We used data from 1985 to 1996 in this study, with census totals ranging from 6887 to 8371 individuals over this period.

No accurate locations were recorded for the sightings prior to 1985.

In 1997, the water provision policy in the park was revised and many artificial waterholes were closed (Pienaar et al., 1997).

This caused a change in the resource distribution and hence data from post 1996 were excluded from the present analysis.


In order to get a better idea of elephant distribution, all sight- ings over the 12-year period were combined into a single GIS data layer.

This approach of combining snap-shot data from various periods is common in ecological studies (e.g. Osborne et al., 2001; Lichstein et al., 2002; Silva et al., 2002; Tognelli & Kelt, 2004; Olivera-Gomez & Mellink, 2005).

The data were then transformed into absence/presence data for each 1-km grid cell.


The fixed-wing EAS were used as an independent data set for validating the model predications.

The EAS was conducted annually over most of the park in the dry season (May-August), using a total area count on 800-m wide strip transects (Viljoen, 1996).

A fixed-wing aircraft was flown at a height of 65-70 m at a speed of 95-100 knots, with four observers on board.

The location and herd size of the key herbivore species were recorded.

Most of the park was covered from 1981 to 1993, after which the methodology was changed and only sections or specific transects in the park were flown.

We used data from 1985 to 1996, covering the same period as the mega-herbivore census data used in this study.

Again, all the sightings were combined into a single GIS layer and transformed into absence/presence data for each 1-km grid cell.


The annual counting of elephants in both the mega-herbivore census and EAS may appear like duplication in effort.

It is known that the EAS data contain an under-count bias (Redfern et al., 2002) and because mega-herbivores (elephant, buffalo and rhino) are of particular concern for managers of confined conservation areas, like the KNP, the helicopter census was designed to provide better estimates and distributions of these heavy, bulk feeders (Joubert, 1983).

The greater manoeuvrability of the helicopter compared to the fixed-wing aeroplane improves data collection, resulting in better-quality data.

However, even though the EAS data represent an under-count of the elephant population, it still provides independent and spatially accurate distribution data suitable for validating the regression models.


Table 1 lists the resource variables included in the study.


Every May since 1989, herbaceous biomass has been estimated at more than 500 field sites scattered throughout the park.

These sites were selected to be proportionally representative of the different landscapes in the park (Trollope et al., 1989).

Field measurements were taken with a disc pasture meter at these sites and the herbaceous biomass calculated from a simple regression equation calibrated for Kruger.

The method is fully described in Trollope & Potgieter (1986).


Variograms of the average herba- ceous biomass at each site revealed that the sites exhibited strong spatial structure, suggesting that geostatistical techniques may be useful for interpolation across the park.

Co-kriging is one such geostatistical interpolation technique that minimizes the variance of the estimation error by exploiting the cross-correlation between the variable to be interpolated and another variable (Isaaks & Srivistava, 1989; Webster & Oliver, 2002).

We established that advanced very high-resolution radiometer (AVHRR) derived normalized difference vegetation index (NDVI) metrics had a reasonable cross-correlation with biomass and therefore we used it as a secondary variable in the co-kriging.

Various other studies have also shown that annual metrics derived from remote sensing can be useful for estimating vegetation cover (e.g. Hansen et al., 2002a; Schwarz & Zimmerman, 2005).

We found that co-kriging, using the satellite-derived data described previously as secondary variable, gave better interpolation results than ordinary kriging without the satellite data.

The data and method are fully described in Smit et al. ( in preparation).

The interpolation was carried out on a 500-m grid, before being averaged to the 1-km reference grid.


These data were derived from the moderate resolution imaging spectroradiometer (MODIS).

The product we used is referred to as MOD44B (vegetation continuous fields) and can be found on the internet (http://glcf.umiacs.umd.edu/data/modis/vcf/ description.shtml).

This product estimates for each 500 m pixel, the percentage of the pixel covered by (1) trees (2) bare soil and (3) herbaceous layer.


The dependent data were derived by classifying high-resolution imagery and then aggregating it to the coarser MODIS scale.

The independent data were derived from multitemporal metrics based on a full year of coarse resolution MODIS data.

A regression tree algorithm was then used to predict the dependent variable of tree cover based on signatures from the multi-temporal metrics.


The method is fully described in Hansen et al. ( 2002a) and verified for an African woodland in Hansen et al. ( 2002b).


We included tree canopy cover in our analysis, averaging it to the 1-km reference grid.


The distance from the centroid of each 1-km grid cell to the closest main river was calculated using standard GIS procedures.

Rivers were classified as 'main' rivers if they were perennial or seasonal with reliable pools.


The period studied was during the height of the water stabilization programme, with more than 300 artificial waterholes providing surface water for animals throughout the park (Gaylard et al., 2003).

The distance from the centroid of each 1-km grid cell to the closest artificial waterhole was calculated.


As a first step to establish whether bulls have a wider occurrence in the park, we simply compared the proportion of the total number of cells within which bulls were observed to the proportion of cells within which mixed herds were observed.

The McNemar test (McNemar, 1947) was used to test if the probability is the same to detect a bull group or a mixed herd in any given cell.


Figure 2 illustrates the  results.

Note that both distance to closest artificial waterhole and distance to closest river were nor- malized by the square-root transform.

Northing was relatively uniformly distributed and could not be transformed to a normal distribution using the transformations usually employed, so care should be taken when interpreting the northing results.


To explore the multivariate significance of each resource variable in explaining the presence of mixed herds and bull groups, respectively, logistic regression models were used.

However, con- ventional logistic regression ignores spatial autocorrelation in the residuals.

This often inappropriately inflates the statistical importance of variables (Lichstein et al., 2002; Knapp et al., 2003).


