and therefore E
Z
t
X
N s
A
N
Φds − Z
t
X
s
σ
2
∆Φ 2
ds
−→
k →∞
0. It finally remains to deal with the predictable square function of the martingale term. By Lemma 5.1
there is an ε
N
= ε
N
t → 0 as N → ∞ so that E
|〈M
N
Φ〉
t
− Z
t
4 πσ
2
X
N s
Φ
2
ds| ≤ E
2 Z
t
X
N s
log N Φ
2
f
N
ξ
N s
− 4πσ
2
X
N s
Φ
2
ds + ε
N
. The proof that the first term goes to zero as N
→ ∞ proceeds as in the argument above for D
N ,2
Φ. In the analogue of 61 one gets log N P
{0|e
1
}
t
N
→ 2πσ
2
see 13 in place of log N P {0|e
1
∼ e
2
} → γ. It is now routine to take limits in 28 for Φ as above to obtain the martingale problem MP characterizing super-Brownian motion. The details are just as in the proof of Proposition 3.2
of [5]. We have completed the proof of Theorem 1.5.
8 Proof of Theorems 1.4, 1.2.
8.1 Outline of the proof of survival, Theorem 1.4
We proceed as in Section 9 of [7] and we establish analogues of Lemma 9.1 and Proposition 9.2 of that reference. Given these results, the proof of Theorem 4.1 of [6] then goes through to give
Theorem 1.4 just as before.
The stochastic equation for ξ
N
and the associated process ξ
N
, killed outside of an open square I
′
, used in Subsection 2.2 was convenient to define the duals but does not give the natural ordering of
the two processes. For this we use the equation in Proposition 2.1 of [6]. Let {N
x,i
, x ∈ S
N
, i = 0, 1 }
be independent Poisson point processes on R
+
× R
+
with intensity N ds × du Lebesgue measure.
N
x,i
will be used to switch the type at x from i to 1 −i. Let {F
t
, t ≥ 0} be the natural right-continuous
filtration generated by these processes and recall the flip rates in 18. Let ξ
N
be a deterministic initial condition with
|ξ
N
| ∞. By Proposition 2.1 of [6] there is a unique F
t
-adapted solution to
ξ
N t
x = ξ
N
x +
1
X
i=0
−1
1 −i
Z
t
Z
∞
1 {ξ
N s
−
x = i}1{u ≤ r
N i
→1−i
x, ξ
s −
}N
x,i
ds, du 117
for all t ≥ 0, x ∈ S
N
which is the unique Feller process associated with the rates in 18. ξ
N
is defined in the same filtration and still satisfies equation 117 for all t
≥ 0, x ∈ I
′
, but satisfies ξ
N t
x = 0 for all t ≥ 0, x ∈ S
N
\ I
′
. It follows from the monotonicity of LV α
, α
1
for α
i
≥ 12 see Section 1 of [6] and 12 that we may apply Proposition 2.1bi of [6] to conclude that
if ξ
N
≤ ξ
N
then ξ
N t
≤ ξ
N t
∀t ≥ 0 almost surely. 118
1239
Let K 2, L 3, I = [−L, L]
2
and I
′
= −K L, K
L
2
. Assume {ξ
N t
x, x ∈ S
N
, t ≥ 0} and
{ξ
N t
x, x ∈ S
N
, t ≥ 0} are as above with ξ
N
x = ξ
N
x if x ∈ S
N
∩ I
′
, and ξ
N
x = 0 otherwise. Therefore by the above
ξ
N t
≤ ξ
N t
for all t ≥ 0 a.s. We still write P
α
for the joint distribution of ξ
N
, ξ
N
. We let B
x,N
be a rate-v
N
continuous time random walk with step distribution p
N
, started at x. If T
′ x
= inf{t ≥ 0 : B
x,N t
∈ I
′
} then define B
x,N t
= B
x,N t
if t T
′ x
∆ if t
≥ T
′ x
. The associated semigroup is denoted
¦ P
N t
, t ≥ 0
© . We will often drop the dependence in N from
these notations. We define the measure-valued process X
N t
∈ M
F
R
2
as before, and X
N t
≤ X
N t
by X
N t
= log N
N X
x ∈S
N
ξ
N t
xδ
x
. Although we are now using a different stochastic equation to couple
ξ
N
and ξ
N
than those in Section 2.2, we may still use the distributional results derived in Section 2.2 for each separately,
such as 25 and 27, since the individual laws of ξ
N
and ξ
N
remain unchanged. Here is the version of Lemma 9.1 of [7] we will need.
Lemma 8.1. Let T 0. There is a C
8.1
, depending on T, β, and a universal constant δ
8.1
, such that if X
N
= X
N
is supported on I , then for N 1, K
2, L 3, and 0 ≤ t ≤ T , E
X
N t
1−X
N t
1
≤ C
8.1
X
N
1
Psup
s ≤t
|B
s
|K −1L−3 + 1∨X
N
1log N
−δ
8.1
.
For α
∈ [
1 2
, 1, a bit of calculus shows there exists a unique N = N α
e
3
so that 1
− α =
log N
3
N .
119 Let I
−1
= −2L, 0e
1
+ I, I
1
= 0, 2Le
1
+ I. Here is the analogue of Proposition 9.2 of [7] we will need. It will imply that the Lotka-Volterra process dominates a 2K
-dependent supercritical oriented percolation and therefore, survives. The role of the additional killing for X
N
is to ensure the finite range of dependence need for the associated oriented percolation. Recall that K is as in 6.
Proposition 8.2. Assume 0 η K. There are L 3, K
2, J ≥ 1 all in N, T ≥ 1, and r ∈ 0, e
−4
, all depending on
η such that if α
≤ α
1
≤ α +
K − η
γ 1
− α log
1 1
−α 2
, 1 − α
r and N = N α ,
then X
N
1 = X
N
I ≥ J implies P
α
X
N T
I
1
∧ X
N T
I
−1
≥ J
≥ 1 − 6
−42K +1
2
. 1240
Remark: The parameters β
N
, β
N 1
are now defined by N , α
, α
1
and 11. Indeed our choice of N = N
α in the above implies β
N
= 0 and β
N 1
= N
log N α
1
− α ∈
0,
K − η
γ log N
2
log N − 3loglog N
2
.
Therefore, for r η small enough we will have
β
N 1
∈ 0,
K − η2
γ .
Proof : Given Lemma 8.1, Proposition 8.2 is proved by making trivial changes to the proof of Proposition 9.2 in [7]. We omit the details but the intuition should be clear. By Theorem 1.5, X
N
converges to a super-Brownian motion with drift θ = K − γβ
1
. By the above remark this quantity is bounded below by
η2 0 for a good choice of r. Super-Brownian motion with positive drift and large enough initial mass will continue to grow exponentially up to time T with high probability,
and so the same should be true for X
N
for N large. Finally, Lemma 8.1 bounds X
N T
− X
N T
and allows us to make the same conclusion for X
N T
. To complete the proof of survival it remains to establish Lemma 8.1.
8.2 Proof of Lemma 8.1.