To overcome this, we adopted the approach proposed by Augus- tine et al. ( 1996), by incorporating an autologistic term in the models.

The autologistic term was calculated as the probability of occurrence based on kernel density in a neighbourhood around the cell of interest.


This means that cells with many herds in close proximity will have bigger autologistic values (higher probabilities) than cells with no herds in close proximity (lower probabilities), using a Gaussian distance decay function.


After investigating the indicator variograms (Isaaks & Srivastava, 1989) for mixed herds and bull groups, it was decided to define the neighbourhood as 15 km, representing the range of the spatial dependence.


Although calculated slightly differently, this approach of using a distance decay function is similar with that used in other studies (Augustine et al., 1996; Osborne et al., 2001; Lichstein et al., 2002; Tognelli & Kelt, 2004).


To select among multivariate model terms, backward selection procedures were used.

The full model was run with all explana- tory variables and the term with the highest P value was removed Sexual segregation of elephants running the model.

Terms were removed until the biggest P value was less than 0.05.


The predicted surfaces from the regression models were cross- validated using the elephant distribution data collected in an independent survey (EAS).

We compared if elephants indeed occurred more frequently in cells with higher predicted probabil- ities using a different data set than the one used for calibrating the models.


Statistical analyses were conducted using  (version 14) and all GIS analyses were conducted using  (version 3.2).


Over the 12-year period of this study, bulls were observed in significantly more grid cells (24.89%) than the mixed herds (17.50%) (McNemar χ 2 = 354.8164, d.f. = 1, P < 0.001).


Additionally, 25.08% of the cells within which bulls were observed occurred more than 2 km from the nearest cell within which a mixed herd was recorded, whereas only 3.96% of mixed herd cells were farther than 2 km from the nearest cells where bulls were observed.

Figure 1 confirms this visually.

Note the wide distribution of bulls and the exclusive bull areas as well as the apparent lack of such exclusive areas for mixed herds.


Cells within which only bulls were observed were more north than cells within any of the other categories (Fig. 2a: F = 31.28, d.f. = 3, P < 0.001).

The cells with no elephants were, however, more north than both the cells within which mixed herds and bull groups occurred concurrently, as well as the cells within which only mixed herds occurred.

However, the northing of the cells within which mixed herds and bulls occurred simultane- ously were not significantly different from the northing of cells occupied exclusively by mixed herds.


The categories differed concerning herbaceous biomass (Fig. 2b: F = 41.30, d.f. = 3, P < 0.001).

Bull groups occurred, on average, in cells with more herbaceous biomass than cells within which no elephants were encountered.

Furthermore, cells within which no elephants were observed had more herbaceous biomass © 2007 The Authors Diversity and Distributions, 13, 225-236, Journal compilation © 2007 Blackwell Publishing Ltd 231 than cells within which both mixed herds and bull groups occurred simultaneously.

The latter did not differ significantly from cells within which only mixed herds occurred.


The tree cover was highest in the cells where mixed herds occurred and lowest in cells within which only bulls were encountered (Fig. 2c: F = 63.7, d.f. = 3, P < 0.001).


The cells where elephants occurred were closer to the rivers than cells within which no elephants were observed (Fig. 2d: F = 112.27, d.f. = 3, P < 0.001).

Cells within which both mixed herds and bull groups were observed were closest to the rivers, but this was not significantly closer to rivers than cells where only mixed herds occurred.

Cells within which bull groups occurred exclusively were farther from the rivers than cells where mixed herds occurred exclusively, but it was still closer than cells where no elephants were observed.


The categories differed concerning distance to the closest arti- ficial waterhole (Fig. 2e: F = 37.75, d.f. = 3, P < 0.001).

The mul- tiple comparison test showed that cells within which bull groups occurred were significantly closer to the artificial waterholes than cells within which no elephants were observed.

However, cells within which both mixed herds and bull groups occurred as well as cells within which mixed herds occurred exclusively were farther from the waterholes.


Collinearity between predictor variables may confound their independent effects.

Therefore, prior to logistic regression ana- lysis, we calculated the Pearson correlation coefficients (r) for all pairwise combinations of the independent variables.

The corre- lations between the resource variables were moderate to low (| r | < 0.3) and therefore we did not consider collinearity to be of major concern (Berry & Feldman, 1985).

Table 2 summarizes the variables that were included in the models after backward selection.

Note that both mixed herds and bull groups are attracted to rivers.

Additionally, mixed herds prefer areas with higher tree coverage.

Bull groups seem to be significantly associated with waterholes and although they occur throughout the park, their distribution is biased towards the north.


The autologistic models performed better than a random model in predicting mixed-herd and bull-group dry season distribution of an independently collected data set (Fig. 3).

The cells were sorted from small to large predicted probabilities and based on this, divided into four classes with equal number of cells within each category (i.e. quartiles).

The percentages of the total Fig. 3(a-b), respectively.


The upward trend in both Fig. 3(a-b) indicates that the models are useful in predicting the distribution of mixed herds and bull groups, with more elephant herds occurring in cells with higher predicted probabilities.

Note, however, that there are still considerable sightings in the lower probability classes.

Consider- ing (1) that the models were based on aggregating snapshot data from different years (2) the inherent inaccuracies associated with data collection and recording (3) that elephants may have been recorded as they moved across non-preferred habitat between feeding hot spots (Bayley et al., 1998) (4) the uncertainty associ- ated with interpolation techniques (herbaceous biomass), (5) the inaccuracies associated with remote sensing data (tree cover), and (6) the fact that the various data layers were collected on different spatial scales, the models performed reasonably well.

However, these models are probably more valuable in an explor- atory way, indicating which variables drive the landscape scale and sexually distinct dry season distribution patterns of elephants, rather than being accurate models for predicting fine-scale localized patterns.


This study indicates differences between the elephant dry season distribution patterns of mixed herds and bull groups in the KNP.

Compared to mixed herds, elephant bull groups had a wider scatter across the park.

This provides support for the hypothesis that elephant bull groups have less specific habitat and food requirements (Stokke, 1999; Stokke & Du Toit, 2002), and there- fore they can roam more extensively across a wider range of habitats than mixed herds.

This can be expected because the larger- bodied bulls can tolerate a wider range of forage quality, are more mobile to move between feeding hot spots, and because of smaller group sizes, have lower collective feeding requirements than the bigger mixed herds.

This also agrees with work carried out in Mozambique that found bulls' home range to be larger than that of females (Ntumi et al., 2005).


As resources are more widely available during the wet season, niche differentiation may be weaker during the abundant season when competition is less intense.

For example, Gordon & Illius (1989) and Voeten & Prins (1999) have shown that the distributions of herbivores overlap more during the abundant season than during the lean season.

Ntumi et al. ( 2005) also found that elephants occur in hot spots during the dry season when resources are less abundant.

Therefore, although it seems as if the bull areas remain intact during the wet season (I Whyte, pers.

obs.), the sexual segregation boundaries may become softer during the rainy season because of a wider availability of resources, and musth bulls seeking more mating opportunities.


Over and above the wider distribution of bull groups com- pared to mixed herds, our results demonstrate inter-sexual differences between the resources in the grid cells frequented by the mixed herds and bull groups, respectively.

The univariate non-spatial analysis suggests that the average of resources in grid cells where mixed herds occurred exclusively differed significantly from the average of resources in grid cells where bull groups roamed exclusively.

In addition, it was of particular significance that the resources in the cells within which both mixed herds and bull groups occurred concurrently were more similar to the resources in the cells within which only mixed herds occurred than the cells within which only bulls occurred.

This illustrates that the bulls' distribution and resource use overlaps with that of mixed herds, but not vice versa.


One possible reason is that bulls can utilize resources that the mixed herds utilize, but mixed herds cannot utilize resources/areas that the bulls use (i.e. bull areas).

Furthermore, the bulls' distribution may overlap with mixed herds, as a result of musth bulls associating with the family units for mating opportunities.

However, the time of the surveys (dry season) is when very few females are in oestrus (Smuts, 1975) and mating possibilities would be limited.

It is also likely that the overlap is the result of young males that have left their natal herd, but still occur in the vicinity (Moss, 1988).

We will leave this as an untested hypothesis because no data on size, age and social relationships of the bulls were collected.


Note, however, that this hypothesis of young bulls leaving their natal herd and staying in the area close to the herd is in line with the principle of allometric scaling.

Because elephants grow for their entire lifetime (Laws, 1966; Hanks, 1972), the young mature bulls leaving the mixed herds will be smaller than old bulls and may therefore have habitat and forage requirements similar to that of the smaller individuals in mixed herds (hence the observed resource overlap).

However, as the bulls grow older and bigger, they can move out of these mixed herd areas into bull areas, exploiting resources that will not meet the requirements of the smaller cows, calves and new-generation young bulls.


Mixed herds were expected to occur closer to surface water sources than the bull groups because of higher rates of water turnover (Gordon, 1977; Beuchat, 1990) and lower mobility.

This was indeed observed around the rivers (Fig. 2d), with mixed herds occurring closer to the rivers than the bull groups.

Stokke & Du Toit (2002) observed the same pattern in Chobe, Bot- swana, with bull groups roaming farther from the rivers than the mixed herds during the dry season.

Laws (1970) also noted that bulls could survive for longer periods away from water.


A point of interest in Kruger, however, concerns the pattern around the artificial waterholes, with bull groups occurring closer and mixed herds being neutral to these artificial water sources (Table 2).

We hypothesize that this preference for rivers by the mixed herds is because rivers not only act as water sources, but additionally as a habitat hot spot with shade and higher-quality browse.

Many of the waterholes, especially in the north of the park, are located farther from the main rivers, sometimes in open, grassy areas with little tree cover.

As seen in this paper, this is suboptimal dry season habitat for the mixed herds.

Therefore, surface water availability did not seem to provide a strong enough incentive to lure large numbers of mixed herds away from the rivers and the areas with a denser tree canopy where nutritional, water and shade requirements could be met.

However, our results suggest that waterholes may be more important for bulls.

Visual inspec- tion of Fig. 1 seems to further support this notion.


However, it must be remembered that because of the data collection method, the results of this paper show that mixed herds are not aggregat- ing around the waterholes at the time of the survey and do not Sexual segregation of elephants © 2007 The Authors Diversity and Distributions, 13, 225-236, Journal compilation © 2007 Blackwell Publishing Ltd 233 necessarily imply that the mixed herds do not utilize the water- holes from time to time, at night, during other seasons or when moving across otherwise waterless areas.


The multivariate spatial analysis suggests that tree cover and distance to rivers are significant predictors for mixed herds' dis- tribution, with mixed herds preferring areas close to rivers and with higher tree coverage.

This agrees with the work of Ntumi et al. ( 2005) in Mozambique who found that elephants prefer habitat types with a relatively closed canopy as well as riverine thickets.

This may be expected, as the mixed herds, with their requirements for shade (Laws & Parker, 1968; Laws, 1970; Barnes, 1993) and higher-quality forage (Miquelle et al., 1992; Du Toit, 1985; Weckerley, 1993; Stokke, 1999) will select areas with adequate tree cover.

Furthermore, many studies in Africa have proven that elephants prefer to browse rather than graze during the dry season, providing further support for the impor- tance of trees (Laws, 1970; Beekman & Prins, 1989; Kabigumila, 1993).

The bark and roots of trees also form important high- quality resources utilized by elephants.


Both distance to rivers and distance to waterholes, as well as UTM northing, significantly predict the bull's distribution (Table 2).

It therefore seems as if it is primarily water resources, and not forage (tree or herbaceous cover) determining bull dry season distribution.

This also agrees with our hypothesis that bulls, because of their size, can utilize a wider range of low-quality forage.

Stokke (1999) found similar results in a study conducted at a feeding patch scale.

It was found that family units discriminated between patches in their surroundings and selected patches offer- ing the highest density of palatable species, whereas males were apparently less influenced by the distribution of resources in their environment and browsed in patches containing the same amount and combination of species as surrounding areas.

In Kruger, bulls are often seen during the aerial censuses to stand fully exposed on grassy plains or under the few large trees on grassy plains, whereas it is extremely uncommon to see mixed herds in these open areas.

We propose that bulls can occur on the open grassy plains in the dry season because of their wider tolerance for low-quality food, with grass being less nutritious than browse in the dry season.

Also, individual or small groups of bulls need fewer trees to satisfy their shade and browse requirements (com- pared to the larger mixed herds, which need more shade, cover and browse, especially if young elephants are present).


Considering all the grid cells, the average distance to the closest artificial waterhole and closest main river were 4.4 km and 6.1 km, respectively, for the study period.

This is relatively short distances that should not pose major difficulty for elephants to cross.

There- fore, very little of the park was really significantly far from surface water and hence we expect that a full expression of how elephants relate to surface water could not be expressed in the Kruger context.

Only if many or all the waterholes are closed or rivers dry up, may elephants alter their distribution with regard to surface water.


The elephant population of the Kruger National Park (KNP) increased at a mean intrinsic rate of 7.5% per year between 1967 and 2000 (Whyte et al., 2003).

The culling of elephants from 1966 to 1994 kept the population between 7000 and 8500 indi- viduals.

However, after 1994, a moratorium was placed on cull- ing (Whyte et al., 1999) and the elephant population increased to 12,467 (0.66/km 2 ) in 2005 (Whyte, 2005).

Cumming et al. ( 1997) stated that an elephant density of 0.5/km 2 could convert a savanna woodland to a shrubland.

Various other studies have also proven that high densities of elephants modify and change animal and plant communities (e.g. Western & Gichohi, 1989; Moolman & Cowling, 1994; Johnson et al., 1999; Lombard et al., 2001; Western & Maitumo, 2004) and homogenize habitats.

The core mission of the KNP is the preservation of biodiversity (Mabunda et al., 2003), and a loss of biodiversity as a result of the effects of a large elephant population is therefore unacceptable.

However, whether Kruger has long reached, are about to reach or will ever reach an elephant population density that will cause structural homogenization or cause a loss of biodiversity, are highly debated topics (Anon, 2004) and hence elephant manage- ment, or the lack thereof, remains controversial (Whyte et al., 1998; Whyte, 2001a,b; Gillson & Lindsay, 2003; Skinner, 2005; Whyte & Fayrer-Hosken, in press).


But not only is the need for elephant management in Kruger, and indeed many southern African countries, controversial, but also the options available to managers.

Culling, immuno-contraception, translocations and metapopulations with movement corridors are some of the options often considered (Whyte, 2001b; Anon, 2004).

Another non-intrusive management tool that has been proposed is the management of artificial water sources (Owen-Smith, 1996; Gillson & Lindsay, 2003).


Considering (1) the results presented here (preference of mixed herds for areas close to rivers as opposed to areas close to artificial waterholes during the dry season) (2) the fact that the population growth rate did not decrease post-1997 when more than half of the waterholes in Kruger were permanently closed (Pienaar et al., 1997; Whyte, 2005) (3) the abundance of natural surface water in Kruger (Redfern et al., 2005), and (4) the abundance of elephants in parks with no or little artificial water provision (Barnes et al., 1999), it is unlikely that closure of artificial waterholes will affect the elephant population in the KNP significantly.


In fact, smaller less mobile species with more specific habitat requirements will possibly be more affected if waterholes are closed within their home range (Smit et al., in press).


It is important to distinguish between resource use by elephant bulls and mixed herds because their impact on the habitat may differ.

In this study, we compared the dry season distribution of bull groups and mixed herds, highlighting spatial and resource segregation between them in the Kruger National Park (KNP), South Africa.


It was found that bulls roamed more widely across the park and were more tolerant in their habitat requirements than mixed herds.

It was of particular significance that resources in areas fre- quented by both mixed herds and bull groups were more similar to resources in areas where only mixed herds occurred, but differed Diversity and Distributions, 13, 225-236, Journal compilation © 2007 Blackwell Publishing Ltd from resources in areas that were used only by bulls.

Also, it was possible to delineate areas exclusively roamed by bulls in the dry season, but no comparable exclusive mixed herds area could be found.

This implies that bulls share space with mixed herds, but additionally have exclusive access to other areas and resources that are not available for mixed herds to utilize.

These 'bull areas' occur in almost every studied elephant population (e.g. Moss, 1988) and we have shown this to be the case in Kruger as well.

Bulls spend most of their adult lives living in peaceful harmony with other bulls, and only move out into the breeding herd areas when in musth.


During the dry season, mixed herds occurred more frequently in areas with abundant tree cover and close to rivers.

Although bulls were also attracted to rivers, they occurred, on average, far- ther from them than mixed herds.

Furthermore, artificial water- holes might potentially have opened up previously unutilized areas for bulls in the dry season, especially on the grassy basaltic plains in the north of the park.

Elephant bulls show a distinct preference for drinking at artificial waterholes' reservoirs, appar- ently preferring the clean water that they can easily access with their trunks.

However, natural surface water did not seem to be a scarce enough resource for artificial waterholes to lure mixed herds away from the rivers where they could meet their forage, water and habitat requirements.

The feeding and water require- ments hypotheses proposed and examined in this study should be further tested in the field, tracking individual herds for extended periods and over different seasons.


Elephant management is highly controversial and any elephant-related study is quickly seen as a pro or con for one of the usual elephant management practices (culling, contraception or metapopulation management).

Managing water sources has also been suggested as one option, even though no experimental data exist.

In a recent issue of Diversity and Distributions, Smit et al. ( 2007) rejected this management tool as an efficient method to control elephant distribution and dynamics in Kruger National Park, South Africa.

However, we believe that (1) Smit et al. 's conclusion is poorly supported by the available data, and (2) that this non-intrusive management tool has the potential for being one of the few practices able to reconcile managers, scientists, and the public opinion in the never-ending ‘elephant problem’.

For these reasons, we believe that the strong conclusions of Smit et al. should be moderated.

Finding a management tool, on which there is a consensus, to control elephant impacts seems to be harder than the quest for the Holy Grail.

In a recent issue of Diversity and Distributions, Smit et al. ( 2007) questioned the idea that surface-water management could help in managing elephant distribution in the famous Kruger National Park (KNP, South Africa), specifically stating that ‘considering (1) the results presented here (preference of mixed herds for areas close to rivers as opposed to areas close to artificial waterholes during the dry season); (2) the fact that the population growth rate did not decrease post-1997 when more than half of the waterholes in Kruger were permanently closed (Pienaar et al., 1997; Whyte, 2005); (3) the abundance of natural surface water in Kruger (Redfern et al., 2005); and (4) the abundance of elephants in parks with no or little artificial water provision (Barnes et al., 1999), it is unlikely that closure of artificial waterholes will affect the elephant population in the KNP significantly’.

While we accept Smit et al. 's results of a closer vicinity of elephant herds to rivers than to artificial waterholes (and distance to river, not to artificial waterholes, being a significant predictor of elephant presence), we strongly disagree with the management-related conclusions of the authors.

Our work in Hwange National Park (HNP, Zimbabwe) has been leading us to think that artificial surface-water management remains a highly promising, overlooked, elephant management practice (Chamaillé-Jammes et al., 2007), and that this may hold true for most other parks and specifically for KNP.

Building on our experience, we here offer a re-interpretation of Smit et al. 's arguments, discussing the four above points backward.


(4) Is there any ecological basis to consider that artificial water provision must be necessary to reach high elephant abundance? We believe not.

Elephants need to access water to survive the dry season, however, there is yet no demonstration that natural or artificial water have different ability to sustain elephant presence, and global water distribution across the landscape is likely to be the major factor affecting elephant abundance.

Comparison of elephant densities with respect to surface-water origin should only be conducted after having accounted for this overall availability of water, and such analysis remains to be conducted.

Historical information from individual parks, however, show that artificial water provision indeed increased elephant densities where surface-water was low or inexistent in the dry season.

In HNP, elephant abundance increased from a guestimate of 1000 individuals in 1928 to more than 8000 in 1971 following the creation of over 60 boreholes during this period (Davidson, 1967; Williamson, 1975), and went on growing despite population control through culling (Cumming, 1981).

Unhopefully, the potential historical effect of artificial water supply on elephant in Kruger cannot be disentangled from the recovery of past hunting, and may only be inferred from surface-water availability across the landscape.


(3) Redfern et al. ( 2005) suggested that boreholes in KNP had a negligible effect on the general vicinity to water due to numerous ponds or river pools linked to dry season rainstorms or carry-over effects of previous rainfall.

However, the importance of these small sources for elephant distribution and demography remains unknown, and the absence of these types of water sources in the analysis of Smit et al. suggests that these authors did not consider them relevant.

Actually, it has been shown that ‘removing all boreholes during dry years [below-median] increases the percentage of area beyond 10 km of persistent water sources from 1% to 50% in the northern region and from 1% to 38% in the southern region’ (Redfern, 2002 p. 85; see also fig. 4 in Redfern et al., 2005).

Therefore, approximately 40% of KNP remains further than 10 km of water during dry years in a natural situation, and could be considered inaccessible for elephant herds.

Thus, can one really say that natural surface-water availability in Kruger is abundant?

Conversely, artificial waterholes clearly open space for elephant herds in KNP.


(2) Not only elephant population growth rates did not decline post-1997 when the water provision policy was revised and half of the artificial waterholes were closed, but the population went on increasing at the same rate for at least six more years, recovering from the culling control ceased in 1994.

However, one should expect population growth rates to respond to changes in resource availability only when a population approaches the so-called carrying capacity mainly determined by such resource, especially in such long-lived species where density dependence is likely to be highly convex (Sinclair, 2003).

Has the Kruger elephant population ever reached levels at which resources will affect elephant demography noticeably?

We believe it has not.

The KNP elephant population has been around 12,500 individuals at its highest (Blanc et al., 2007).

Is this that much? It is not.

In HNP, only three-quarters the size of KNP, the elephant population has been estimated to be over 25,000 individuals for over a decade, reaching up to 44,492 animals in 2001 (Dunham & Mackie, 2002).

Although Kruger and Hwange NP experience similar annual rainfall, surface-water availability is much lower in HNP, with less than 60% of the park being within 10 km of water when all artificial waterholes are active (Chamaillé-Jammes et al. in press).

Considering the KNP elephant population as a low-density population would be of a shock for some, but it has to be reminded that the often stated sustainable threshold of 7000 individuals for KNP has been based on elephant impacts (Van Wyk & Fairall, 1969), not elephant demography per se.

It is not surprising, and in any case not an argument to reject potential later effects of surface-water management, that closure of half the artificial waterholes had no noticeable effect on elephant dynamics in KNP.


(1) What Smit et al. 's study showed is that elephant herds tend to be closer on average from rivers than artificial waterholes (respectively approximately 3.7 and 4.3 km) at the time aerial censuses were conducted, obviously during daylight.

It is, however, known that elephants drink mainly at dusk or night (Owen-Smith, 1988), day being mostly spent foraging and resting.

It is therefore expected that, even if case elephants would heavily use waterholes at night, they should not be found close to waterholes during the day, especially as food quantity is likely to be lower in the vicinity of permanent water sources (Thrash, 1998; Brits et al., 2002).

This is also probably why more generally elephant locations in Smit et al. 's study were not in the close vicinity of water.

Artiﬁcial waterholes could have been vital to elephants in KNP, results would not have been different.

The relevance of only an approximate 500 m difference in the average distance to water could also be discussed for such a large ranging animal.

Lastly, the extent to which habitat selection of elephants was affected by culling (conducted 4 months before censuses during 10 out of the 12 study years) remains unaccounted for (for large-scale effects of culling in KNP see Van Aarde et al., 1999).

Smit et al. 's study adds to the literature on elephant habitat use and suggests that increased cover and perennial rivers could be an important habitat for elephants.

It would have been more convincing in rejecting the importance of artificial waterholes if the analysis had investigated distribution changes when artificial water policy was revised.

We believe that the demonstration that surface-water management would be ineffective in limiting the KNP elephant population remains to be made.

We showed elsewhere that surface-water has been a key-driver of changes in elephant densities in one of the world’s largest elephant populations (Chamaillé-Jammes et al., 2007).

However, we acknowledge that controlling elephant numbers and mitigating elephant influence on ecosystems may at times have to be distinguished to achieve some specific management objectives.

Contradictory opinions may often simply highlight lack of knowledge.

We therefore urge ecologists to investigate under which environmental conditions various non-intrusive, low cost surface-water management policies could control elephant distribution and abundance.

We hope that in suitable sites surface-water management could help in reaching a non-controversial way forward in elephant management, rather than facing again the cyclical management and public opinion crisis.


In a recent paper we demonstrated that elephant bull groups and mixed herds exhibited spatial and resource segregation across the Kruger National Park.

It was found, inter alia, that both bull groups and mixed herds occurred more frequently closer to rivers than expected if they were randomly distributed, but that only bull groups occurred more frequently closer to the artificial waterholes.

Although Chamaillé-Jammes et al. ( 2007) accepted these results, they disagreed with our interpretation regarding the potential effect that closure of artificial waterholes might have.

Here we address some of the specific concerns expressed and provide a broader context regarding water provision and elephant management.

Although water provision can influence elephant density and distribution, we argue that the effectiveness of surface-water manipulation as a management tool will depend on (1) natural surface-water availability, (2) forage quality, (3) elephant densities, (4) position of a population on its growth trajectory, and (5) management objectives.

Even though elephants are water-dependent, the effectiveness of water provision as a management tool will therefore be area- and population-specific and will depend on management objectives.


In a recent paper we considered the landscape-scale sexual segregation of elephant bulls and mixed herds in the Kruger National Park (KNP), South Africa, and compared the resources between the areas frequented by the mixed herds and bull groups, respectively [1].

We found that bull groups and mixed herds exhibited both spatial as well as resource segregation.

Although Chamaillé-Jammes et al. ( 2007) accepted the results presented in Smit et al. ( 2007b), they disagreed with our interpretation of the implications of the paper regarding surface-water management.

We welcome their comments and believe that these provide useful context and alternative interpretations for the results presented in our original paper.

It is our opinion however, that the quote on which Chamaillé-Jammes et al. ( 2007) base their response does not reflect the broader context and conclusions of the original paper, which we believe provide some of the suggested moderation.

We therefore appreciate this opportunity to respond to the interpretation provided by Chamaillé-Jammes et al. ( 2007).

We agree that surface water is one of the primary drivers of elephant population dynamics and distribution patterns.

Hence, as stated by Chamaillé-Jammes et al. ( 2007), surface-water manipulation can potentially be a powerful tool for managing elephant populations.

This, however, will only be true for surface-water-limited populations.

In this communication we would like to emphasize that water scarcity and a whole suite of other factors need to be taken into account when considering the most appropriate options to achieve specific management objectives.


In our opinion, two conditions are crucial if the removal of artificial waterholes is to be a successful tool for managing elephant populations: 1.

The elephant population must be water limited (i.e. addition or removal of water should influence the density and distribution patterns).

2. The densities at which the impact of elephants exceed the impacts that are acceptable/desirable to managers must be similar or lower than the densities that would be achieved if no water was artificially provided.

Although these two conditions may hold true in certain parks, it is debatable whether it is the case in Kruger.

This was recognized by Owen-Smith et al. ( 2006), who suggested that the reduction of artificial waterpoints might potentially contribute towards restricting the dry season distribution of elephants, but at the same time acknowledged that it was debatable how effective this would be due to the fairly widespread natural water sources across the Kruger landscape.


We believe that one of the reasons for the response of Chamaillé-Jammes et al. ( 2007) may be the differences between the KNP (our study area) and Hwange National Park (study area on which Chamaillé-Jammes et al. ( 2007) based their experience) and their respective elephant populations.

These parks differ in terms of (1) surface-water availability (due to geology and distribution of rivers); (2) forage quality; (3) elephant densities; (4) position of the respective elephant populations on their population growth trajectories; and (5) management objectives/end points.

Due to these differences one would not expect similar responses from elephants to water provision in these systems.

Many of the issues listed above will be referred to in more detail throughout this communication.


The lack of response of the Kruger elephant population to extensive artificial waterhole closure (Pienaar et al., 1997; Whyte, 2005) led us to suspect that the population was not operating close to a surface-water-driven density dependence.

Although the removal of water may have reduced the ultimate density-dependent ‘carrying capacity’, we suggest that Kruger may still be relatively far from that point.

Furthermore, the elephant density that may be achieved in the presence of only natural water may potentially still exceed acceptable densities.

‘ Acceptable densities’ in KNP are impact defined, rather than density, and are informed by ‘thresholds of potential concern’ (Biggs & Rogers, 2003).

Smit et al. ( 2007b) did not make it clear that surface-water management was considered from the perspective of avoiding exceeding specific elephant impact thresholds, and that these thresholds are associated with local densities that may potentially be lower than the density-dependent densities of the population, even in the absence of water provision.


Another concern expressed by Chamaillé-Jammes et al. ( 2007) was that the aerial census data were not collected when the elephants drink, but when they feed and rest.

Although the original paper by Smit et al. ( 2007b) acknowledged that mixed herds may utilize certain waterholes at night (p. 233), we find this rather unlikely for the large bull areas in the park.

For example, in one of the large bull areas of the northern basaltic plains, it was found that over the 12-year period of this study, bulls were recorded on 372 occasions and mixed herds only on 12 occasions (i.e. only 3% of all the recorded sightings were mixed herds) (based on snap-shot data from the dry season of 12 years).

In this specific area, where on average only one mixed herd was observed per yearly census over 12 years, in excess of 30 artificial waterholes were provided.

In our opinion it is unlikely that mixed herds will move in large numbers (even across rivers) into this area at night, if it is devoid of any mixed herds by day.

It is also questionable whether some of the boreholes with a windmill will be able to replenish water into the trough fast enough to provide for the needs of large mixed herds.

Therefore, although mixed herds will sometimes drink at artificial waterholes, also by night, we still consider, as proposed in Smit et al. ( 2007b), that the closure of waterholes in bull areas will potentially have a more pronounced effect on the bull population than on the mixed herd population.


Chamaillé-Jammes et al. ( 2007) stated that the average distance that mixed herds occurred from the closest artificial waterhole was not very different in absolute terms from the average distance to the closest main river.

Smit et al. ( 2007b) (p. 233) mentioned that the short distances between water sources would not pose difficulty for elephants to cross, and hence that the full expression of how elephants relate to surface-water would not be expressed in the Kruger context.

This further supports the notion expressed earlier that the Kruger population has not yet experienced water limitations.

However, the results did clearly indicate that the mixed herds were more biased towards the rivers than the artificial waterholes.

By expressing the observed distance to closest artificial waterhole and observed distance to closest main river as percentages of the average expected distance to closest waterhole and river, respectively, mixed herds in fact occurred 40% closer to rivers than expected assuming a distribution random with regard to rivers, and only 2% closer to waterholes than expected under a random distribution with regard to waterholes (compared to approximately 22% and 15%, respectively, for rivers and artificial waterholes for bull groups).

This illustrates that although the absolute difference in average distance to closest artificial waterhole and major river was small, mixed herds occurred randomly with regard to artificial waterholes, whereas they occurred closer to rivers than expected under a random distribution.


The KNP is mandated to protect biodiversity in all its natural facets and fluxes (Mabunda et al., 2003).

This mandate implies managing a complex heterogeneous system that supports a wide variety of organisms, some that benefit from the presence of elephant, and some that disappear even in the presence of very low numbers of elephant.

This necessitates the creation of a heterogeneous mosaic of elephant density and impact across the park that will favour all components of biodiversity along the elephant density/impact gradient.

In Smit et al. ( 2007b), it was stated that smaller, less mobile, water-dependent species with more specific habitat requirements than elephants would possibly be more affected than elephants if waterholes were closed within their home range.

This statement is supported by Redfern et al. ( 2005), Smit et al. ( 2007a), and classic allometric scaling theory relating body size to mobility (e.g. Stokke & du Toit, 2002).

In light of the KNP objectives, we have to caution against a single-species approach when considering surface-water manipulation as elephant management tool.

We consider the main contributions of Smit et al. ( 2007b) that it clearly identified the presence of large areas in Kruger that were used exclusively by bulls, and subsequently indicated that the resources in these areas differed from the resources in the areas where the mixed herds occurred.

These bull areas are likely to be differently impacted due to the size, feeding, and behaviour differences between the mixed herds and bull groups (Stokke & du Toit, 2002).

Managers should therefore take cognizance of these large bull areas when considering the different management options.

For example, increasing and decreasing artificial waterholes in these bull areas may potentially alter the resource base for the bull population directly, though it will probably not have any direct resource effect on the mixed herds that seem to avoid these areas.


We welcome the comments by Chamaillé-Jammes et al. ( 2007) and believe that these provide useful context and alternative interpretations for the results presented in Smit et al. ( 2007b).

In this communication, we suggest that several factors will influence the response of elephant populations to the addition (or removal) of artificial water sources.

These include, inter alia, (1) natural surface-water availability and distribution, (2) placement/context of artificial water sources, (3) other resources that may also be driving/limiting the population, (4) prevailing elephant densities, (5) position of the population on their density-dependent growth trajectory, and (6) the influence of sexual segregation in space and resource use.

The implication is that even though elephants are a water-dependent species, the influence of water provision will be area and population specific and it is cautioned to transfer conclusions between populations and areas.

As a consequence, we would encourage research on and debate about the importance of artificial surface-water manipulation as management tool in as many elephant populations in Africa as possible.

However, we would like to suggest a systems- rather than single-species approach in such studies.

Further research in Kruger should, inter alia, consider the role of water provision in determining elephant densities and distribution (including home range sizes) under increased elephant densities, especially if serious droughts ensue and, coupled to this, if rivers dry up due to increased pressure on these resources from outside the boundaries of the park.

The seasonal importance of waterholes and the effect of waterholes on all aspects of biodiversity should also receive further attention.


Reactive arthritis (ReA) is classically considered a sub-type of spondyloarthritis (SpA) that is precipitated after a gastro- intestinal or genitourinary infection [1].

The usual presen- tation is monoarticular or oligoarticular arthritis involving large joints that occurs around 2-4 weeks after an infection [2].

However, the term has been used in a wider context of an immune-mediated arthritis that may occur after any infec- tion.

The primary concept is that there is no direct invasion of the joints by any pathogen but the arthritis occurs as a result of induced changes in the immune system.


The proposed definitions of ReA have under the umbrella of SpA, be it under the Amor or the European Spondyloar- thropathy Study Group (ESSG) proposed criteria for "Spon- dyloarthropathy" [3] or the currently used ASAS (ASsess- ment in Ankylosing Spondylitis working group) criteria for peripheral SpA [4].

According to these definitions, the pathognomic features of SpA are required to label a patient as having ReA. These include sacroiliitis, uveitis, dactylitis, enthesitis, and HLA-B27 or family history of SpA, psoriasis, or uveitis [4, 5].


ReA allows us a distinctive opportunity to scrutinize and learn how an infective trigger precipitates an autoim- mune phenomenon.

A majority of ReA resolves within a few weeks to a few months.

The rest assume a chronic form indistinguishable from other chronic autoimmune arthritides [6].

Thus, it also provides an opening to understand how the autoimmune process becomes self-sustaining and chronic.


ReA is a predominant problem of low-to-middle income countries where gut and urinary tract infections abound.

Though it is reported from high-income countries, the phenotype is usu- ally limited to arthralgia, tenosynovitis, dactylitis or often not- so-severe arthritis.

The phenotype seen in the tropics is much different with the rapid development of secondary osteoarthri- tis or even evolution into ankylosing spondylitis [7].

However, with the COVID-19 pandemic, there are a lot of reports of post- COVID-19 ReA, re-igniting interest in this entity worldwide.


This perspective aims to explore how the concept of ReA has evolved over the last century, touching upon similar enti- ties and finally how the COVID-19 pandemic is coercing us to re-look into the definitions of this enigmatic malady.


We have adhered to recommendations for narrative review searches [8].

We searched through Scopus and LitCovid/Pub- Med databases [9].

Non-English sources have not been con- sulted.

Conference abstracts or non-peer reviewed sources were not included.

To avoid confusion, we used the MeSH keyword "reactive arthritis" that includes "post infectious arthritis" for searches through LitCovid/PubMed.

For Scopus, we used "reac- tive arthritis" OR "post infectious arthritis" in the search string.


The first descriptions of a post-infectious arthritis were made during the time of the First World war by Fiessinger and Leroy [10].

However, it was more commonly known with the eponym from a Nazi doctor who had first described a triad of urethritis, conjunctivitis, and arthritis.

However, since he was convicted of war crimes, the eponym is not encouraged [11].

Also, a similar triad had already been described almost a century ago by Sir Benjamin Brodie in five cases [12].


More than half a century after the First World War, the concept of ReA was established as a non-purulent arthri- tis that occurred after a gastrointestinal infection without the direct invasion of the bacteria into the joints [13].

This concept was first contradicted by the finding of Chlamydia elementary bodies in the synovial cells of patients with ReA [14].

The tug of war over this concept has kept on going for a few decades.

Now, it is clear that the entire live organism is not found in the joint but some antigen or genetic material, possibly carried by endosomes, may persist in the joint and lead to a sustained inflammatory reaction [15].


As the definition of ReA evolved, more and more entities were proposed for inclusion such as Lyme disease, gono- coccal arthritis, post-streptococcal reactive arthritis, and rheumatic fever [16].

While it is true that Lyme disease and gonococcal arthritis may not fulfil the classical Koch's postulates to be defined as an "infection," both have unique characteristics clinical features.

Clubbing them with ReA will neither help in the management nor further research.

Similarly, the differences between ReA and post-streptococ- cal reactive arthritis are elaborated elsewhere [17].


The most commonly used definition of ReA has been pro- vided by Braun and associates [18, 19].

This definition requires monoarthritis or oligoarthritis preceded by symptomatic diar- rhoea or urethritis.

For "definite" ReA to be diagnosed by the Braun criteria, an organism with known association with ReA needs to be demonstrated by culture or PCR.

Even while these classification criteria were formulated, there was a lack of agree- ment on various points like the relationship of HLA-B27 with ReA, the existence of ReA without arthritis, or whether it should include only spondyloarthritis presentations or any arthritis [18].

More and more organisms are being added to the list of potential precipitants of ReA [20].

Also, the definition by Braun et al. does not consider the entity of "post-vaccination ReA."


The American College of Rheumatology (ACR) or the Euro- pean Alliance of Associations for Rheumatology(EULAR) do not have separate practice guidelines pertaining to ReA as possibly the rheumatologists in Europe or the United States do not see severe cases of ReA[21-23].

The incidence is apparently declining in most high-income countries [24].

However, the rest of the world that depend on the ACR and EULAR recommendations may find this gap challenging.

For example, Latin America had the largest proportion of patients with "peripheral spondyloarthritis" [25].

ReA from India has arthritis as the predominant feature in 95% of patients [26] while a report from Finland showed only arthralgia in two and arthritis in none of 17 patients with post-Escherichia coli musculoskeletal conditions [23].

Thus, there seem to be great differ- ences in how clinicians from different parts of the world view ReA.